Article

The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations

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Abstract

We propose a novel OPtical TRApezoid Model (OPTRAM), which is based on the linear physical relationship between soil moisture and shortwave infrared transformed reflectance (STR) and is parameterized based on the pixel distribution within the STR-NDVI space. The OPTRAM-based surface soil moisture estimates derived from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds were compared with ground truth soil moisture data. Results indicate that the prediction accuracies of OPTRAM and the traditional thermal trapezoid model are comparable, with OPTRAM only requiring observations in the optical electromagnetic frequency domain. The volumetric moisture content estimation errors of both models were below 0.04 cm³ cm-³. We also demonstrate that OPTRAM only requires a single universal parameterization for a given location, which is a significant advancement that opens a new avenue for remote sensing of soil moisture. ____________________________________________________________________________________ An OPTRAM code is available in Google Earth Engine (written by João Otavio Firigato): https://code.earthengine.google.com/42db584622de1309c60faf4cb5f88573

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... Based on a physically-based radiative transfer model, Sadeghi et al. (2015) indicated that shortwave infrared transformed reflectance (STR) is linearly correlated with soil moisture variability. Based on this model, Sadeghi et al. (2017) developed the optical trapezoid model (OPTRAM) for satellite-based mapping of soil moisture near the surface over bare soil and root-zone soil moisture over vegetated areas. The foremost advantage of OPTRAM is the use of SWIR bands of optical sensors instead of thermal sensors used in the conventional trapezoid model for soil moisture (Nemani et al., 1993). ...
... Between the two SWIR bands on board the Landsat-8 and Sentinel-2 satellite sensors, the band centered around 2200 nm showed a relatively higher correlation with soil moisture availability (Sadeghi et al., 2017). Although the penetration of light in the SWIR portion rarely exceeds a few mm depths of the soil (Norouzi et al., 2021), the surface soil moisture availability can be a suitable representative for the evaporable layer A. Mokhtari et al. ...
... To further clarify this point, Fig. 8 presents a typical STR-VI space [after Petropoulos et al., 2009 andHassanpour et al., 2020] showing two different definitions for the dry edge. The first definition is representative of our manual edge where soil moisture availability is at a non-zero minimum stage (Sadeghi et al., 2017). The second definition is representative of our optimized edge where ET a = 0. ...
Article
Satellite remote sensing technology provides a promising means for near real-time monitoring of crop water status and requirements in agricultural and hydrological applications. Estimation of actual evapotranspiration (ETa) often requires thermal information; however, not every satellite is equipped with a thermal sensor, which limits the estimation of ETa. To address this limitation, here we propose a satellite-based ETa estimation model, OPTRAM-ET, based on the optical trapezoid model (OPTRAM) estimates of soil moisture and a vegetation index (VI). We applied the OPTRAM-ET model to Sentinel-2 and Landsat-8 satellite data and evaluated the model for ETa estimates using 16 eddy covariance flux towers in the United States and Germany with different landcover types, including agriculture, orchard, permanent wetland, and foothill forests. Next, OPTRAM-ET was compared with the conventional land surface temperature (LST)-VI model. The proposed OPTRAM-ET model showed promising performance over all the studied landcover types. In addition, OPTRAM-ET showed comparable performance to the conventional LST-VI model. However, since the OPTRAM-ET model does not need thermal data, it benefits from higher spatial and temporal resolution data provided by ever-increasing drone- and satellite-based optical sensors to predict crop water status and demand. Unlike the LST-VI model, which needs to be calibrated for each satellite image, a temporally-invariant region-specific calibration is possible in the OPTRAM-ET model. Therefore, OPTRAM-ET is substantially less computationally demanding than the LST-VI model.
... The use of microwave RS offers a good alternative to optical RS for mapping irrigated areas also under cloudy conditions, due to the ability of microwaves to penetrate through vegetation canopy and underlying soil, especially at lower frequencies, where measurements are not impeded by clouds or darkness (Sadeghi et al., 2015(Sadeghi et al., , 2017. Specifically, microwave domain measurements can be used to estimate soil moisture dynamics because the pronounced contrast between the dielectric constant values of the wet and dry soils (Baghdadi and Zribi, 2016;Lakhankar et al., 2009). ...
