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Review and evaluation of remote sensing methods for soil-moisture estimation
Abstract
Soil-moisture information plays an important role in disaster predictions, environ- mental monitoring, and hydrological applications. A large number of research papers have introduced a variety of methods to retrieve soil-moisture information from different types of remote sensing data, such as optical data or radar data. We evaluate the most robust methods for retrieving soil-moisture information of bare soil and vegetation-covered soil. We begin with an introduction to the importance and challenges of soil-moisture information extraction and the development of soil-moisture retrieval methods. An overview of soil-moisture retrieval methods using different remote sensing data is presented—either active or passive or a combination of both active and passive remote sensing data. The results of the methods are compared, and the advantages and limitations of each method are summarized. The comparison shows that using a statistical method gives the best results among others in the group: a combination of both active and passive sensing methods, reaching a 1.83% gravimetric soil moisture (%GSM) root-mean-square error (RMSE) and a 96% correlation between the estimated and field soil measurements. In the group of active remote sensing methods, the best method is a backscatter empirical model, which gives a 2.32-1.81%GSM RMSE and a 95-97% correlation between the estimated and the field soil measurements. Finally, among the group of passive remote sensing methods, a neural networks method gives the most desirable results: a 0.0937%GSM RMSE and a 100% correlation between the estimated and field soil measurements. Overall, the newly developed neural networks method with passive remote sensing data achieves the best results among all the methods reviewed. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).
... Many studies strive to overcome such limitations by estimating soil moisture from synthetic aperture radar (SAR) data, exploiting SAR's ability to map large areas with high temporal and spatial resolution [7], [8]. ...
... Empirical and semi-empirical models based on SAR images are commonly used for soil moisture estimation in bare soil scenarios [7]. Empirical models based on a single polarization and angle of incidence are more effective with short Jhonnatan Yepes and Bárbara Teruel are with the School of Agricultural Engineering, University of Campinas-UNICAMP, Campinas, 13083-875, wavelengths and low incidence angles to minimize the effects of ground roughness on radar reflectivity during data acquisition [9]. ...
... ( , ) = 10 ( tan + ) , (5) ( , ) = 10 ( tan + ) , (6) ( ) = cos( ) 1.5 sin( ) 5 ⁄ , (7) ( ) = cos( ) 3 sin( ) 3 ⁄ . (8) The variables , , , , V , V , V , and V are fitting constants. ...
The paper introduces a novel approach for estimating soil moisture in vegetated surfaces, specifically focusing on sugarcane crops throughout various growth stages in agriculture applications. While existing models typically address bare soil scenarios, this model utilizes data from P-, L-, and C-band Synthetic Aperture Radar (SAR) to estimate soil moisture. The semi-empirical Dubois model forms the basis of the proposed model, which has been adapted to accommodate multiband operation and crop height variations. Synthetic datasets are generated using the adjusted model to train two neural networks incorporated into the overall model. Additionally, a linear expression for estimating crop height is integrated into the model. The model is validated in an Experimental Site at the School of Agricultural Engineering, UNICAMP, and an independent area at the Sugarcane Technology Center in Piracicaba, Brazil. The model utilizes a multiband drone-borne SAR system with a 3-meter image resolution and radiometric accuracy of 0.5 dB. The results indicate that the model can estimate soil moisture with root-mean-square errors of 0.05 cm3.cm-3 (5 vol. %) across crop heights ranging from zero to 2.5 meters.
... For passive optical methods, the availability of finer resolution remains an advantage [9], while the penetration capability of active sensors is an advantage that can be used to interrogate soil moisture at the subsurface level. As part of an overview of established methods in both fields, there are some detailed reviews of the evaluation of remote sensing methods [6,10,11], a current state of use in the microwave domain [12], and some significant recent advances [13] carried out by researchers at recent years. One of the earlier methods is the extraction of soil moisture from the uppermost layer [14]. ...
... Other applications focused on establishing a link between soil parameters, vegetation indices and surface radiation temperature, which were studied and progressively developed [15][16][17]. Some of the models and approaches in this area include the universal triangular relationship method, brightness models, statistical analysis techniques, and the application of neural networks [11]. In passive environments, thermal sensors, including thermal infrared (TIR) sensors, can monitor soil surface temperature and reveal details about subsurface soil moisture content. ...
... Finally, the volumetric soil moisture was derived using Equation (11) proposed in [32,37]. ...
Understanding physical processes in nature, including the occurrence of slow-onset natural disasters such as droughts and landslides, requires knowledge of the change in soil moisture between two points in time. The study was conducted on a relatively bare soil, and the change in soil moisture was examined with an index called Normalized radar Backscatter soil Moisture Index (NBMI) using Sentinel-1 satellite data. Along with soil moisture measured with a probe on the ground, a study of correlation with satellite imagery was conducted using a Multiple Linear Regression (MLR) model. Furthermore, the Dubois model was used to predict soil moisture. Results have shown that NBMI on a logarithmic scale provides a good representation of soil moisture change with R²~86%. The MLR model showed a positive correlation of soil moisture with the co-polarized backscatter coefficient, but an opposite correlation with the surface roughness and angle of incidence. The results of the Dubois model showed poor correlation of 44.37% and higher RMSE error of 17.1, demonstrating the need for detailed and accurate measurement of surface roughness as a prerequisite for simulating the model. Of the three approaches, index-based measurement has been shown to be the most rapid for understanding soil moisture change and has the potential to be used for understanding some mechanisms of natural disasters under similar soil conditions.
... Although the scope to exploit RS is increasing as more datasets and processing techniques are becoming available (Peng et al. 2017), the extent to which it has been used to provide SMI has not been adequately investigated. This limitation is demonstrated by emphasis on studies that do not address operational farm-level requirements (Ahmed et al. 2011;Srivastava 2017). ...
... Soil moisture is spatially and temporally variable because of localized differences in topography, precipitation, humidity, temperature and vegetation cover (Suepa et al. 2016). Topography determines SM by interacting with energy fluxes from the sun through reflection and scattering (Ahmed et al. 2011). Precipitation and humidity regulate SM by controlling the amount of soil water available for plant growth (Wang et al. 2019). ...
... Dark soils reflect less radiation than bright soils and the same applies for rough areas that exhibit more scattering compared to smooth-surfaced soils. Likewise, vegetation has similar influences, as height variations and uneven plant distributions cause differences in surface roughness (Ahmed et al. 2011). Where the land comprises a mix of bare soil and vegetation, spectral mixing can pose problems by corrupting signatures before they reach the sensor (Zhang et al. 2018). ...
Remotely sensed soil moisture is crucial in enhancing our understanding of how climate change influences food production. Conventionally, the acquisition of soil moisture data has always been based on in-situ measurements, which are costly, labour-intensive, spatially restricted and time-consuming to acquire. These limitations justify why most resource-constrained developing countries have been paying increasing attention to remote sensing. Although remote sensing has established potentials to address these challenges, progress in the application of this technology to crop production in Africa has not been properly documented. This chapter attempts to bridge this gap by providing a comprehensive review of the progress that has been accomplished to date and the gaps that need to be filled in and, successes and opportunities that have to strengthened and exploited.
... However, in situ sensors are difficult to scale spatially and are expensive to install and maintain. Remote sensing-based methods scale globally and provide modestly accurate estimates of top-soil moisture (typically 0-5 cm) (Ahmed et al. 2011), while lowering deployment and maintenance costs relative to ground-based methods. While remote sensing soil moisture estimates generally have lower accuracy than in situ measurements, they scale well spatially. ...
... As mentioned in the introduction there are a number of types of remote sensing sources that can be useful for soil moisture estimation (Ahmed et al. 2011). We select sources that have a significant correlation with soil moisture and/or have potential to help with the disaggregation of low-resolution soil moisture estimates. ...
We develop a deep learning–based convolutional-regression model that estimates the volumetric soil moisture content in the top ∼5 cm of soil. Input predictors include Sentinel-1 (active radar) and Sentinel-2 (multispectral imagery), as well as geophysical variables from SoilGrids and modeled soil moisture fields from SMAP and GLDAS. The model was trained and evaluated on data from ∼1000 in situ sensors globally over the period 2015–21 and obtained an average per-sensor correlation of 0.707 and ubRMSE of 0.055 m ³ m ⁻³ , and it can be used to produce a soil moisture map at a nominal 320-m resolution. These results are benchmarked against 14 other soil moisture evaluation research works at different locations, and an ablation study was used to identify important predictors.
Significance Statement
Soil moisture is a key variable in various agriculture and water management systems. Accurate and high-resolution estimates of soil moisture have multiple downstream benefits such as reduced water wastage by better understanding and managing the consumption of water, utilizing smarter irrigation methods and effective canal water management. We develop a deep learning–based model that estimates the volumetric soil moisture content in the top ∼5 cm of soil at a nominal 320-m resolution. Our results demonstrate that machine learning is a useful tool for fusing different modalities with ease, while producing high-resolution models that are not location specific. Future work could explore the possibility of using temporal input sources to further improve model performance.
... Proximal soil sensing technologies allow for real-time and non-destructive assessment of soil characteristics such as fertility level, moisture level, and soil electrical conductivity to provide an immediate feedback to farmers and growers (Nichols 2011). These sensors can reduce laboratory-based soil analytical costs and save time while minimizing the risk of soil sample deterioration and contamination during transit. ...
The increasing frequency of extreme weather events and rising temperatures have significantly impacted global crop production. Precision agriculture technologies such as remote sensing, proximal sensing, variable rate applications, and autonomy in farming provide solutions to mitigate these uncertainties. Precision tools can optimize the use of seeding rate, fertilizers, pesticides, and irrigation, improving efficiency and resilience. These tools can provide real-time, continuous data on soil conditions, crop health, and resource use efficiency. Detecting nutrient deficiencies, water stress, and disease infestation enables a farmer for targeted interventions at right time, right spot, right resources, right amount simply by using right data from the sensors. Recent developments in autonomous operations have modernized the fields of farm machinery and mechatronics for efficient functionality and optimized use of energy to manage operations. These farm data layers derived from these technologies can be normalized to efficient management zones for an optimized decision-making during extreme weather, soil, or crop variabilities.
... Soil moisture data is extensively utilized for hydrological modelling, early warnings of dry seasons, yield predictions, and irrigation development. Numerous remote sensing approaches utilize various data types, such as visual, infrared radiation thermal, and microwave data, to obtain moisture level information (Nichols, 2011b). ...
Soil moisture is a crucial element in the hydrological cycle that regulates weather conditions, crop growth, and groundwater retention. However, estimating soil moisture usually consists of point-based observations at a certain location and time, making it challenging to capture spatial and temporal soil moisture dynamics. In this study, we utilized Landsat 8 multispectral images to estimate soil moisture conditions. The integration of data from remote sensing (RS) as secondary sources has been effective in the enhancement of computer-based landscape modelling on all dimensions in recent decades. The analysis demonstrated that Landsat 8 OLI images possessed the potential to detect soil moisture in Rajshahi District. The soil moisture calculation empowered the identification of the district boundary. Soil moisture values are between -1 and +1. Four categories were identified: very dry, dry, wet, and very wet. The soil moisture of the wet area has been identified as the largest in 2023 (28.36%), followed by 2020 (22.37%), and then 2017 (21.37%). On the other hand, soil moisture maps show that the dry area was the lowest in 2023 (11.01%), followed by 2020 (17.90%), and then 2017 (12.77%). The availability of soil moisture data is of profound significance in the fields of hydrological applications, paddy rice field forecasting, environmental monitoring, and disaster prediction.
... Sensors measuring physical, chemical, and mechanical properties of soil, using optical, radiometric, mechanical, acoustic, electrical, electromagnetic, pneumatic, or electrochemical methods, offer comprehensive data for irrigation decisions [25]. Recent advancements include combining remote sensing with ground sensors to address soil heterogeneity issues [26]. ...
Optimizing irrigation water usage is crucial for sustainable agriculture, especially in the context of increasing water scarcity and climate variability. Accurate estimation of evapotranspiration (ET), a key component in determining water requirements for crops, is essential for effective irrigation management. Traditional methods of measuring and estimating ET, such as eddy-covariance systems and lysimeters, provide valuable data but often face limitations in scalability, cost, and complexity. Recent advancements in machine learning (ML) offer promising alternatives to enhance the precision and efficiency of ET estimation and smart irrigation systems. This review explores the integration of machine learning techniques in optimizing irrigation water usage, with a particular focus on ET prediction and smart irrigation technologies. We examine various ML models, that have been employed to predict ET using diverse datasets comprising meteorological, soil, and remote sensing data. In addition to ET estimation, the review highlights smart irrigation systems that optimize irrigation schedules based on real-time data inputs. Through this review, we aim to provide a comprehensive overview of the state-of-the-art in ML-based ET estimation and smart irrigation technologies, contributing to the development of more resilient and efficient agricultural water management strategies.