... Although these products have shown greater potential for monitoring soil moisture dynamics, they consider both rainfall and irrigation effects on soil moisture (Karthikeyan et al., 2020) and may require calibrations to account for surface roughness which causes perturbation of the microwave signal (Shi et al., 2006). Additionally, the application of microwave-based retrieval of soil moisture is not well suited for small-scale applications because the very coarse resolution, especially when compared with the higher spatial resolution outputs of optical methods (Sadeghi et al., 2017;Yue et al., 2019). More recently, several authors (Bazzi et al., 2019a;Gao et al., 2018) exploited instead Sentinel-1 SAR (Synthetic Aperture Radar) time series to map irrigated fields. ...
... Recently, Sadeghi et al. (2017) proposed the physically-based OPTRAM. Specifically, this methodology is based on the pixel distribution within the NDVI and shortwave infrared transformed reflectance (STR) space, for estimating the soil moisture, by using only optical data. ...
Article
Under the current water scarcity scenario, the promotion of water saving strategies is essential for improving the sustainability of the irrigated agriculture. In particular, high resolution irrigated area maps are required for better understanding water uses and supporting water management authorities. The main purpose of this study was to provide a stand-alone remote sensing (RS) methodology for mapping irrigated areas. Specifically, an unsupervised classification approach on Normalized Difference Vegetation Index (NDVI) data was coupled with the OPtical TRApezoid Model (OPTRAM) for detecting actual irrigated areas without the use of any reference data. The proposed methodology was firstly applied and validated at the Marchfeld Cropland region (Austria) during the irrigation season 2021, showing a good agreement with an overall accuracy of 70%. Secondly, it was applied at the irrigation district Quota 102,50 (Italy) for the irrigation seasons 2019-2020. The results of the latter were instead compared with the data declared by the Reclamation Consortium, finding an overestimation of irrigated areas of 21%. In conclusion, this study suggests an easy-to-use approach, eventually independent of reference data such as agricultural statistical surveys or records and replicable under different agricultural settings in continental or Mediterranean climates to support stakeholders for regular estimation of irrigated areas in different growing years or detecting eventual unauthorized water uses. However, some uncertainties should be considered, needing further analyses for improving the accuracy of the proposed approach.
... To overcome these two limitations, Sadeghi et al. [28] proposed the physically based Optical Trapezoidal Model (OPTRAM) for SM estimation. This model uses Short Wave Infrared (SWIR) transformed reflectance (STR) instead of LST. ...
... The OPTRAM model developed by Sadeghi et al. [28] to estimate SM is a physically based trapezoidal space of pixel distribution within the STR-NDVI space ( Figure 2). The NDVI is normalized difference vegetation index and STR is SWIR transformed reflectance [54]. ...
... where, the saturation degree W can be expressed as θ (m 3 m −3 ) when multiplied with the soil porosity. OPTRAM is parameterized based on the pixel distributions within the STR-NDVI space and was proposed by Sadeghi et al. [28]. Adapeted with permission from Ref. [28]. ...
Article
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Remote sensing tools have been extensively used for large-scale soil moisture (SM) mapping in recent years, using Landsat satellite images. Rainfall, soil clay percentage, and the standardized precipitation index play key roles in determining the moisture content of crop fields. The objective of this study was to (i) calculate and determine the effectiveness of moisture-related indices in predicting surface SM, (ii) predict surface SM from satellite images using the Optical Trapezoid Model (OPTRAM), and (iii) evaluate if the OPTRAM predictions can be improved by incorporating weather station, soil, and crop data with a random forest algorithm. The ENVI® platform was used to create moisture-related indices maps, and the Google Earth Engine (GEE) was used to prepare OPTRAM maps. The results showed a very weak relationship between the moisture-related indices and surface SM content where r2 and slopes were ˂0.10 and ˂0.20, respectively. OPTRAM SM, when compared with in situ surface moisture, showed weak relationship with regression values ˂0.2. Surface SM was then predicted using random forest regression using OPTRAM moisture values, rainfall, and the standardized precipitation index (SPI), and percent clay showed high goodness of fit (r2 = 0.69) and low root mean square error (RMSE = 0.053 m3 m−3).