... An alternative approach is the use of conceptual or physical numerical models, which are powerful tools for estimating daily SM in the plant root-zone (Ma et al., 2011;Martínez-Ferri et al., 2013;Wang et al., 2018;Ursulino et al., 2019;Wang et al., 2020). Because soil optical reflectance behavior is very dependent on SM, several remote sensing (RS)-based methods that can accompany ground-based data and simulation models have been developed (Wang and Qu, 2009;Ahmed et al., 2011;Zhang and Zhou, 2016) to retrieve SM remotely. Microwave RS, both active and passive, has shown the most potential for retrieving SM dynamics. ...
Efficient use of water and irrigation management are essential to sustain irrigated agriculture in drylands, where water resources are limited. Because of the high cost and difficulties of operation and maintenance of in situ instrumentation over irrigated fields, fine-scale monitoring of soil moisture (SM) based on remote sensing and a simulation model may be a practical way to inform irrigation practices. We herein propose a method for integration of low-cost, available, multi-source data, including field data (crop, soil, and weather) and high-resolution satellite data (Sentinel-2 and Landsat-8) into a soil-water-atmosphere-plant (SWAP) model to provide daily, accurate surface-and root-zone SM at the field scale that can inform optimal management of irrigation water. Specifically, effective soil parameters and crop growth in the SWAP model were parameterized and updated using satellite-based surface SM and leaf area index data obtained using inverse modeling and assimilation techniques. We applied and evaluated the developed method for the surface-and root-zone SM estimates using the measured SM over 13 marked locations in six study fields in Iran with two crop types, wheat and maize. The proposed method showed promising results at all marked locations, study fields, study crops, crop growth stages, and monitored soil depths and layers. The root mean square errors (RMSEs) and coefficients of determination (R 2 values) were < 0.032 cm 3 cm − 3 and 0.52-0.95, respectively. The results showed that the type of irrigation system had a direct effect on the SM estimated with the proposed method. The proposed method could improve the spatiotemporal resolution of surface and root-zone SM monitoring via simulation of daily root-zone SM at a spatial resolution of 10 m. This method may enable the development of precise irrigation systems that optimize water allocations and conserve limited water resources at the field scale.
... Our findings, obtained between VV, VV-VH bands, and TN, are based on study to predict annual soil water content in vegetated lands with the help of SAR images and machine learning techniques; soil moisture satellite data are promising to increase the reliability of flood measures (Ahlmer et al., 2018). The Sentinel-1 VV band showed a better relationship than the VH band in estimating soil moisture with the help of Sentinel-1 microwave and Landsat 7/8 thermal data (Amazirh et al., 2019); evaluating the use of active and passive data separately and together in obtaining soil moisture information, (Amer et al., 2011);(Bauer-Marschallinger et al., 2018), reporting that Sentinel-1 data are highly productive in plains, forests, and agricultural areas. Development a remote sensing product combining active and passive microwave sensors to determine soil moisture deficits (Hirschi et al., 2014). ...
In the study carried out in 8 villages and 27 cotton parcels in the Artuklu and Kızıltepe Districts of Mardin Province, data logger devices were installed on the lands. These devices are programmed to record soil temperature and humidity values every 6 hours. The data collected from the data loggers were compared with the Landsat-8 and Sentinel-1 images used by pre-processing in the Google Earth Engine (GEE) cloud environment, and the relationship between them was investigated by analyzing them. A significant and high correlation was found between soil moisture (TN) and Sentinel-1 values, VV (R2 = 0.67), VV-VH (R2 =0.65), and Landsat-8 SMI (R2 = 0.85) values. A significant and high correlation was found between soil temperature (TS) and the Sentinel-1 values of VV (R2 = 0.57), VV-VH (R2 =0.54), and Landsat-8 SMI (R2 = 0.75). In conclusion, it is recommended that the Sentinel-1 VV and VV-VH bands and the Landsat-8 SMI index could be used in soil moisture (TN) and soil temperature (TS) estimation studies, while the Landsat-8 LST band is recommended to be used in larger-scale land areas and regions
... Research on microwave SSM data products has demonstrated their capability to provide valuable information for various applications, but land surface properties such as vegetation and surface roughness can affect their accuracy and quality Njoku and Chan 2006). Evaluation methods for SSM data products often include comparison with in-situ measurements and inter-comparison with other satellite-derived datasets to assess consistency and reliability (Li, Tang, and Hong 2018;Nichols 2011). To address disparities in spatial data, robust techniques are needed, utilising in-situ measurements for calibration and validation . ...
Global-scale surface soil moisture (SSM) products (e.g. SMAP L3.0, ASCAT V3.0, ESA/CCI V7.1 and GLDAS V2.2) are vital for applications in hydrology, climate variability, and agriculture. This study uses a new SSM evaluation approach by combining temporal evolution, Coefficient of Variation (CV), Cumulative Distribution Function (CDF), evaluation metrics, and Triple Collocation Analysis (TCA) to assess SSM accuracy and spatial–temporal variability, particularly the impact of footprint mismatch when comparing retrieved SSM with in-situ measurements. Results revealed significant spatial variability and seasonal patterns in SSM, as indicated by the CV values and temporal evaluations at different resampling scales. The variability captured by in-situ measurements was comparable to that of SSM products. The impact of footprint mismatch between in-situ measurements and data products, particularly for SMAP and ASCAT SSM, was more significant and led to substantial differences in evaluation metrics between smaller and larger spatial scales. TCA alone cannot reliably assess the accuracy of global-scale SSM products without in-situ SSM measurements. Overall, our findings highlight the critical role of footprint mismatch on the estimated accuracy of SSM products and underscore the need to combine multiple evaluations into an overall scoring indicator, as proposed in this study.
... Globally, these methods can be divided into two large groups: laboratory (direct) -soil moisture is estimated as the difference between the mass of wet and dry soil; indirect (field, remote) -soil moisture is estimated on the basis of data on changes in any physical or chemical parameter associated with it [13][14][15][16][17]. A graphical representation of the results of the analysis and systematisation of the known methods of non-destructive soil moisture monitoring is shown in Fig. 6. ...
The global intensification of the use of land resources for agricultural purposes and the simultaneous negative dynamics of cultivated areas in Ukraine necessitate the substantiation of effective approaches and technologies for controlling critical parameters and the general condition of land resources when cultivating crops in open-field conditions. Soil moisture is one of the crucial factors in the justification and implementation of measures to increase soil productivity and improve crop stress resistance. Effective control of soil moisture prevents soil erosion and improves the regularity of river flow, which has a significant environmental and economic effect in the course of open-field crop production. The aim of the research is to substantiate the requirements for the creation of computer technologies for the predictive control of soil moistening modes in order to improve the efficiency of agrotechnical measures for the cultivation of grain crops through a comprehensive information analysis and systematisation of modern applied information and computer technologies. The object of the research is information processes of detection, network exchange and predictive processing of a set of distributed data on the modes of moistening of grain crops. The subject of the research is models, methods and hardware and software means of creating computer technologies for the predictive control of grain crop moistening modes. General trends in agricultural activities for growing grain crops at the national and global level have been analysed; functional and technological features, as well as models, methods and means of computerised intelligent control of soil moistening modes in the process of growing grain crops, have been analysed in detail; world experience in the creation and utilisation of software and hardware for the computerised intelligent control systems of soil moisture has been analysed and systematised; the necessity of further research on the development of effective approaches to optimising the modes of growing of grain crops through the creation and implementation of computer technologies for the predictive control of the state of agricultural objects of the open-field crop production has been proved.
... Passive microwaves are preferred for estimating SM (Chew & Small, 2018;Entekhabi et al., 2010;Peng & Loew, 2017). Passive microwave sensors including Scanning Multichannel Microwave Radiometer, Special Sensor Microwave/Imager sensors, Advanced Microwave Scanning Radiometer Earth Observing System and Soil Moisture Ocean Salinity Satellite/Soil Moisture Active Passive mission have been used successfully to measure SM (Ahmad et al., 2011;Jones et al., 2010;Kerr et al., 2001;Njoku & Chan, 2006;Paloscia et al., 2001;Wen et al., 2005;Wigneron et al., 2021). However, spatial and temporal resolution is significantly lower than the infrared range and is thus considered a significant limiting factor for SM measurements. ...
... RS has largely bloomed to be used in soil science including soil properties monitoring from early of this century [121][122][123][124], soil properties and resources mapping and survey [125][126][127][128], soil salinity monitoring and salinization management [129,130], soil organic carbon estimation [131], soil characteristics prediction [132]. RS is particularly appropriate for soil moisture monitoring and estimation as early as 50 years ago worldwide [133][134][135][136][137][138][139]. Here are several cases, especially from the last decade as a clearer interpretation. ...
... Soil moisture can be obtained from optical and microwave remote sensing [9]. Optical remote sensing is mainly sensitive to the topsoil surface as it relies on the reflection of light from the earth's surface [10]. Microwave remote sensing provides a unique capability and has shown great potential for soil moisture estimation as it removes most of the constraints of optical remote sensing over retrieving soil moisture [11]. ...
Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R² of 0.89 and RMSE of 0.050 without considering vegetation effects and an R² of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R² of 0.89 and RMSE of 6.79 without considering vegetation effects and an R² of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.
... Although the global, long-term, frequent, and high-resolution characterization of all surface water types is beyond the capabilities of current satellite observations, several types of datasets are being used to document them as well as possible (Aires 2020). Indeed, this information is widely used in hydrological applications, helping to quantify the diverse components of water budgets, including infiltration, surface runoff, wetland water storage, deep percolation, changes in soil moisture (Zhan et al. 2009, Parida et al. 2013, Ahmed et al. 2015, Welikhe et al. 2017, Jalilvand et al. 2019, Yue et al. 2019, evapotranspiration (Liou and Kar 2014, Sharma and Tare 2018, Pasqualotto et al. 2019, Senay et al. 2020, Weerasinghe et al. 2020, Wu et al. 2020) and precipitation (Milewski et al. 2009, Stocker et al. 2015, Berges 2019, Bouhali et al. 2020, which can be transmuted cell by cell into a deterministic distributed estimation of variables (Mohebzadeh and Fallah 2018). ...
Understanding the hydrological processes associated with wetland dynamics is fundamental to studying climate change impacts and the global water cycle. This study simulates the variation of water balance in the Ghorra Playa, Eastern Tunisia, over the period extending from September 2017 to August 2020. The playa is a seasonal habitat for a number of migratory bird species and its watershed is of vital agricultural importance. Water balance estimations were performed using two different approaches: remote sensing-based analyses (Sentinel-2B, GPM and FEWS-NET) and modelling founded on field data. The playa presents an average annual water balance of 1.1 million cubic meters. Water inflows come from direct rainfall mostly in the fall and spring seasons. Groundwater flow into the playa significantly influenced the pattern of water flux (81 %). The annual, seasonal and monthly water budget simulations show reasonably good agreement between the remote sensing-based analysis and the hydrological modelling exercise.
... Historically, engaging in ground-based soil moisture (SM) measurement has been considered to yield the most accurate results [1]. However, ground-based SM measurements are often limited by cost, time, associated destructive mechanisms, and the inability to provide information for a large area [2]. ...
Remotely sensed data acquired by multispectral sensors can be used to monitor soil moisture (SM) across a larger land area than in situ monitoring alone. Although there have been wide-ranging applications of remote sensing tools in SM estimation on many ecosystems, there is a limited understanding of their accuracy in restored wetlands. The objective of this study was to examine the potential of remotely sensed data from Landsat-9, Sentinel-1A SAR, and Blackswift E2 Uncrewed Aircraft Systems (UAS) in predicting SM in a restored wetland complex in the Gunnison Basin of Colorado. We tested two response variables, gravimetric SM and volumetric SM, to determine which indicator of SM was better predicted with remotely sensed data. We also tested the accuracy of remotely sensed data in predicting SM at different soil depths. Overall, satellite and UAS indices predicted gravimetric SM better than volumetric SM. The Normalized Difference Red Edge (NDRE) index from Blackswift E2 UAS had the best prediction of GSM at the depth of 0 to 5 cm (R2 = 0.86, RMSE = 7.41). Results from the study provide information on the accuracy of remotely sensed data for SM monitoring on restored wetlands.
... However, Soil Moisture and Ocean Salinity (SMOS) (Mecklenburg et al., 2012) and Soil Moisture Active Passive (SMAP) (Jun et al., 2016) are the only RS missions developed to observe SMC using the L-band microwave radiometers. These RS missions can provide global SMC maps with typically 2-3 days of revisit time in all weather conditions, with an accuracy better than 0.04 cm 3 /cm 3 , under vegetation cover with less than 5 kg/m 2 water content (Pradhan et al., 2018). ...
The change in Soil Moisture Content (SMC) is one of the most crucial variables for regulating and analyzing basic
hydrological processes, including runoff, evaporation, carbon and energy cycles, infiltration of water resources,
droughts and floods, and desertification. This study aimed to detect and map the global SMC change using
microwave remote sensing observations. Monthly SMC data from the Soil Moisture Ocean Salinity (SMOS) with a
spatial resolution of 25 km were used to assess the SMC change from January 2010 to December 2021. Various
trend patterns, including linear, quadratic, cubic, and concealed, were examined by applying a parametric
polynomial fitting-based algorithm (Polytrend). In particular, approximately 16.93% of global land is subjected
to soil moisture dynamics, of which 8.33% has become drier and 8.60% has become wetter. Both linear and
nonlinear trends were observed in the global land areas that have experienced statistically significant changes.
The concealed and linear trends were however the dominant trend patterns globally. The obtained trend results
were further investigated using a well-known non-parametric trend test, Mann-Kendall, which showed 93.20%
agreement, demonstrating the robustness and reliability of the observed trends.
... Hence, soil moisture estimation helps in many natural resource applications such as hydrological modeling, stream flow and flood and drought mapping and monitoring. Soil moisture in the upper part of the earth's surface is recognized as a key variable in numerous environmental studies including meteorology, hydrology, agriculture and climate change [3,4,5,6,7,8,9]. Therefore, it is imperative to monitor and estimate accuracy of the spatial and temporal variations of soil moisture.Development in remote sensing satellite technology has offered a number of techniques for determining soil moisture across a wide area continuously over time [10]. ...
Soil moisture estimation is crucial for effective water and soil resource management, impacting global water and energy balance. Deviations in moisture levels indicate potential flooding or drought. In-situ measurements are costly and time-consuming, making remote sensing an ideal option for large-scale assessments. This study used Landsat-8 thermal infrared data and ArcGIS for estimating soil moisture in Oredo Local Government Area, facilitating town planning and flood management strategies
... Board storing, the early admonition of dry season, harvest yield anticipating, and water system planning are commonly used soil moisture data. Various remote sensing techniques employ different types of data, including visible, infrared, thermal, and microwave data, to acquire soil moisture information [4]. Bangladesh has ample water supply with a fertimprovesl which improve the different crops grow in easy ways. ...
Accurate monitoring and mapping of soil moisture are essential for sustainable agricultural practices, water resource management, and climate studies. This study aims to explore the mapping of soil moisture in Bangladesh using multispectral remote-sensing satellite images. The purpose of this study is to prepare a map of soil moisture aiming to help government authorities in developing agricultural activities to accelerate the sustainable development of the rural economy in Bangladesh. A total of 14 Landsat scenes of paths 135-139 and rows 42-46 covers the entire Bangladesh. Thus, a set of Landsat imagery (a total of 14 scenes) for the year 2022 was used in this study to map the soil moisture of Bangladesh through the application of Geographical Information System (GIS) and Remote Sensing. Satellite Image preprocessing, correction, and analysis were done with ENVI software (version 5.1, developed by Research Systems, Inc., USA) and the ArcGIS software (version 10.6, developed by Environmental Systems Research Institute, USA). For the study of the long-term variation of soil moisture over Bangladesh and its seasonal characteristics, a soil moisture map can be used. In addition, to improve the climate model over Bangladesh, an up-to-date soil moisture map will be very helpful. The objective of this study is to provide accurate and detailed up-to-date spatial soil moisture information at reduced cost and time which is essential for environment modeling, risk assessment, decision-making for different government agencies and development partners, and help toward socio-economic development. In this study, the map shows soil moisture as very wet, wet, dry, and very dry soils of Bangladesh. The overall land cover classification accuracy was 92.56%, with a Kappa value 0.90 for Random Forest and the overall soil classification accuracy was 87.27%, with a Kappa value 0.858 for maximum likelihood classification indicating good consistency.
... Furthermore, active microwave backscattering signals of the land surface are also sensitive to SM (Mirmazloumi et al., 2021;Zribi et al., 2020). Both mentioned electromagnetic spectrum domains enable sensors to operate at higher spatial resolution than passive microwave, but SM retrieval is challenging without auxiliary data and more complex mathematical algorithms, considering, e.g., the complex spatio-temporal dynamics of vegetation cover (Pradhan et al., 2018). The use of these intermediate and highresolution observations in combination with SM PMW allows estimation of SM with higher spatial resolution (Petropoulos et al., 2015). ...
... In a climate-smart agriculture (FAO, 2022) perspective, monitoring SSM in agricultural fields at high temporal and spatial resolution is essential to safeguard soil and water resources, developing sustainable cropping systems, and thus positively determine the adaptability to new climate scenarios (Lewis, 2019). SSM estimate can find applicability in irrigation scheduling, to facilitate a rational use of water, reduce plant stress and improving crop yield (Pradhan et al., 2018). It could also encourage diversification of production orientations at the expense, in areas where it is environmentally sustainable, of low-income crops (Zucaro et al., 2009). ...
Surface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model’s accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4).
... Precisely monitoring and estimating the spatial and temporal changes in soil moisture is of great significance [71]. Estimating soil moisture becomes more challenging in regions with dense vegetation or snow cover, as well as areas with intricate topography [72]. Soil moisture content in correlation with the number of erosive days could predict some mitigation effect for some areas [39]. ...
Remote sensing (RS) has revolutionized field data collection processes and provided timely and spatially consistent acquisition of data on the terrestrial landscape properties. This research paper investigates the relationship between Wind Erosion (WE) and Remote Sensing (RS) techniques. By examining, analyzing, and reviewing recent studies utilizing RS, we underscore the importance of wind erosion research by exploring indicators that influence the detection, evaluation, and modeling of wind erosion. Furthermore, it identifies research gaps particularly in soil erodibility estimation, soil moisture monitoring, and surface roughness assessment using RS. Overall, this research enhances our understanding of WE and RS and offers insights into future research directions. To conduct this study, we employed a two-fold approach. First, we utilized a non-systematic review approach by accessing the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database. Subsequently, we conducted a systematic review of the relevant literature on wind erosion and remote sensing in the core collection of the Web of Science (WoS) database. Additionally, we employed the VOSviewer bibliometric software to generate a cooperative keyword network analysis, facilitating the advancements and identifying emerging areas of WE and RS research. With a non-systematic review, we focused on examining the current state and potential of remote sensing for mapping and analyzing following indicators of wind erosion modelling: (1) soil erodibility; (2) soil moisture; (3) surface roughness; (4) vegetation cover; (5) wind barriers; and (6) wind erosion mapping. Our study highlights the widespread utilization of freely available RS data, such as MODIS and Landsat, for WE modeling. However, we also acknowledge the limitations of high resolution sensors due to their high costs. RS techniques offer an efficient and cost-effective approach for mapping erosion at various scales and call for a more comprehensive and detailed assessment of soil erosion at regional scales. These findings provide valuable guidance for future research endeavors in this domain.
... Second, the majority of the remote-based sensing techniques are affected by clouds, weather conditions, vegetation, surface texture, and other environmental conditions [8]. In addition, research studies showed that most of the methodologies and algorithms used for estimating soil moisture and based on datasets derived from remote-based sensing techniques, were not successful in providing accurate moisture information from deep soil layers (root-zone soil layers) [9]. ...
Measuring the soil moisture is an essential task in precision agriculture and smart irrigation strategies. However, most of the available measurement instruments are expensive and are not suitable for use by traditional farmers with a low level of education. In addition, several technical limitations affect the performance of the currently used soil moisture sensors, such as weather changes, dependency on calibration, soil types, aging and drift among others. This policy brief is based on the work done on implementing a new low-cost and simple-to-use instrument, for measuring the soil water content, based on a discrete capacitive sensing technique. The proposed device measures the vertical soil moisture profile and serves as an indicator for water infiltration rate. In contrast to the existing commercial capacitive sensors that measure volumetric water content, the proposed sensor is measuring the soil moisture at different depths. The presented device is capable of long-term field operation, and is characterized by: 1) low-cost and simple-to-use, 2) robust sensing technique (does not require calibration against different soil types), 3) protection against corrosion and reduced aging, and 4) low-power consumption allowing long-term field deployment.
... However, one advantage of microwaves is the semitransparency of vegetation at longer microwave wavelengths, leading to negligible scattering Schmugge 1991, Shen et al. 2022). Using surface temperature and vegetation indices can modify SSM estimation (Xiwu et al. 2006, Kaniska et al. 2009, Ahmed et al. 2011, Taghvaeian et al. 2012, Piles et al. 2014, Chen et al. 2015, Attarzadeh et al. 2018. In this context a reverse relationship was observed between surface temperature and moisture content ). ...
Nine remote sensing-based surface soil moisture (SSM) estimation models using images from Landsat 8, Sentinel-2 and Sentinel-1 satellites were compared. To evaluate these models, we measured SSM at 179 locations in a 50-ha sunflower field . The result showed that the Water Cloud-based model, a semi-empirical regression model, which used the synergy of Landsat 8 and Sentinel-1 data, was the best model, with an R² of 0.73 and RMSE of 0.053 m³/m³. In sum, with the integration of images from multiple satellites, soil moisture maps with suitable spatial resolutions were retrieved that may be used for irrigation planning.
... Four performance metrics that can demonstrate the quality of the satellite data were calculated (Ahmad et al., 2011;Entekhabi et al., 2010b;Fang et al., 2018;Gupta et al., 2009), including bias (Equation (1)), root-mean-square error (RMSE; Equation (2)), ubRMSE (Equation (3)), correlation coefficient (denoted as R; Equation (4)). The ubRMSE is used against RMSE, because the biases vary in different periods, locations, instruments and even deriving algorithms. ...
Using the 43‐station soil moisture (SM) at a depth of 0–10 cm over eastern China, the near‐surface (about 0–5 cm) SM products from satellite missions including the level‐2 product of Chinese FengYun 3C (FY3C), level‐2 neural network product of the European Soil Moisture and Ocean Salinity (SMOS) and level‐4 product of the US Soil Moisture Active Passive (SMAP) were evaluated and compared during summers of 2015–2018. Thus, features of those products can be identified for their further application of climate study over eastern China. Large diversity is found among the satellite and field SM in terms of spatial distribution. This disagreement may cause the poor performance of the relationship between spatial coefficients of variation (CV) and mean SM. Compared with the field SM, the FY3C SM has smaller bias than the other satellite products. FY3C also performs better than SMOS in terms of the root‐mean‐square error (RMSE) and unbiased RMSE (ubRMSE) but has the smallest correlation coefficient (R). The SMAP product is generally the best among the three products in terms of RMSE, ubRMSE and R. However, a good performance in those metrics does not guarantee the same results on various time scales. On subseasonal time scales, the R in FY3C is the smallest among the three products, and the SMAP product has the largest R, but its amplitudes of the subseasonal variations are much smaller than the field observations. This indicates that when the SMAP products are applied for the analysis on subseasonal SM–atmosphere interactions, the effects of SM may be underestimated. On 10–30 days and above 60 days, dry period tends to have large spatial CV but this phenomenon is weak in all the satellite products. On the other hand, dry area tends to have large temporal CV on the four time scales, and this relationship is the strongest in SMAP but the weakest in FY3C. Therefore, there are large uncertainties of variability among different satellite SM products on subseasonal time scales over eastern China. Besides seasonal and overall performance, more attention is called to the variations on different time scales.
... Passive microwaves are preferred for estimating SM (Chew & Small, 2018;Entekhabi et al., 2010;Peng & Loew, 2017). Passive microwave sensors including Scanning Multichannel Microwave Radiometer, Special Sensor Microwave/Imager sensors, Advanced Microwave Scanning Radiometer Earth Observing System and Soil Moisture Ocean Salinity Satellite/Soil Moisture Active Passive mission have been used successfully to measure SM (Ahmad et al., 2011;Jones et al., 2010;Kerr et al., 2001;Njoku & Chan, 2006;Paloscia et al., 2001;Wen et al., 2005;Wigneron et al., 2021). However, spatial and temporal resolution is significantly lower than the infrared range and is thus considered a significant limiting factor for SM measurements. ...
With population growth, water will increase in the following decades tremendously. The optimization of water allocation for agriculture requires accurate soil moisture (SM) monitoring. Recent Global Navigation Satellite System Reflectometry (GNSS-R) studies take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations to retrieve spatiotemporal soil moisture changes for soil with high clay content. It presents the advantage of sensing a whole surface around a reference GNSS antenna. This article focuses on sandy SM monitoring in the driest condition observed in the study field of Dahra, (Senegal). The area consists of 95% sand and in situ volumetric soil moisture (VSM) range from ~3% to ~5% durinf the dry to the rainy season. Unfortunately, the GNSS signals’ waves penetrated deep into the soil during the dry period and strongly reduced the accuracy of GNSS reflectometry (GNSS-R) surface moisture measurements. However, we obtain VSM estimate at low/medium penetration depth. The correlation reaches 0.9 with VSM error lower than 0.16% for the 5–10-cm-depth probes and achieves excellent temporal monitoring to benefit from the antenna heights directly correlated to spatial resolution. The SM measurement models in our research are potentially valuable tools that contribute to the planning of sustainable agriculture, especially in countries often affected by drought.