... This model has been proven to be resistant to the negative effects of thick vegetation cover as demonstrated by a litany of studies, each approaching the process of its parameterization through a different methodology [3]- [5]. More precisely, this paper proposes a modification of the WCM by considering the use of IEM as a descriptor of the radar signal, and the OPtical TRapezoid Model (OPTRM) [6] as a vegetation descriptor. The proposed design is applied, in this investigation, on Sentinel-1 remote sensing data. ...
... The proposed approach employs and combines the following models: a modified version of the IEM as proposed in [7], the OPTRM [6], and the WCM [2]. The approach consists of the modification of the WCM components. ...
... OPTRM is a multispectral index that exhibits a strong correlation with SMC levels. It is inspired by Thermal-OPTRM and uses a similar approach [6]. However, this model replaces the Land Surface Temperature with the Shortwave infrared Transformed Reflectance (STR) as a function of a vegetation index in the scatterplot determination. ...
Conference Paper
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A novel alteration of the Water Cloud Model (WCM) and its inversion is proposed in this research work to improve the accuracy of Soil Moisture Content (SMC) mapping. This paper suggests using the Optical Trapezoid Model as the sole vegetation descriptor in the WCM, as well as the use of a specific configuration of parameters derived from a function of radar frequency, polarization, dielectric particle size, and orientation distribution. The proposed inversion scheme is applied on Sentinel-1 data along with the Landsat-8 images component. The proposed approach achieves higher SMC estimation accuracies compared to those produced by tested methods. Indeed, the designed approach achieved improvements in terms of accuracy as demonstrated by a decrease of Root Mean Square Error values in the order of 0.3% and 0.55% in the Blackwell farms and Sidi Rached study areas respectively.
... Indeed, development and research utilizing in particular SAR data but also optical sensors has been widespread in soil moisture studies, also in peatlands. Furthermore, decadal satellite observations (e. g., NASA/USGS Landsat and MODIS) provide opportunities for monitoring the state of peatlands for long time periods while newer satellites such as Sentinel satellites provide high spatial and temporal resolution for the most recent past (El Hajj et al., 2017;Gao et al., 2017;Paloscia et al., 2013;Ambrosone et al., 2020;Sadeghi et al., 2017;Bauer-Marschallinger et al., 2018). ...
... Of the optical bands and indices, shortwave infrared (SWIR) bands and indices have been shown to be promising in soil moisture detection. As SWIR reflectance is sensitive also to vegetation, SWIR-based wetness indices typically include also near-infrared (NIR) (Sadeghi et al., 2017;Wang and Qu, 2007;Gao, 1996), or visible light reflectance (Zhang et al., 2013). One of the most recent development has been the use of optical trapezoid model (OPTRAM) that utilizes normalized difference vegetation index (NDVI) as a measure of vegetation content and SWIR transformed reflectance (STR) as a measure of soil moisture. ...
... One of the most recent development has been the use of optical trapezoid model (OPTRAM) that utilizes normalized difference vegetation index (NDVI) as a measure of vegetation content and SWIR transformed reflectance (STR) as a measure of soil moisture. A twodimensional STR-NDVI space, in which each pixel represents one observation, models soil moisture along varying vegetation content (Sadeghi et al., 2017). Studies in peatlands have shown that OPTRAM works well in WTD monitoring , but there are also other optical metrics that have been tested in peatland WTD or wetness detection. ...