... Previous studies have evaluated soil moisture estimates using in situ observations (e.g., field data and climate stations with soil sensors) [15,18,204] and remotely sensed products [205]. Many challenges of such evaluations may exist due to differences in spatial (horizontal) scaling [149,206], measurements of VWC at different depths/volumes [149,207] and temporal differences. ...
Soil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. However, spatially explicit soil-climate estimates that can inform management and research are lacking. We developed a framework to estimate spatiotemporal-varying soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes as gridded surfaces by enhancing the Newhall simulation model. Importantly, our approach allows for the substitution of data and parameters, such as climate, snowmelt, soil properties, alternative potential evapotranspiration equations and air-soil temperature offsets. We applied the model across the western United States using monthly climate averages (1981–2010). The resulting data are intended to help improve conservation and habitat management, including but not limited to increasing the understanding of vegetation patterns (restoration effectiveness), the spread of invasive species and wildfire risk. The demonstrated modeled results had significant correlations with vegetation patterns—for example, soil moisture variables predicted sagebrush (R² = 0.51), annual herbaceous plant cover (R² = 0.687), exposed soil (R² = 0.656) and fire occurrence (R² = 0.343). Using our framework, we have the flexibility to assess dynamic climate conditions (historical, contemporary or projected) that could improve the knowledge of changing spatiotemporal biotic patterns and be applied to other geographic regions.
... It is considered unacceptable if PBIAS is greater than 20% [44,45]. When the Corr is high and the RMSE is low, the simulation is considered robust and more desirable [46]. of September. The simulated GPP generally coincided with the observations, and the simulated GPP of the BGCDV_CTL experiment started earlier and ended later than that of the observed GPP. ...
Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal–deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation.
... As mentioned in the introduction there are a number of types of remote sensing sources that can be useful for soil moisture estimation (Ahmed, Zhang, and Nichols 2011). We select sources that have a significant correlation with soil moisture and/or have potential to help with the disaggregation of low resolution soil moisture estimates. ...
We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and SMAP (passive radar) as well as geophysical variables from SoilGrids and modelled soil moisture fields from GLDAS. The model was trained and evaluated on data from ~1300 in-situ sensors globally over the period 2015 - 2021 and obtained an average per-sensor correlation of 0.727 and ubRMSE of 0.054, and can be used to produce a soil moisture map at a nominal 320m resolution. These results are benchmarked against 13 other soil moisture works at different locations, and an ablation study was used to identify important predictors.
... In addition, soil moisture anomalies in under-populated areas can be an indicator of wildfire risk [1,2]. Thus, large number of researches have introduced methods to retrieve soil moisture content using remote sensing data as listed by Ahmad et al. [3], namely active, passive and a combination of both active and passive remote sensing methods. ...
Predicting soil moisture plays a key role in precision agriculture development and wildfires prevention. In this paper, we designed and implemented a novel deep learning architecture to predict soil moisture content (SMC) from satellite images using vegetation index (NDVI). The architecture combines a set of U-Net semantic segmentation model with a sequence-to-sequence ConvLSTM layers in order to capture both the pixel-wise satellite image content as well as taking into account the spatial correlation property of SMC. The model was trained on data collected from European Sentinel-2 and NASA SMAP satellites for an agricultural area in the Senegalese-Mauritanian river valley. We deployed the model with an end-to-end ML-Ops pipeline using KServe on Google Cloud platform and Microsoft Azure with a production-ready Json-API. The model shows predictions close to ground truth data with a Mean Absolute Error of 0.0325, a RMSE of 0.0447 and an unbiased RMSE of 0.0435.
... For modeling the radar backscattered signal, three kinds of models have been 1.3. Soil moisture remote sensing techniques 23 used for soil-moisture estimation: the physical (theoretical) models, empirical models, and the semi-empirical models [81]. Physical models for active soil moisture retrieval are based on simulations of σ 0 . ...
Soil moisture remote sensing has been an active area of research over the past few decades due to its essential role in agriculture and in the prediction of some natural disasters. GNSS-Reflectometry (GNSS-R) is an emerging bistatic remote sensing technique that uses the L-band GNSS signals as sources of opportunity to characterize Earth surface. In this passive radar system, the amplitudes of the GNSS signal reflected by soil and the GNSS signal received directly from the GNSS satellites can be used to derive measurements of reflectivity from which the soil moisture content of the surface is determined.The study of soil moisture content using reflectivity measurements can also be applied for the detection of in-land water body surfaces. In this dissertation, we propose in the first step a non-linear estimate of the GNSS signal amplitude. This estimate is based on a statistical model that we develop for the coherent detection of a GNSS signal quantized on 1 bit. We show with experimentations on synthetic and real data that the proposed estimator is more accurate than reference approaches and provide measurements of the Signal-to-Noise Ratio (SNR) at a higher rate. When the reflected GNSS signal is obtained in an airborne experiment, its evolution as a function of time is piecewise stationary. The different stationary parts are associatedto different kinds of reflecting surfaces. We propose in a second step a change point detector that takes into account the radar signal characteristics in order to segment the signal. We show on synthetic data that the proposed change point detector can detect and localize changes more accurately than reference approaches present in the literature. This work is applied to airborne GNSSR observation of Earth. We propose in the third step, a new GNSS-R sensor with its implementation on a lightweight airborne carrier. We also propose a new front-end receiver architecture, a software radio implementation of thereceiver, and the complete instrumentation of the airborne carrier. A real flight experimentation has taken place in the North of France obtaining reflections from different landforms. We show using the airborne GNSS measurements obtained, that the proposed radar technique detects different surfaces along the flight trajectory, and in particular in-land water bodies, with high temporal and spatial resolution. We also show that we can localize the edges of the detected water body surfaces at meter accuracy.
... For more information, see https://creativecommons.org/licenses/by/4.0/ passive remote sensing systems are used in soil moisture remote sensing [18]- [20]. Examples of passive microwave remote sensing systems include the Scanning Multichannel Microwave Radiometer (SMMR [21]), the Special Sensor Microwave/Imager (SMM/I [22]), the Advanced Microwave Scanning Radiometer (AMSR [23]), the Soil Moisture Active Passive (SMAP [24]) satellite, and the Soil Moisture and Ocean Salinity (SMOS [25]) satellite. ...
Spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. There is growing interest in monitoring soil moisture in relation to soil- and land-based natural flood management (NFM), to understand the soil’s ability, via land-use and management changes, and to delay the arrival of flood peaks in nearby watercourses. This article monitors relative surface soil moisture (rSSM) across the Thames Valley, U.K., using Sentinel-1 data, and the Vienna University of Technology (TU-Wien) Change Detection Algorithm, with a novel exploration of monthly and annual normalization factors and spatial averaging. Two pairs of normalization factors are introduced to remove impacts from varying local incidence angles through direct and multiple regression slopes. The spatiotemporal distribution of rSSM values at various spatial resolutions (1000, 500, 250, and 100 m) is assessed. Comparisons with
in situ
soil moisture data from the COSMOS-UK network show that, while general temporal trends agree, the difference in effective depth of measurements, coupled with vegetation impacts during the growing season, makes comparison with soil moisture observations difficult. Temporal rSSM trends can be retrieved at spatial resolutions down to 100 m, and the rSSM RMSE was found to decrease as the spatial resolution increases. The vegetation effects upon the rSSM are further explored by comparing the two dominant land cover types: Arable and Horticulture, and Improved Grassland. It was found that, while the rSSM retrieval for these land covers was possible, and the general soil moisture trend is clear, overlying vegetation during the summer artificially increased the rSSM values.
Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with Soil Moisture Active Passive (SMAP) SSM products as the true value. The errors in CYGNSS SSM are primarily attributed to med–high elevation and large relief. Compared with the Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products, CYGNSS exhibits superior performance in terms of AD and RMSE (median AD = −0.10 m³/m³, RMSE = 0.14 m³/m³). The ubRMSE of CYGNSS (median ubRMSE = 0.094 m³/m³) outperforms SMOS, but is slightly worse than AMSR2, with the differences mainly observed in med–high elevation and large-relief regions. The three satellites complement each other in detecting complex terrain. CYGNSS errors (AD, RMSE) are higher in the rainy season than in the dry season, with greater discrepancies observed in large-relief, high-elevation regions compared to flatter, lower-elevation areas. This study provides the first comprehensive analysis of CYGNSS in such a complex region, offering valuable insights for improving the application of GNSS-R inversion technology.
Growing population and climate change are putting massive stress on the planet. Extreme weather with less predictability has led to conditions like drought, flooding, wildfires and famine. Environmental monitoring is a promising technique for coping with this new climate reality and, ideally, preventing it from getting worse. For example, using soil moisture sensors to control irrigation could enable us to reduce agricultural water consumption by 10-50% [4, 74], which would have significant benefits for preventing desertification and groundwater depletion. Wildfire risk monitoring systems could significantly reduce the impact of fires that have been growing in frequency and destruction. High-density humidity and leaf wetness sensor deployments improve our capacity to monitor disease-causing conditions, letting us avoid or minimize pesticide applications. In addition to traditional sensing modalities, we also need novel sensing technologies that will enable us to more easily and scalably quantify things we currently have no easy way to measure. Moreover, we must build out this sensing infrastructure in a way that does not make environmental problems worse - computing has a large and growing carbon footprint, so it is essential that computing solutions aiming to address the climate crisis are themselves environmentally sustainable.
This review book chapter delves into the transformative landscape of soil-based irrigation scheduling, with a focus on integrating cutting-edge technologies. The chapter begins by elucidating active and passive remote sensing techniques, emphasizing their role in estimating soil moisture levels. It further explores the benefits of soil moisture sensors in irrigation management, including improved water-use efficiency, water conservation, and data-driven decision-making. Subsequently, the narrative shifts to machine learning (ML) in irrigation scheduling, delineating fundamental ML concepts, and their applications in optimizing water usage and crop yield. The abstract highlights how ML supports real-time decision-making, risk mitigation, precision irrigation, and optimization under uncertainty. The discussion section engages in a comprehensive exploration of the implications, challenges, and opportunities associated with these technologies. In particular, it underscores the potential benefits and the challenges involved in utilizing soil moisture sensors, remote sensing data, and ML techniques in synergy. The section emphasizes the importance of data quality assurance, interdisciplinary collaboration, continuous learning, and technology investment in practical irrigation recommendations. The chapter concludes by underscoring the transformative potential of integrated irrigation management systems, offering a greener and more productive future for agriculture.
https://books.google.com/books?hl=en&lr=&id=iunLEAAAQBAJ&oi=fnd&pg=PA361&dq=info:EEK65jHG9XYJ:scholar.google.com&ots=Ipx-_1na8o&sig=oPbggS7u8zr1qHbSCG-g1Twk4QU
Wide-area soil moisture sensing is a key element for smart irrigation systems. However, existing soil moisture sensing methods usually fail to achieve both satisfactory mobility and high moisture estimation accuracy. In this paper, we present the design and implementation of a novel soil moisture sensing system, named as SoilId, that combines a UAV and a COTS IR-UWB radar for wide-area soil moisture sensing without the need of burying any battery-powered in-ground device. Specifically, we design a series of novel methods to help SoilId extract soil moisture related features from the received radar signals, and automatically detect and discard the data contaminated by the UAV's uncontrollable motion and the multipath interference. Furthermore, we leverage the powerful representation ability of deep neural networks and carefully design a neural network model to accurately map the extracted radar signal features to soil moisture estimations. We have extensively evaluated SoilId against a variety of real-world factors, including the UAV's uncontrollable motion, the multipath interference, soil surface coverages, and many others. Specifically, the experimental results carried out by our UAV-based system validate that SoilId can push the accuracy limits of RF-based soil moisture sensing techniques to a 50% quantile MAE of 0.23%.
Soil moisture is an essential parameter for understanding the interactions and feedbacks between the atmosphere and the Earth's surface through energy and water cycles. Knowledge of the spatiotemporal distribution of land surface soil moisture for various environmental and socio-economic studies. Over recent past years, remote sensing using electromagnetic spectra from the optical/thermal to the microwave regions, have been intensively investigated for soil moisture retrieval, providing of several algorithms, models and products that are available for actual applications. However, the use of remote sensing technologies in estimating soil moisture is a challenge in low-income economies due to resource constraints. This present study gives a critical review of the remote sensing approaches applied in estimating soil moisture in Zimbabwe. The research findings show that remote sensing products have little been used in soil moisture monitoring in Zimbabwe.KeywordsGISRemote sensing techniquesSoil moistureZimbabwe
Assessing spatial and temporal variations of soil moisture has a great role in different applications, such as natural resource management, flood-risk prediction, irrigation development, hydrology, and climatology. The current study is focusing on investigating the spatial relationship between soil moisture and vegetation cover. The Delta index model was applied using Sentinel-1 images to estimate soil moisture, and the Normalized Difference Vegetation Index was applied using Sentinel-2 images to assess vegetation cover. Besides, regression analysis was conducted to investigate the spatial relationship between soil moisture and vegetation cover. The derived soil moisture ranges from 0% to 48.4811%. Most areas with high elevation had high soil moisture, and low elevation had low soil moisture. In most part of the study area, vegetation cover and soil moisture have a direct relationship. Thus, higher vegetation cover was observed in areas with higher soil moisture; and lower vegetation cover was observed in areas with lower soil moisture.