Article
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Peatland water table depth (WTD) and wetness have widely been monitored with optical and synthetic aperture radar (SAR) remote sensing but there is a lack of studies that have used multi-sensor data, i.e., combination of optical and SAR data. We assessed how well WTD can be monitored with remote sensing data, whether multi-sensor approach boosts explanatory capacity and whether there are differences in regression performance between data and peatland types. Our data consisted of continuous multiannual WTD data from altogether 50 restored and undrained Finnish peatlands, and optical (Landsat 5-8, Sentinel-2) and Sentinel-1 C-band SAR data processed in Google Earth Engine. We calculated random forest regressions with dependent variable being WTD and independent variables consisting of 21 optical and 10 SAR metrics. The average regression performance was moderate in multi-sensor models (R 2 43.1%, nRMSE 19.8%), almost as high in optical models (R 2 42.4%, nRMSE 19.9%) but considerably lower in C-band SAR models (R 2 21.8%, nRMSE 23.4%) trained separately for each site. When the models included data from several sites but were trained separately for six habitat type and management option combinations, the average R 2 was 40.6% for the multi-sensor models, 36.6% for optical models and 33.7% for C-band SAR models. There was considerable site-specific variation in the model performance (R 2 − 3.3-88.8% in the multi-sensor models ran separately for each site) and whether multi-sensor, optical or C-band SAR model performed best. The average regression performance was higher for undrained than for restored peatlands, and higher for open and sparsely treed than for densely treed peatlands. The most important variables included SWIR-based optical metrics and VV SAR backscatter. Our results suggest that optical data works usually better than does C-band SAR data in peatland WTD monitoring and multi-sensor approach increases explanatory capacity moderately little.
... Optical remote sensing is able to retrieve SMC of the skin layer of bare soil based on changes in soil brightness with changing SMC (Liu et al., 2020;Bablet et al., 2018). For vegetated areas, in turn, SMC at the root zone can be derived (Liu et al., 2021;Wyatt et al., 2021;Sadeghi et al., 2017) using the link between plant traits, such as leaf water content or chlorophyll content, and SMC at depths where major water uptake takes place (Bollig and Feller, 2014;Gross et al., 2008). A considerable amount of literature has been published on the successful mapping of these SMC relevant plant traits (Lei et al., 2022;Xie et al., 2019;Zhao and Qin, 2019;Zhang et al., 2018) and SMC itself using optical remote sensing (Döpper et al., 2022;Liu et al., 2020Liu et al., , 2021West et al., 2018;Sadeghi et al., 2017;Hassan-Esfahani et al., 2017;Zaman et al., 2012). ...
... For vegetated areas, in turn, SMC at the root zone can be derived (Liu et al., 2021;Wyatt et al., 2021;Sadeghi et al., 2017) using the link between plant traits, such as leaf water content or chlorophyll content, and SMC at depths where major water uptake takes place (Bollig and Feller, 2014;Gross et al., 2008). A considerable amount of literature has been published on the successful mapping of these SMC relevant plant traits (Lei et al., 2022;Xie et al., 2019;Zhao and Qin, 2019;Zhang et al., 2018) and SMC itself using optical remote sensing (Döpper et al., 2022;Liu et al., 2020Liu et al., , 2021West et al., 2018;Sadeghi et al., 2017;Hassan-Esfahani et al., 2017;Zaman et al., 2012). ...
... The S2 and S2Soil-Topo RFR-models are mainly driven by SWIR bands, especially band 11, and spectral indices that include bands of the SWIR, such as the normalized difference infrared index (NDII) or the normalized soil moisture index (NSMI). These results confirm the high potential of the SWIR bands to distinguish moisture states of soil and plant material (Ambrosone et al., 2020;Sadeghi et al., 2017;Shih and Jordan, 1992). The S2 and S2SoilTopo models, which do not distinguish between wet and dry periods, imply that S2 is not affected by the decreasing penetration depth of the CRNS-based SMC signal with wetter soils. ...
Article
Full-text available
Deriving soil moisture content (SMC) at the regional scale with different spatial and temporal land cover changes is still a challenge for active and passive remote sensing systems, often coped with machine learning methods. So far, the reference measurements of the data-driven approaches are usually based on point data, which entails a scale gap to the resolution of the remote sensing data. Cosmic Ray Neutron Sensing (CRNS) indirectly provides SMC estimates of a soil volume covering more than 1 ha and vertical depth up to 80 cm and is thus able to narrow this scale gap. So far, the CRNS-based SMC has only been used as validation source of remote sensing based SMC products. Its beneficial large sensing volume, especially in depth, has not been exploited yet. However, the sensing volume of the CRNS, which is changing with hydrological conditions, bears challenges for the comparison with remote sensing observations. This study, for the fist time, aims to understand the direct linkage of optical (Sentinel 2) and SAR (Sentinel 1) data with CRNS-based SMC. Thereby, the CRNS-based SMC is obtained by an experimental CRNS cluster that covers the high temporal and spatial SMC variability of an entire pre-alpine subcatchment. Using different Random Forest regressions, we analyze the potentials and limitations of both remote sensing sensors to follow the CRNS-based SMC signal. Our results show that it is possible to link the CRNS-based SMC signal with SAR and optical remote sensing observations via Random Forest modelling. We found that Sentinel 2 data is able to separate wet from dry periods with a R2 of 0.68. It is less affected by the changing soil volume that contributes to the CRNS-based SMC signal and it is able to assign a land cover specific SMC distribution. However, Sentinel 2 regression models are not accurate (R2
... Soil moisture content (SMC) is one of the important hydro-meteorological variables that influence the interactions between land surface and atmospheric processes (Mallick et al. 2009). However, reliable regional soil moisture data are usually available at a limited number of stations and are expensively determined through conventional point measurements (Mallick et al. 2009, Jacome et al. 2013, Sadeghi et al. 2017. Therefore, satellite data, that is, derived from Sentinel-1, Sentinel-2 and Landsat-8 observations are useful for SMC estimation. ...
... Remote Sensing (RS) techniques, including microwave and optical and thermal satellite, are of the two frequently used ones for soil moisture monitoring. The former is suitable for monitoring global scale SM dynamics while the latter is well suited for smallscale applications (Sadeghi et al. 2017). ...
... In this context, remote sensing could be considered a critical method to map soil moisture at a large scale with dynamics (Rodríguez-Fernández et al. 2019, Feizizadeh et al. 2021a. Besides, the so-called 'trapezoid' or 'triangle' model is of the most frequently applied tools in Remote Sensing of soil moisture estimation by utilizing optical and thermal data (Mohanty et al. 2017, Sadeghi et al. 2017. The potential of thermal infrared remote sensing data for estimating surface soil moisture has been presented by several studies (Price 1980, 1982, Carlson et al. 1994, Fang and Lakshmi 2014, Yang et al. 2015, Zhang and Zhou 2016. ...
Article
This study estimates the surface soil moisture content in a case study situated in the Vietnamese Red River Delta, using the Landsat 8 satellite images. The trapezoidal relationship between land surface temperature and vegetation index was used to obtain soil wetness indexes. A split-window algorithm was developed to overcome the missing of atmospheric data. The method was validated with ground truth across different land covers. The RMSE between the calculated and measured SMC ranges between 0.556 and 0.971 and varies across different types of land covers. The method is important to monitor SMC across large areas with limited surveyed data.
... Significant efforts have been devoted to SM acquisition and estimation techniques during the past decades, and numerous global-scale SM estimates have been generated and are available for scientific studies [11][12][13]. To fulfill the increasingly comprehensive requirements for SM estimates, their quality, including spatial coverage, temporal span, spatial resolution, temporal resolution, time latency, and data precision, is notably improved through advanced methods. ...
... Many studies have been conducted on SM downscaling using statistical models, data fusion, assimilation, and machine-learning algorithms. These works obtained good results by integrating high-resolution ancillary data collections from MODIS, Landsat, and Sentinel [11,14,113,176]. Moreover, machine learning approaches have notable advantages in terms of simplicity, efficiency, and competence. ...
Article
Full-text available
Soil moisture is a crucial component of land–atmosphere interaction systems. It has a decisive effect on evapotranspiration and photosynthesis, which then notably impacts the land surface water cycle, energy transfer, and material exchange. Thus, soil moisture is usually treated as an indispensable parameter in studies that focus on drought monitoring, climate change, hydrology, and ecology. After consistent efforts for approximately half a century, great advances in soil moisture retrieval from in situ measurements, remote sensing, and reanalysis approaches have been achieved. The quality of soil moisture estimates, including spatial coverage, temporal span, spatial resolution, time resolution, time latency, and data precision, has been remarkably and steadily improved. This review outlines the recently developed techniques and algorithms used to estimate and improve the quality of soil moisture estimates. Moreover, the characteristics of each estimation approach and the main application fields of soil moisture are summarized. The future prospects of soil moisture estimation trends are highlighted to address research directions in the context of increasingly comprehensive application requirements.