The selection of better polarisation and incidence angle for soil moisture retrieval using RISAT-1 microwave data was experimented at Hayathnagar research farm of ICAR-CRIDA, Hyderabad. Fine Resolution Sensor (FRS-1) data (spatial resolution of 3 m) and Medium Resolution ScanSAR (MRS) data (spatial resolution of 25 m) acquired from circular and dual polarised microwave data were used for the retrieval of soil moisture and to identify the best polarisation suitable for the study area. The evaluation was carried out during 2016-17. FRS-1 data was more accurate than MRS data for the extraction of soil moisture in the study area. Circular and dual-Polarisation retrieved soil moisture values were compared with volumetric soil moisture values for assessing the better polarisation. Dual polarisation and the incident angle between 15-20 degrees with R2=0.952 performed better as compared to circular polarisation with more or less than 20 degrees' incident angle. A correction factor of -0.723 was derived and applied to FRS-1 data with less than a 20-degree incident angle for getting real soil moisture values on the spatio-temporal basis to optimize the management of natural ecosystems under climate change threat.
La distribución de la radiación solar es considerada como el elemento microclimático más importante en una comunidad vegetal. El coeficiente de extinción de luz del dosel (k) es un factor importante al aplicar la ley de Lambert-Beer en cultivos, ya que su valor está determinado por la estructura del dosel, la especie y el patrón de plantación (Zarea et al., 2005).
Arundo donax L. es una especie C3, que se propaga vegetativamente a partir de fragmentos de tallos y rizomas. Esto puede limitar el cultivo a gran escala, ya que lleva mucho tiempo e implica costos y esfuerzos considerables. El cultivo de tejidos permite obtener plantas libres de enfermedades, a la vez facilita propagar masivamente material vegetal en cualquier época del año, conservando su potencial genético y calidad sanitaria, cualidades apreciadas para realizar un cultivo (Cavallaro et al., 2011). (Falasca, et al., 2011) delimitaron las áreas aptas para el cultivo en Argentina y concluyeron que la región Centro-Sur de la Provincia de Buenos Aires es apta, ya que presenta adecuada disponibilidad de energía radiante.
En condiciones óptimas de crecimiento, la productividad de la biomasa puede describirse por la cantidad de radiación solar interceptada por las hojas principalmente y la eficiencia con la que dicha radiación interceptada se convierte en materia seca vegetal. (Cosentino et al., 2016) encontraron valores de eficiencia en el uso de la radiación (EUR), en experimentos realizados en Italia, que van desde 1,26 g/MJ cuando crece sin riego ni fertilización, llegando a 1,94 g/MJ en condiciones potenciales. En Italia, en condiciones ambientales favorables, se ha informado de un notable rendimiento durante el primer año de cultivo, superior a 20 t/ha (Cosentino et al., 2016). Sin embargo, no está claro que puedan obtenerse esos resultados en condiciones de cultivo en nuestra región pampeana.
El objetivo del trabajo fue determinar la producción de biomasa en función de la intercepción y eficiencia de utilización de la radiación, de un cultivo de Arundo donax L., en condiciones potenciales (con riego y fertilización) y reales (sin riego, ni fertilización), en Azul, centro geográfico de la provincia de Buenos Aires, partiendo de plantines obtenidos por técnicas de micropropagación.
La utilización del concepto de tasa de desarrollo (inversa del tiempo de duración) permite conocer la respuesta fenológica de los cultivos (De Wit, 1970). La fenología de cereales y leguminosas de invierno es regulada principalmente por la repuesta genética a la temperatura y fotoperíodo de tal forma que se puede analizar la
duración de un subperíodo -o el ciclo completo- por medio de funciones lineales entre los mismos (Confalone et al., 2011).
La cebada tiene una respuesta fotoperiódica de tipo cuantitativa de día largo. Es decir, que a medida que se incrementa el fotoperíodo (de junio a diciembre en el hemisferio sur), el ciclo ontogénico se acorta gradualmente hasta un determinado punto llamado fotoperíodo umbral, en el cual se llega al fotoperíodo óptimo, donde la
respuesta a este factor se satura y su duración solo es modificada por la temperatura (Miralles et al., 2014). La sensibilidad frente al fotoperíodo y el fotoperíodo umbral son características genéticas y por lo tanto, variables entre cultivares de cebada, incluso la sensibilidad varía también en un mismo cultivar para diversas etapas del desarrollo. Por esto, la predicción de la fenología de distintos materiales es una valiosa estrategia para escapar a diferentes eventos de estrés, así como proveer las condiciones propicias para aumentar la duración de ciertas etapas vinculadas estrechamente a la generación de rendimiento (Ramirez-Cáceres, 2013).
El objetivo de este trabajo fue cuantificar el subperíodo emergencia-encañazón (E-En) de un cultivar de cebada por medio de un modelo lineal aditivo considerando la interacción entre temperatura y fotoperíodo
El impacto de los combustibles fósiles sobre la generación de gases de efecto invernadero (GEIs) ha intensificado el estudio de nuevas fuentes de energía renovables, entre ellas los cultivos energéticos, ya que desacelerar el calentamiento global es el principal desafío ambiental para la humanidad (IPCC, 2014).
Arundo donax L. (caña de Castilla) es una especie C3 pero de alta eficiencia fotosintética que se reproduce por rizomas. Una vez implantado, el cultivo puede dar producciones durante 15 a 20 años con una elevada capacidad de reproducción vegetativa. Si bien en otros países la rápida velocidad de producción de biomasa la ubican como candidata para el desarrollo de biocombustibles (Barney y Di Tomaso, 2008), en Argentina hasta la actualidad es considerada una maleza. Aun así, Falasca et al. (2011) analizaron el potencial de la caña de Castilla en nuestro país y determinaron la factibilidad de cultivo para una amplia zona. En un ensayo preliminar en Azul (Facultad de Agronomía-UNCPBA), la producción de biomasa en el primer año de cultivo fue entre 5196 y 11317 kg/ha para densidades de 1 y 2 plantas/m2, respectivamente (Barrado et al., 2019), lo que demuestra la posibilidad de convertirse en un cultivo energético en nuestra región. El objetivo de este trabajo fue ampliar la información existente y evaluar cómo el manejo del cultivo: condiciones potenciales (con riego y fertilización) y reales (sin
riego, ni fertilización) en Azul, centro geográfico de la provincia de Buenos Aires , afectan la determinación del crecimiento y la producción de biomasa.
La temperatura es la principal causa de variación anual en el desarrollo de los cultivos y en algunas especies, la duración del fotoperíodo influye también en el mismo. De esa forma, la relación entre temperatura y desarrollo sustentó la elaboración de los modelos para el cálculo de las sumas térmicas usadas para predecir el momento de ocurrencia de distintas fases fenológicas o etapas del desarrollo. Las distintas versiones de los métodos de sumas térmicas difieren en el grado de precisión de sus previsiones, en función de las interacciones entre la variación del tiempo meteorológico y la fisiología del cultivo (Confalone y Navarro, 1999). El ciclo ontogénico de los cereales está dividido en etapas de desarrollo o fases fenológicas: emergencia, macollaje, encañazón, espigazón, antesis, llenado de grano, madurez fisiológica. La tasa de desarrollo determina la duración del ciclo y tiene una respuesta universal a la temperatura (Sadras et al., 2000), puesto que en todas las etapas ontogénicas a medida que la temperatura aumenta por encima de un valor base (0-4 °C) hasta un valor óptimo (25-26 °C) según el cultivar y etapa fenológica, la tasa de desarrollo se incrementa reduciendo la duración de las etapas (Miralles et al., 2014). El objetivo de este trabajo fue evaluar la performance de algunos modelos de sumas térmicas para predecir la fase fenológica de encañazón en cultivos de trigo, cebada y avena.
The regional-scale climatic impact of urbanization is examined using two land cover parameters, fractional vegetation cover (Fr) and surface moisture availability (Mo). The parameters are hypothesized to decrease as surface radiant temperature (To) increases, forced by vegetation removal and the introduction of non-transpiring, reduced evaporating urban surfaces. Fr and Mo were derived from vegetation index and To data compared from the Advanced Very High Resolution Radiometer (AVHRR), and then correlated to a percentage of urban land cover obtained from a supervised classification of Landsat TM imagery. Data from 1985 through 1994 for an area near State College, PA, USA, was utilized. Urban land cover change (at the rate of >3 per cent km2 per year) was statistically significant when related to a decrease in normalized values of Fr and increase in normalized values of To. The relationship between urbanization and Mo, however, was ill-defined due to variations in the composition of urban vegetation. From a nomogram of values of Fr and To, a Land Cover Index (LCI) is proposed, which incorporates the influence of local land cover surrounding urbanized pixels. Such an index could allow changes in land use at neighbourhood-scale to be input in the initialization of atmospheric and hydrological models, as well as provide a new approach for urban heat island analyses. Furthermore, the nomogram can be used to qualify urbanization effects on evapotranspiration rates.
A technique to retrieve surface soil moisture was assessed at the global scale using a synthetic data set of L-band (1.4 GHz) brightness temperatures TB for 2 years, 1987 and 1988. The global TB database consists of half-degree continental pixels and accounts for within-pixel heterogeneity, on the basis of 1 km resolution land cover maps. The retrievals were performed using a three-parameter inversion method applied to the L-band Microwave Emission of Biosphere model (L-MEB). Three land surface variables were retrieved simultaneously from the multiangular and dual-polarization TB data: surface soil moisture wg, vegetation optical depth τ, and surface temperature TS. The retrievals were obtained in two TS configurations: TS was either unknown or known with an uncertainty of 2 K. Applying these two assumptions, global maps of the estimated accuracy of the wg retrievals were produced, and the capability of the TB to monitor wg was evaluated. A sensitivity study was carried out in order to analyze the effect of the main parameters that may affect the retrieval accuracy: the fraction cover of open water and forests, frozen soil conditions, and the radiometric noise on TB. These results contribute to the better definition of the potential of the observations from future spaceborne missions such as the Soil Moisture and Ocean Salinity (SMOS) project.
Recent technological advances in remote sensing have shown that soil moisture can be measured by microwave remote sensing under some topographic and vegetation cover conditions. However, current microwave technology limits the spatial resolution of soil moisture data. It has been found that the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) are related to surface soil moisture; therefore, a relationship between ground observed soil moisture and satellite NDVI and LST products can be developed. Three years of 1 km NDVI and LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been combined with ground measured soil moisture to determine regression relationships at a 1 km scale. Results show that MODIS NDVI and LST are strongly correlated with the ground measured soil moisture, and regression relationships are land cover and soil type dependent. These regression relationships can be used to generate soil moisture estimates at moderate resolution for study area.
Abstract The information regarding spatial and temporal variation of soil moisture in a catchment is of utmost importance in hydrological, as well as many other studies. Point measurements from gravimetric and other methods for soil moisture determination are insufficient to understand the spatial behaviour of soil moisture in a region. Microwave remote sensing data from active sensors on board various satellites are increasingly being used to map spatial distribution of soil moisture within the 0–10 cm top surface. The northern part of India has a network of large rivers and canals and, therefore, spatial and temporal distribution of soil moisture in this region has a significant bearing on the hydrology of the region. In this paper, results on estimation of soil moisture from an ERS-2 SAR image in the catchment of the Solani River (a tributary to the River Ganga) in and around the town of Roorkee, India, have been presented. The radar backscatter coefficient for each pixel of the image has been modelled from the digital numbers of the SAR image. Gravimetric measurements have been made simultaneously during the satellite pass to determine the concurrent value of volumetric soil moisture at a large number of sample points within the satellite sweep area. The backscatter coefficient is found to vary from –30 dB to –42 dB for a variation in soil moisture from 30 to 75%. Regression analyses between volumetric soil moisture and both the digital numbers and backscatter coefficients were performed. Strong correlations between volumetric soil moisture and digital number were observed with R 2 values of 0.84, 0.75 and 0.83 for bare soil, vegetative and combined surfaces, respectively. A similar trend was observed with the relationship between backscatter and volumetric soil moisture with R 2 values of 0.60, 0.89 and 0.67 for bare soil, vegetative and combined surfaces, respectively. These results demonstrate the utilization of SAR data for estimation of spatial distribution of soil moisture in the region of the present study.
Surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and
energy fluxes at the land surface/atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture
is critical for numerous environmental studies. Recent technological advances in satellite remote sensing have shown that
soil moisture can be measured by a variety of remote sensing techniques, each with its own strengths and weaknesses. This
paper presents a comprehensive review of the progress in remote sensing of soil moisture, with focus on technique approaches
for soil moisture estimation from optical, thermal, passive microwave, and active microwave measurements. The physical principles
and the status of current retrieval methods are summarized. Limitations existing in current soil moisture estimation algorithms
and key issues that have to be addressed in the near future are also discussed.