... These ground-based SSM measurements are often time-consuming and not effective since it provides only pointbased SSM information. Therefore, retrieval of accurate spatial SSM is crucial from local to global scales [6] and potentially can be achieved using space-borne technologies [7] and smart sensors [8]. ...
... The backscattering coefficient (σ°) can be calculated using Equations (1) and (2) for HH and VV polarizations, respectively. In this study, we have used only Equation (2), which is based on VV. σ°HH = 10 −2.75 (cos 1.5 θ/sin 5 θ) × 10 0.028εtan θ (k·s·sinθ) 1.4 λ 0. 7 (1) σ°VV = 10 −2.37 (cos 3 θ/sin 3 θ) × 10 0.046εtan θ (k·s·sinθ) 1.1 λ 0.7 (2) where θ is the incidence angle, s is the soil surface roughness (cm), λ is the SAR wavelength (5.3 cm), and k is (2π/λ). The θ and λ are related to the sensor parameters, while ε and s are the target parameters which are usually unknown. ...
Article
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Surface Soil Moisture (SSM) is a key factor for understanding the physical process between the land surface and atmosphere. With the advancement of Synthetic Aperture Radar (SAR) technology and backscattering models, retrieval of SSM over the land surface at higher spatial resolution became effective and accurate. This study examines the potential of C-band Sentinel-1 SAR data to derive SSM in a dry season (February 2020) over bare soil and vegetated agricultural fields in the Kosi River Basin (KRB) in North Bihar. Field campaigns were conducted simultaneously with Sentinel–1A acquisition date, and measurements comprised 54 in-situ sampling plots for the top of the soil (0–7.6 cm depth) using time-domain reflectometry (TDR–300). The modified Dubois model was employed to estimate relative soil permittivity from the backscatter values (σ°) of VV polarization. With the help of Topp’s model, volumetric SSM (m3/m3) was derived for all areas with normalized difference vegetation index (NDVI) less than 0.4 that majorly covered bare land or sparse vegetation. The key findings demonstrated that model-derived SSM was well correlated with the in-situ SSM with the coefficient of determination (R2) of 0.77 and root mean square error (RMSE) of 0.06 m3/m3. The spatial distribution of SSM ranged from 0.05 to 0.5 m3/m3 over the KRB, and the highest moisture was found in the Kosi Megafan. The modified Dubois model was effective in providing SSM from Sentinel–1A data in bare soil and agricultural fields and, thus, supporting use in hydrological, meteorological and crop planning applications.
... The so-called "trapezoid" or "triangle" methods have been widely applied to SM estimation utilizing both optical and thermal data (Sadeghi et al., 2017). Amani et al. (2016) designed triangle soil moisture index (TSMI) and the modified TSMI (MTSMI) based on the NIR-red space using Landsat-8 OLI imagery. ...
... Moreover, different from optical/thermal data, microwaves can penetrate through vegetation canopy and underlying soil surface to directly detect the surface SM (Tabatabaeenejad et al., 2015;Bai et al., 2019). Therefore, microwave remote sensing has shown a higher potential for retrieving SM (Sadeghi et al., 2017;Amazirh et al., 2018;Bao et al., 2018). The dielectric constant of soil is sensitive to its water content, and backscattering coefficient decreases with decreasing SM (Bindlish and Barros, 2000). ...
Article
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Accurate and timely information on soil moisture (SM) is crucial for better understanding the hydrological and ecological processes in humid saline regions. This study investigated the potential of using Sentinel-1A Synthetic Aperture Radar (SAR) imagery and Machine Learning Algorithms for SM mapping in China's east coast. This study used the recursive feature elimination (RFE) technique to select optimum SAR indices from the backscattering coefficients of the sampling date as well as the averaged backscattering coefficients of the neighbouring dates. The newly developed support vector regression (SVR) model provided more accurate SM estimation than that from the random forest regression (RFR) and multivariate linear regression (MLR) models. By using the leave-one-out cross-validation (LOOCV), the achieved coefficient of determination (R²), root mean square error (RMSE), and the relative RMSE (RRMSE) in SM estimation were 0.77, 1.82%, and 10.29%, respectively. The SVR model was able to map the SM effectively over a large area under the condition that the information of soil surface roughness, soil salinity, and land cover types was unknown. Results from this study demonstrate that high-temporal-resolution Sentinel-1A SAR, with its free-accessibility, offers a great source for the SVR model to provide frequent and accurate SM mapping over coastal saline regions.