The Integral Equation Model (IEM) was previously used in conjunction with an inversion model to retrieve soil moisture using multifrequency and multipolarization data from Spaceborne Imaging Radar C-band (SIR-C) and X-band Synthetic Aperture Radar (X-SAR). Convergence rates well above 90%, and small RMS errors were attained, for both vegetated and bare soil areas, using radar data collected during Washita 1994. However, the IEM was originally developed to describe the scattering from bare soil surfaces only, and, therefore, vegetation backscatter effects are not explicitly incorporated in the model. In this study, the problem is addressed by introducing a simple, semiempirical, vegetation scattering parameterization to the multifrequency, soil moisture inversion algorithm. The parameterization was formulated in the framework of the water–cloud model and relies on the concept of a land-cover (land-use)-based dimensionless vegetation correlation length to represent the spatial variability of vegetation across the landscape and radar-shadow effects (vegetation layovers). An application of the modified inversion model to the Washita 1994 data lead to a decrease of 32% in the RMSE, while the correlation coefficient between ground-based and SAR-derived soil moisture estimates improved from 0.84 to 0.95.
This study addresses the issue of the variability and heterogeneity problems that are expected from a sensor with a larger footprint having homogenous and heterogeneous sub-pixels. Improved understanding of spatial variability of soil surface characteristics such as land cover and vegetation in larger footprint are critical in remote sensing based soil moisture retrieval. This study analyzes the sub-pixel variability (standard deviation of sub-grid pixels) of Normalized Difference Vegetation Index and SAR backscatter. Back-propagation neural network was used for soil moisture retrieval from active microwave remote sensing data from Southern Great Plains of Oklahoma. The effect of land cover heterogeneity (number of different vegetation species within pixels) on soil moisture retrieval using active microwave remote sensing data was investigated. The presence of heterogeneous vegetation cover reduced the accuracy of the derived soil moisture using microwave remote sensing data. The results from this study can be used to characterize the uncertainty in soil moisture retrieval in the context of Soil Moisture Active and Passive (SMAP) mission which will have larger footprint.
Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE ~6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas.
Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale.
An overview of the ‘triangle’ method for estimating soil surface wetness and evapotranspiration fraction from satellite imagery is presented here. The method is insensitive to initial atmospheric and surface conditions, net radiation and atmospheric correction, yet can yield accuracies comparable to other methods. We describe the method first from the standpoint of the how the triangle is observed as obtained from aircraft and satellite image data and then show how the triangle can be created from a land surface model. By superimposing the model triangle over the observed one, pixel values from the image are determined for all points within the triangle. We further show how the stretched (or ‘universal’) triangle can be used to interpret pixel configurations within the triangle, showing how the temporal trajectories of points uniquely describe patterns of land use change. Finally, we conclude the paper with a brief assessment of the method's limitations.
Soil moisture is a key parameter in different environmental applications, such as hydrology and natural risk assessment. In this paper, surface soil moisture mapping was carried out over a basin in France using satellite synthetic aperture radar (SAR) images acquired in 2006 and 2007 by C-band (5.3 GHz) sensors. The comparison between soil moisture estimated from SAR data and in situ measurements shows good agreement, with a mapping accuracy better than 3%. This result shows that the monitoring of soil moisture from SAR images is possible in operational phase. Moreover, moistures simulated by the operational Météo-France ISBA soil-vegetation-atmosphere transfer model in the SIM-Safran-ISBA-Modcou chain were compared to radar moisture estimates to validate its pertinence. The difference between ISBA simulations and radar estimates fluctuates between 0.4 and 10% (RMSE). The comparison between ISBA and gravimetric measurements of the 12 March 2007 shows a RMSE of about 6%. Generally, these results are very encouraging. Results show also that the soil moisture estimated from SAR images is not correlated with the textural units defined in the European Soil Geographical Database (SGDBE) at 1:1000000 scale. However, dependence was observed between texture maps and ISBA moisture. This dependence is induced by the use of the texture map as an input parameter in the ISBA model. Even if this parameter is very important for soil moisture estimations, radar results shown that the textural map scale at 1:1000000 is not appropriate to differentiate moistures zones.
In this paper we develop a method to estimate land-surface water content in a mostly forest-dominated (humid) and topographically-varied region of eastern Canada. The approach is centered on a temperature-vegetation wetness index (TVWI) that uses standard 8-day MODIS-based image composites of land surface temperature (TS) and surface reflectance as primary input. In an attempt to improve estimates of TVWI in high elevation areas, terrain-induced variations in TS are removed by applying grid, digital elevation model-based calculations of vertical atmospheric pressure to calculations of surface potential temperature (θS). Here, θS corrects TS to the temperature value to what it would be at mean sea level (i.e., ∼101.3 kPa) in a neutral atmosphere. The vegetation component of the TVWI uses 8-day composites of surface reflectance in the calculation of normalized difference vegetation index (NDVI) values. TVWI and corresponding wet and dry edges are based on an interpretation of scatterplots generated by plotting θS as a function of NDVI. A comparison of spatially-averaged field measurements of volumetric soil water content (VSWC) and TVWI for the 2003-2005 period revealed that variation with time to both was similar in magnitudes. Growing season, point mean measurements of VSWC and TVWI were 31.0% and 28.8% for 2003, 28.6% and 29.4% for 2004, and 40.0% and 38.4% for 2005, respectively. An evaluation of the long-term spatial distribution of land-surface wetness generated with the new θS-NDVI function and a process-based model of soil water content showed a strong relationship (i.e., r² = 95.7%).
Various remote sensing techniques have been evaluated and proven to be a valuable source of information for different hydrological applications. For example, with the actual Earth observation satellites, we can observe the entire river basin in rather than sparse points and provide unique information about properties of the surface or shallow layers of the Earth. Furthermore, the actual remote sensing sensors offer the potential of measuring new hydrologic variables not generally possible with traditional techniques such as soil moisture, snow status, land cover parameters etc. Previous researches in microwave remote sensing technology indicate that surface soil moisture can be inferred with remote sensing systems operating in the microwave region of the electromagnetic spectrum. The ability to estimate soil moisture in the upper surface layer by microwave remote sensing (active and passive) has been demonstrated under a variety of the topographic and land-cover conditions. The primary intent of this project is to produce a spatial estimation of soil moisture from active microwave data with sufficient spatial and temporal resolution using neural networks. The derived soil moisture was analyzed in conjunction with vegetation data to understand the effect of land cover on the soil moisture variation. This paper describes the first steps in evaluating the performance of the neural network classification and presents some of the early results.
This paper presents a study that was conducted using ERS SAR archived images to assess the soil water-content of open desert areas containing two different soil types (loess and sand). For this purpose we used the different look directions model proposed by Blumberg and Freilikher. The results show that the soil moisture predictions are almost equal to those known as typical for these soil types at the equivalent seasons.
A variable transition rate factor is proposed for the modified
IEM, such that it uses a variable dielectric profile down to the radar
observation depth. A theoretical observation depth model is also
proposed. It is shown that radar observation depth calculated by this
model agrees with values noted in literature, and that backscattering
simulations using the variable transition rate factor compare well with
data collected in the European Microwave Signature Laboratory (EMSL)
experiments
A physically based bare-surface soil moisture inversion technique for application with passive microwave satellite measurements, including the Advanced Microwave-Scanning Radiometer-Earth Observing System, Special Sensor Microwave/Imager, Scanning Multichannel Microwave Radiometer, and Tropical Rainfall Measuring Mission Microwave Imager, was developed in this paper. The inversion technique is based on the concept of a simple parameterized surface emission model, the Qp model, which was developed using advanced integral equation model simulations of microwave emission. Through evaluation of the relationship between roughness parameters Qp at different polarizations, it was found that they could be described by a linear function. Using this relationship and the surface emissivities measured from two polarizations, the effect of the surface roughness is cancelled out. In other words, this approach consisted in adding different weights on the v and h polarization measurements so as to minimize the surface roughness effects. This method leads to a dual-polarization inversion technique for the estimation of the surface dielectric properties directly from the emissivity measurements. For validation, we compared the soil moisture estimates, derived from ground radiometer measurements at C- to Ka-band obtained from the Institute National de Recherches Agronomiques' field experimental data in 1993 and the Beltsville Agricultural Research Center's field experimental data at C- and X-band obtained in 1979-1982, with the field in situ soil moisture measurements. The accuracies [root-mean-square error (rmse)] are higher than 4% for the available experimental data at the incidence angles of 50deg and 60deg. The newly developed inversion technique should be very useful in monitoring global soil moisture properties using the currently available satellite instruments that commonly have incidence angles between 50deg and 55deg
In the present study, remote sensing of soil moisture is carried out using the Passive and Active L- and S-band airborne sensor (PALS). The data in this paper were taken from five days of overflights near Chickasha, OK during the 1999 Southern Great Plains (SGP99) experiment. Presently, we analyze the collected data to understand the relationships between the observed signals (radiometer brightness temperature and radar backscatter) and surface parameters (surface soil moisture, temperature, vegetation water content, and roughness). In addition, a radiative transfer model and two radar backscatter models are used to simulate the PALS observations. An integration of observations, regression retrievals, and forward modeling is used to derive the best estimates of soil moisture under varying surface conditions.
A semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces is presented. Based on existing scattering models and data sets measured by polarimetric scatterometers and the JPL AirSAR, the parameters of the co-polarized phase-difference probability density function, namely the degree of correlation α and the co-polarized phase-difference ς, in addition to the backscattering coefficients σνν0,σhh0 and σνh0, are modeled empirically in terms of the volumetric soil moisture content mν and the surface roughness parameters ks and kl, where k=2πf/c, s is the rms height and l is the correlation length. Consequently, the ensemble-averaged differential Mueller matrix (or the differential Stokes scattering operator) is specified completely by σνν0,σhh0,σνh0,α, and ζ.
The authors present the retrievals of surface soil moisture (SM)
from simulated brightness temperatures by a newly developed error
propagation learning backpropagation (EPLBP) neural network. The
frequencies of interest include 6.9 and 10.7 GHz of the advanced
microwave scanning radiometer (AMSR) and 1.4 GHz (L-band) of the soil
moisture and ocean salinity (SMOS) sensor. The land surface
process/radiobrightness (LSP/R) model is used to provide time series of
both SM and brightness temperatures at 6.9 and 10.7 GHz for AMSRs
viewing angle of 55°, and at L-band for SMOS's multiple viewing
angles of 0°, 10°, 20°, 30°, 40°, and 50° for
prairie grassland with a column density of 3.7 km/m2. These
multiple frequencies and viewing angles allow the authors to design a
variety of observation modes to examine their sensitivity to SM. For
example, L-band brightness temperature at any single look angle is
regarded as an L-band one-dimensional (1D) observation mode. Meanwhile,
it can be combined with either the observation at the other angles to
become an L-band two-dimensional (2D) or a multiple dimensional
observation mode, or with the observation at 6.9 or 10.7 GHz to become a
multiple frequency/dimensional observation mode. In this paper, it is
shown that the sensitivity of radiobrightness at AMSR channels to SM is
increased by incorporating L-band radiobrightness. In addition, the
advantage of an L-band 2D or a multiple dimensional observation mode
over an L-band 1D observation mode is demonstrated
Polarimetric radar measurements were conducted for bare soil
surfaces under a variety of roughness and moisture conditions at L
-, C -, and X -band frequencies at incidence angles
ranging from 10° to 70°. Using a laser profiler and dielectric
probes, a complete and accurate set of ground truth data was collected
for each surface condition, from which accurate measurements were made
of the rms height, correlation length, and dielectric constant. Based on
knowledge of the scattering behavior in limiting cases and the
experimental observations, an empirical model was developed for
σ°hh, σ°vv, and σ°
hv in terms of ks (where k =2π/λ
is the wave number and s is the rms height) and the relative
dielectric constant of the soil surface. The model, which was found to
yield very good agreement with the backscattering measurements of the
present study as well as with measurements reported in other
investigations, was used to develop an inversion technique for
predicting the rms height of the surface and its moisture content from
multipolarized radar observations
Due to its long-term persistence, accurate initialization of land surface soil moisture in fully-coupled global climate models has the potential to greatly increase the accuracy of climatological and hydrological prediction. To improve the initialization of soil moisture in the NASA Seasonal-to-Interannual Prediction Project (NSIPP), a one-dimensional Kalman filter has been developed to assimilate near-surface soil moisture observations into the catchment-based land surface model used by NSIPP. A set of numerical experiments was performed using an uncoupled version of the NSIPP land surface model to evaluate the assimilation procedure. In this study, "true" land surface data were generated by spinning-up the land surface model for 1987 using the International Satellite Land Surface Climatology Project (ISLSCP) forcing data sets. A degraded simulation was made for 1987 by setting the initial soil moisture prognostic variables to arbitrarily wet values uniformly throughout North America....
Over the past several years NASA, USDA, and Princeton University have collaborated to conduct hydrology field experiments in instrumented research watersheds in Pennsylvania and Oklahoma with a goal of characterizing the spatial and temporal variability of soil moisture using microwave sensors. As part of these experiments, L-band radar data from both truck and aircraft sensors were used to validate the performance of a vegetation scattering model in which discrete scatter random media techniques were employed to calculate vegetation transmissivity and scattering. These parameters were then used in a soil moisture prediction algorithm based on a radiative transfer approach utilizing aircraft passive microwave data from the L-band PBMR and ESTAR radiometers. Soil moisture was predicted in both experiments for several large corn fields which represented the densest vegetation canopies of all the test fields. Over the 20 per cent change in soil moisture encountered in the experiments, the match of predicted to measured soil moisture was excellent, with an average absolute error of about 0·02cmcm.
An approach is evaluated for the estimation of soil moisture at high resolution using satellite microwave and optical/infrared (IR) data. This approach can be applied to data acquired by the Visible/Infrared Imager Radiometer Sensor Suite (VIIRS) and a Conical Scanning Microwave Imager/ Sounder (CMIS), planned for launch in the 2009-2010 time frame under the National Polar-Orbiting Operational Environmental Satellite System (NPOESS). The approach for soil moisture estimation involves two steps. In the first step, a passive microwave remote sensing technique is employed to estimate soil moisture at low resolution (y25km). This involves use of a simplified radiative transfer model to invert dual-polarized microwave brightness temperature. In the second step, the microwave-derived low-resolution soil moisture is linked to the scene optical/IR parameters, such as Normalized Difference Vegetation Index (NDVI), surface albedo, and Land Surface Temperature (LST). The linking is based on the 'Universal Triangle' approach of relating land surface parameters to soil moisture. The optical/IR parameters are available at high resolution (y1km) but are aggregated to the microwave resolution for the purpose of building the linkage model. The linkage model in conjunction with high- resolution NDVI, surface albedo and LST is then used to disaggregate microwave soil moisture into high-resolution soil moisture. The technique is applied to data from the Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) acquired for the Southern Great Plains (SGP-97) experiment conducted in Oklahoma in June- July 1997. An error budget analysis performed on the estimation procedure shows that the rms error in the estimation of soil moisture is of the order of 5%. Predicted soil moisture results at high resolution agree reasonably well with low resolution results in both magnitude and spatio-temporal patterns. The high resolution results are also compared with in situ (0-5cm deep) point measurements. While the trends are similar, the soil moisture estimates in the two cases are different. Issues involving comparison of satellite derived soil moisture with in situ point measurements are also discussed.
Presents a neural network for inverting radar backscatter coefficients into soil volumetric moisture content. The soil surface is characterized by a Kirchhoff rough surface. The permittivity of the soil is estimated from the moisture content using a semi-empirical model from Dobson et al. (1985). Radar backscatter coefficients are simulated from a forward model by invoking the scalar approximation to the Kirchhoff rough surface formulation. The limits of the applicability of such model are studied. Using a wide range of soil parameters, about 500 backscatter patterns are generated. The simulated data are used in a back-error propagation neural network which constitutes an inverse scattering model, for soil moisture content. Random noise is introduced to the test sets of backscatter coefficients to simulate measured responses. The results of the inversion are presented. Various issues such as the optimum network configuration, the optimum learning rate and momentum rate, the optimum training iterations and the problem of overtraining are also investigated
Data gathered during the NASA sponsored Multisensor Aircraft Campaign Hydrology (MACHYDRO) experiment in central Pennsylvania (U.S.A.) in July, 1990 have been analysed to study the combined use of active and passive microwave sensors for estimating soil moisture from vegetated areas. These data sets were obtained during an eleven-day period with NASA's Airborne Synthetic Aperture Radar (AIRSAR), and Push-Broom Microwave Radiometer (PBMR) over an instrumented watershed, which included agricultural fields with a number of different crop covers. Simultaneous ground truth measurements were also made in order to characterize the state of vegetation and soil moisture under a variety of meteorological conditions. Various multi-sensor techniques are currently under investigation to improve the accuracy of remote sensing estimates of the soil moisture in the presence of vegetation and surface roughness conditions using these data sets. One such algorithm involving combination of active and passive microwave sensors is presented here, and is applied to representative corn fields in the Mahantango watershed that was the focus of study during the MACHYDRO experiment. In this algorithm, a simple emission model is inverted to obtain Fresnel reflectivity in terms of ground and vegetation parameters. Since Fresnel reflectivity depends on soil dielectric constant, soil moisture is determined from reflectivity using dielectric-soil moisture relations. The algorithm requires brightness temperature, vegetation and ground parameters as the input parameters. The former is measured by a passive microwave technique and the later two are estimated by using active microwave techniques. The soil moisture estimates obtained by this combined use of active and passive microwave remote sensing techniques, show an excellent agreement with the in situ soil moisture measurements made during the MACHYDRO experiment.
Passive and active microwave remote sensing instruments are capable of measuring the surface soil moisture (0-5 cm) and can be implemented on high altitude platforms, e.g. spacecraft, for repetitive large area observations. The amount of water present in a soil affects its dielectric properties. The dielectric properties, along with several other physical characteristics, determine the microwave measurement. In addi tion, the significance of the dielectric properties depends upon the sensor design, especially the wavelength. Instruments operating at longer wave lengths ( > 5 cm) have fewer problems with the atmosphere and vegeta tion, sense a deeper soil layer and maximize soil moisture sensitivity. Another instrument concern is whether to use an active or passive microwave approach. Active approaches, especially synthetic aperture radar, can provide extremely good ground resolution from space ( 10 km). The existing data interpretation algorithms for passive data are well tested for bare soil and vegetation and can be applied to a wide range of conditions. At the present time, the active microwave algorithms have not been widely verified. There has been a significant amount of recent research using both active and passive methods as a result of the availability of new sensor systems. With these new instru ments have also come greater efforts to integrate the observations in large scale multidisciplinary investigations . A greater emphasis on the spatial distribution and temporal behaviour of soil moisture has produced some very interesting and valuable data sets that demonstrate the potential of a dedicated observing system for scientific and operational studies.
Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R2= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R2 value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.
The dynamic of soil moisture is generally affected by the spatial variation in soil surface characteristics such as land cover, vegetation density, soil texture, and soil material. The main purpose of this project is to develop neural network algorithm for soil moisture retrieval from active microwave data. A back-propagation neural network has been used to estimate the soil moisture from Synthetic Aperture Radar data. Soil moisture data with a spatial resolution of 800 m acquired during the SGP97 campaign, were used as truth data in the training and the validation processes. In addition to backscatter values retrieved from RADARSAT-1 image, normalized difference vegetation index (NDVI), land cover and soil texture have been added as an input to neural network algorithm. The effects of sub-pixels variability of the NDVI and land cover type on the retrieval of soil moisture have been investigated by comparing the measured and the predicted soil moisture. Further, all training and validation pixels (800 m resolution) have been labeled as either homogeneous or heterogeneous based on the occurrence of the same land cover type. The results showed that, homogeneous pixels are more likely to have better accuracy than heterogeneous pixels in soil moisture classification. A better correlation between soil moisture and SAR backscattering was found in areas with high soil moisture content, where the surface wetness dominated the vegetation contribution to the radar backscatter
In experimental studies of electromagnetic wave scattering by rough surfaces, estimation of rough surface parameters, such as root-mean-square (rms) height and correlation length, from measured surface height-profile data is often required. For accurate estimation of these parameters, a data sample with sufficiently long record length is desirable. However, the criterion of the data length required for the estimation is not clear. In this research, we statistically estimate errors arising from the data length based on the interval estimates and check the results through a Monte Carlo simulation.
Utilisation of micro-precision agriculture is essential as an optimization method in the production system to increase quality of product in plant factory. Plant canopy is one of essential indicators of its quality degradation. In this study, samples of cultured Sunagoke moss Rhacomitrium canescens were used. It has been utilized as an active greening material in a forestation technology to mitigate the urban heat island effect. Canopy parameters for moss as low stature plants remain difficult to be quantified accurately. The direct measurement of canopy parameters was considered relatively inefficient and destructive to the plants.
A simplified land surface dryness index (Temperature–Vegetation Dryness Index, TVDI) based on an empirical parameterisation of the relationship between surface temperature (Ts) and vegetation index (NDVI) is suggested. The index is related to soil moisture and, in comparison to existing interpretations of the Ts/NDVI space, the index is conceptually and computationally straightforward. It is based on satellite derived information only, and the potential for operational application of the index is therefore large. The spatial pattern and temporal evolution in TVDI has been analysed using 37 NOAA-AVHRR images from 1990 covering part of the Ferlo region of northern, semiarid Senegal in West Africa. The spatial pattern in TVDI has been compared with simulations of soil moisture from a distributed hydrological model based on the MIKE SHE code. The spatial variation in TVDI reflects the variation in moisture on a finer scale than can be derived from the hydrological model in this case.
The lack of continuous soil moisture fields at large spatial scales, based on observations, has hampered hydrologists from understanding its role in weather and climate. The most readily available observations from which a surface wetness state could be derived is the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations at 10.65 GHz. This paper describes the first attempt to map daily soil moisture from space over an extended period of time. Methods to adjust for diurnal changes associated with this temporal variability and how to mosaic these orbits are presented. The algorithm for deriving soil moisture and temperature from TMI observations is based on a physical model of microwave emission from a layered soil–vegetation–atmosphere medium. An iterative, least-squares minimization method, which uses dual polarization observations at 10.65 GHz, is employed in the retrieval algorithm. Soil moisture estimates were compared with ground measurements over the U.S. Southern Great Plains (SGP) in Oklahoma and the Little River Watershed, Georgia. The soil moisture experiment in Oklahoma was conducted in July 1999 and Little River in June 2000. During both the experiments, the region was dry at the onset of the experiment, and experienced moderate rainfall during the course of the experiment. The regions experienced a quick dry-down before the end of the experiment. The estimated soil moisture compared well with the ground observations for these experiments (standard error of 2.5%). The TMI-estimated soil moisture during 6–22 July over Southern U.S. was analyzed and found to be consistent with the observed meteorological conditions.
Surface soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Passive microwave remotely sensed data have great potential for providing estimates of soil moisture with good temporal repetition on a daily basis and on a regional scale (∼10 km). However, the effects of vegetation cover, soil temperature, snow cover, topography, and soil surface roughness also play a significant role in the microwave emission from the surface. Different soil moisture retrieval approaches have been developed to account for the various parameters contributing to the surface microwave emission. Four main types of algorithms can be roughly distinguished depending on the way vegetation and temperature effects are accounted for. These algorithms are based on (i) land cover classification maps, (ii) ancillary remote sensing indexes, and (iii) two-parameter or (iv) three-parameter retrievals (in this case, soil moisture, vegetation optical depth, and effective surface temperature are retrieved simultaneously from the microwave observations). Methods (iii) and (iv) are based on multiconfiguration observations, in terms of frequency, polarization, or view angle. They appear to be very promising as very few ancillary information are required in the retrieval process. This paper reviews these various methods for retrieving surface soil moisture from microwave radiometric systems. The discussion highlights key issues that will have to be addressed in the near future to secure operational use of the proposed retrieval approaches.
Soil moisture is an important component of the water cycle and will be measured for the first time on a global scale by a dedicated passive L-band microwave radiometer that is planned for launch in 2008. Here, the contribution of topography to the error budget is examined for a vegetated scene with uniform microwave emission. Dual-polarization brightness temperature curves were generated over a range of look angles for 1-D scenes with simple geometrical features, and the soil moisture was retrieved assuming a flat surface. The errors were small for the scenarios considered. Theoretical errors were tested for realistic topography with a DEM transect of a mountainous region, and were found to be comparable. Knowledge of the mean slope from high-resolution DEM data can be used to improve the accuracy of the retrieval.
Intended as an overview aimed at potential users of remotely sensed spatial distributions and temporal variations of soil moisture, this paper begins with an introductory section on the fundamentals of radar imaging and associated attributes. To place the soil moisture sensing task in proper perspective, the prerequisite step of classifying terrain into four basic types—bare surfaces, short vegetation, tall vegetation, and urban—is addressed by demonstrating how a dual-frequency polarimetric radar can correctly classify terrain with an accuracy greater than 90%. Over 5000 image pixels with known terrain identity were involved in the evaluation of the radar image classifier. For bare soil (with vegetation cover shorter than 15 cm), radar can estimate the volumetric moisture content (expressed in per cent) of the top 5 cm soil layer with an r.m.s. error of 3.5%. Based on theoretical model predictions as well as experimental observations, strong evidence exists in support of radar's potential for sensing soil moisture under vegetation cover, but no operational algorithm exists at present.
Research has shown that soil moisture information can be retrieved by passive microwave remote sensing. This is even possible when there is a vegetation canopy present if the effects of the vegetation are corrected for. Vegetation correction algorithms must attempt to include all significant physical parameters, yet they must also only require data that can be readily obtained. Previous research proposed a model that attempted to meet these requirements. Critical to this model is the estimation of a vegetation parameter b that characterizes the canopy. In this study we evaluated published data to determine the functional dependence of this parameter on vegetation characteristics.
The L-band brightness temperature of natural grass fields is strongly influenced by rainfall interception. In wet conditions, the contribution of the soil, mulch, and vegetation to the overall microwave emission is difficult to decouple, thus rendering the retrieval of surface soil moisture from a direct emission model difficult. This paper investigates the development and assesses the performances of statistical regressions linking passive microwave measurements to surface soil moisture in order to assess the potential of soil moisture retrievals over natural grass. First, statistical regressions were analytically derived from the L-Band Emission of the Biosphere model (L-MEB). Single configuration (1 angle, 1 polarisation), and multi-configuration regressions (2 angles, or 2 polarisations) were developed. Second, the performance of statistical regressions was evaluated under different rainfall interception conditions. For that purpose, a modified polarisation ratio at L-band was used to build three data sets with different interception levels. In the presence of interception, a regression based on one observation angle (50°) and two polarisations was able to reduce the effects of vegetation and soil roughness on the soil moisture retrievals. The methodology presented in this study is also able to provide estimates of the vegetation and soil roughness contribution to the brightness temperature.
Four approaches for deriving estimates of near-surface soil moisture from radar imagery in a semiarid, sparsely vegetated rangeland were evaluated against in situ measurements of soil moisture. The approaches were based on empirical, physical, semiempirical, and image difference techniques. The empirical approach involved simple linear regression of radar backscatter on soil moisture, while the integral equation method (IEM) model was used in both the physical and semiempirical approaches. The image difference or delta index approach is a new technique presented here for the first time. In all cases, spatial averaging to the watershed scale improved agreement with observed soil moisture. In the empirical approach, variation in radar backscatter explained 85% of the variation in observed soil moisture at the watershed scale. For the physical and best semiempirical adjustment to the physical model, the root-mean-square errors (RMSE) between modeled and observed soil moisture were 0.13 and 0.04, respectively. Practical limitations to obtaining surface roughness measurements limit IEM utility for large areas. The purely image-based delta index has significant operational advantage in soil moisture estimates for broad areas. Additionally, satellite observations of backscatter used in the delta index indicated an approximate 1:1 relationship with soil moisture that explained 91% of the variability, with RMSE = 0.03. Results showed that the delta index is scaled to the range in observed soil moisture and may provide a purely image based model. It should be tested in other watersheds to determine if it implicitly accounts for surface roughness, topography, and vegetation. These are parameters that are difficult to measure over large areas, and may influence the delta index.
An analytical algorithm for the determination of land surface temperature and soil moisture from the Tropical Rainfall Measuring Mission/Microwave Imager (TRMM/TMI) remote sensing data has been developed in this study. The error analyses indicate that the uncertainties of the enrolled parameters will not cause serious errors in the proposed algorithm. By applying the proposed algorithm to TRMM/TMI remote sensing data collected during the Global Energy and Water Experiment (GEWEX) Asian Monsoon Experiment (GAME)/Tibet Intensive Observation Period field campaign in 1998 (IOP'98), the temporal and regional distributions of land surface temperature and volumetric soil moisture are evaluated over the central Tibetan plateau area. To validate the proposed method, the ground-measured surface temperature and volumetric soil moisture are compared to TRMM/TMI-derived land surface temperature and soil Fresnel reflectivity respectively. The results show that the estimated surface temperature is in good agreement with ground measurements; their difference and correlation coefficient are 0.52 ± 2.41 K and 0.80, respectively. A quasi-linear relationship exists between estimated Fresnel reflectivity and ground-measured volumetric soil moisture with a correlation coefficient 0.82. The land surface thermal status can also be clearly identified from the regional distribution of the estimated land surface temperature; the mountainous area and water bodies have a very lower surface temperature, while the river basin shows a higher surface temperature compared to the mountainous area. The southeastern part of the selected area has lower soil moisture, while the river basin exhibits high soil moisture. It is therefore concluded that the proposed algorithm is successful for the retrieval of land surface temperature and soil moisture using TRMM/TMI data over the study area.
This letter describes recent advances in modeling forest emissivity at L-band. The formulation is based on a previously developed discrete model and includes a new representation of forest litter. Comparisons with multitemporal radiometric data collected in the framework of the ldquoBray 2004rdquo experiment, which was carried out within Les Landes forest, are shown and discussed. Input variables are given by using detailed ground measurements. In general, the model reproduces both absolute values and temporal variations of measured brightness temperature. The contribution of the litter to overall emission was found to be important.
The measured effects of vegetation canopies on radar and radiometric sensitivity to soil moisture are compared to first-order emission and scattering models. The models are found to predict the measured emission and backscattering with reasonable accuracy for various crop canopies at frequencies between 1.4 and 5.0 GHz, especially at angles of incidence less than 30°. The vegetation loss factor L (¿) increases with frequency and is found to be dependent upon canopy type and water content. In addition, the effective radiometric power absorption coefficient of a mature corn canopy is roughly 1.75 times that calculated for the radar at the same frequency. Comparison of an L-band radiometer with a C-band radar shows the two systems to be complementary in terms of accurate soil moisture sensing over the extreme range of naturally occurring soil-moisture conditions. The combination of both an L-band radiometer and a C-band radar is expected to yield soil-moisture estimates that are accurate to better than +/-30 percent of true soil moisture, even for a soil under a lossy crop canopy such as mature corn. This is true even without any other ancillary information.
An observing system simulation experiment is developed to test tradeoffs in resolution and accuracy for soil moisture estimation using active and passive L-band remote sensing. Concepts for combined radar and radiometer missions include designs that will provide multiresolution measurements. In this paper, the scientific impacts of instrument performance are analyzed to determine the measurement requirements for the mission concept. The ensemble Kalman smoother (EnKS) is used to merge these multiresolution observations with modeled soil moisture from a land surface model to estimate surface and subsurface soil moisture at 6-km resolution. The model used for assimilation is different from that used to generate "truth." Consequently, this experiment simulates how data assimilation performs in real applications when the model is not a perfect representation of reality. The EnKS is an extension of the ensemble Kalman filter (EnKF) in which observations are used to update states at previous times. Previous work demonstrated that it provides a computationally inexpensive means to improve the results from the EnKF, and that the limited memory in soil moisture can be exploited by employing it as a fixed lag smoother. Here, it is shown that the EnKS can be used in large problems with spatially distributed state vectors and spatially distributed multiresolution observations. The EnKS-based data assimilation framework is used to study the synergy between passive and active observations that have different resolutions and measurement error distributions. The extent to which the design parameters of the EnKS vary depending on the combination of observations assimilated is investigated
This paper summarizes the progress achieved in the active microwave remote sensing of soil moisture during the four years of the AgRISTARS program. Within that time period, from about 1980 to 1984, significant progress was made toward understanding 1) the fundamental dielectric properties of moist soils, 2) the influence of surface boundary conditions, and 3) the effects of intervening vegetation canopies. In addition, several simulation and image-analysis studies have identified potentially powerful approaches to implementing empirical results over large areas on a repetitive basis. This paper briefly describes the results of laboratory, truck-based, airborne, and orbital experimentation and observations.
This work assesses the possibility of obtaining soil moisture maps of vegetated fields using information derived from radar and optical images. The sensor and field data were acquired during the SMEX'02 experiment. The retrieval was obtained by using a Bayesian approach, where the key point is the evaluation of probability density functions (pdfs) based on the knowledge of soil parameter measurements and of the corresponding remotely sensing data. The purpose is to determine a useful parameterization of vegetation backscattering effects through suitable pdfs to be later used in the inversion algorithm. The correlation coefficients between measured and extracted soil moisture values are R=0.68 for C-band and R=0.60 for L-band. The pdf parameters have been found to be correlated to the vegetation water content estimated from a Landsat image with correlation coefficients of R=0.65 and 0.91 for C- and L-bands, respectively. In consideration of these correlations, a second run of the Bayesian procedure has been performed where the pdf parameters are variable with vegetation water content. This second procedure allows the improvement of inversion results for the L-band. The results derived from the Bayesian approach have also been compared with a classical inversion method that is based on a linear relationship between soil moisture and the backscattering coefficients for horizontal and vertical polarizations.
This paper presents a model of microwave emissions from rough surfaces. We derive a more complete expression of the single-scattering terms in the integral equation method (IEM) surface scattering model. The complementary components for the scattered fields are rederived, based on the removal of a simplifying assumption in the spectral representation of Green's function. In addition, new but compact expressions for the complementary field coefficients can be obtained after quite lengthy mathematical manipulations. Three-dimensional Monte Carlo simulations of surface emission from Gaussian rough surfaces were used to examine the validity of the model. The results based on the new version (advanced IEM) indicate that significant improvements for emissivity prediction may be obtained for a wide range of roughness scales, in particular in the intermediate roughness regions. It is also shown that the original IEM produces larger errors that lead to tens of Kelvins in brightness temperature, which are unacceptable for passive remote sensing.
The backscatter measured by radar and the emission measured by a
radiometer are both very sensitive to the moisture content m<sub>υ
</sub> of bare-soil surfaces. Vegetation cover complicates the
scattering and emission processes, and it has been presumed that the
addition of vegetation masks the soil surface, thereby reducing the
radiometric and radar soil-moisture sensitivities. Even though
researchers working in the field of microwave remote sensing of soil
moisture are all likely to agree with the preceding two statements,
numerous claims and counterclaims have been voiced, primarily at
symposia and workshops, espousing the superiority of the radiometric
technique over the radar, or vice versa. The discussion is often reduced
to disagreements over the answer to the following question “Which
of the two sensing techniques is less impacted by vegetation
cover?” This paper is an attempt to answer that question. Using
realistic radiative-transfer models for the emission and backscatter,
calculations were performed for three types of canopies, all at 1.5 GHz.
The results lead to two major conclusions. First, the accepted
presumption that vegetation cover reduces the soil-moisture sensitivity
is not always true. Over certain ranges of the optical depth τ of
the vegetation canopy and the roughness of the soil surface, vegetation
cover can enhance, not reduce, the radar sensitivity to soil moisture.
The second conclusion is that under most vegetation and soil-surface
conditions, the radiometric and radar soil-moisture sensitivities
decrease with increasing τ, and the rates are approximately the same
for both sensors, suggesting that at least as far as vegetation effects
are concerned, neither sensor can claim superiority over the other
Surface soil moisture retrieval algorithms based on passive
microwave observations, developed and verified at high spatial
resolution, were evaluated in a regional scale experiment. Using
previous investigations as a base, the Southern Great Plains Hydrology
Experiment (SGP97) was designed and conducted to extend the algorithm to
coarser resolutions, larger regions with more diverse conditions, and
longer time periods. The L-band electronically scanned thinned array
radiometer (ESTAR) was used for daily mapping of surface soil moisture
over an area greater than 10000 km2 for a one month period.
Results show that the soil moisture retrieval algorithm performed the
same as in previous investigations, demonstrating consistency of both
the retrieval and the instrument. Error levels were on the order of 3%
for area Integrated averages of sites used for validation. This result
showed that for the coarser resolution used that the theory and
techniques employed in the algorithm apply at this scale. Spatial
patterns observed in the Little Washita Watershed in previous
investigations were also observed. These results showed that soil
texture dominated the spatial pattern at this scale. However, the
regional soil moisture patterns were a reflection of the spatially
variable rainfall and soil texture patterns were not as obvious
An empirical algorithm for the retrieval of soil moisture content
and surface root mean square (RMS) height from remotely sensed radar
data was developed using scatterometer data. The algorithm is optimized
for bare surfaces and requires two copolarized channels at a frequency
between 1.5 and 11 GHz. It gives best results for kh⩽2.5,
μυ⩽35%, and θ⩾30°. Omitting the
usually weaker hv-polarized returns makes the algorithm less sensitive
to system cross-talk and system noise, simplifies the calibration
process and adds robustness to the algorithm in the presence of
vegetation. However, inversion results indicate that significant amounts
of vegetation (NDVI>0.4) cause the algorithm to underestimate soil
moisture and overestimate RMS height. A simple criteria based on the
σhv0/σvv0 ratio
is developed to select the areas where the inversion is not impaired by
the vegetation. The inversion accuracy is assessed on the original
scatterometer data sets but also on several SAR data sets by comparing
the derived soil moisture values with in-situ measurements collected
over a variety of scenes between 1991 and 1994. Both spaceborne (SIR-C)
and airborne (AIRSAR) data are used in the test. Over this large sample
of conditions, the RMS error in the soil moisture estimate is found to
be less than 4.2% soil moisture
Soil moisture retrieval from ERS scatterometer data”, PhD dissertation, Vienna university of technology
- W Wagner
Active sensor system for drought stress monitoring
- A M Smith
- K Scipal
- W Wagner