... Other approaches to improve the spatial resolution of soil water content estimates combine thermal/optical satellite data through the Thermal-Optical TRAapezoid Model (TOTRAM), and the OPtical TRApezoid Model (OPTRAM). This last model reduces the need for thermal bands (Sadeghi et al., 2017). With the increase in development of artificial intelligence, remote sensing inputs have been combined with machine learning techniques to predict soil water content (Ahmad et al., 2010;Hassan-Esfahani et al., 2015;Amazirh et al., 2018). ...
... With the increase in development of artificial intelligence, remote sensing inputs have been combined with machine learning techniques to predict soil water content (Ahmad et al., 2010;Hassan-Esfahani et al., 2015;Amazirh et al., 2018). While several studies (Paloscia et al., 2013;El Hajj et al., 2017;Sadeghi et al., 2017;Babaeian et al., 2018) have applied both alternatives, i.e. the use of high resolution satellite data alone or its combination with machine learning techniques, obtaining relatively accurate estimates, these have been based on limited sets of observed data monitoring points, and focused on surface soil water content estimates. The extrapolation to different land cover classes and to multiple locations remains a challenge (Bauer-Marschallinger et al., 2018). ...
Article
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Soil water is a critical component of the water balance to make water management decisions at multiple scales. While soil water can be sensed remotely, this is generally at coarse scales (> 12.5 km). In addition, soil moisture products developed at field scale resolutions (< 250 m) have been mostly limited to shallow observations (up to 10 cm depth) and are impacted by land use. The objective of this study was to create an accurate downscaled soil water content product at multiple depths and at a fine 90 m resolution by fusing modelled and remote sensing datasets via deep learning. Reference data was based on the OzFlux and Oznet networks. Covariates included the North America Space Agency (NASA) United States Department of Agriculture (USDA) Soil Moisture Active Passive (SMAP) remote sensing data assimilation model, Sentinel 1 from Copernicus, surface reflectance, land surface temperature and land cover from the Moderate Resolution Imaging Spectroradiometer (MODIS), and gridded soil properties. Two model approaches were used including a multilayer perceptron for the surface (0-10 cm), and recurrent neural networks for the surface/subsurface (0-30 cm and 30-60 cm) soil water content. The surface prediction performance resulted in a root mean square error (RMSE), mean absolute error (MAE) and Pearson’s correlation of 0.073, 0.057, and 0.74, degrading in depth to 0.07, 0.062, and 0.5. Overall, these 90 m resolution predictions improve on the performance of NASA-USDA SMAP (10 km resolution) and the Australian Landscape Water Balance (5 km resolution) simulated soil water contents. Land use/land cover (LULC) are important explaining factors for performance and SHapley Additive exPlanations (SHAP) indicate high importance of NASA-USDA SMAP for surface predictions, while soil properties and LULC increase in importance with depth.
... The moisture content of the soil in OPTRAM is calculated using the explanation of STR-VI space, similar to the classic triangle model [97]. Sadeghi et al. [98] used Sentinel-2 and Landsat-8 figures to show that the OPTRAM model can estimate soil moisture accurately (0.04 cm3/cm3) in grassland and agriculture dominated watersheds in Arizona and Oklahoma, USA. Because the OPTRAM model does not require thermal remote sensing data, it can be used with a wider spectrum of data. ...
... When compared with passive microwave sensors, active microwave sensors have a better spatial resolution. Active sensors, on the other hand, are subject to measurement errors owed to land surface coarseness and vegetation cover or canopy area [98]. Passive sensors, on the additional hand, were more precise and deliver high temporal resolution, but they have a rougher geographical resolution (e.g., 10s of kilometres) [99]. ...
Article
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Emerging technologies include remote sensing, global positioning systems (GPS), geographic information systems (GIS), and the Internet of Things. The Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI) are all the promising tools that are being used to solve complications, improve agricultural operations, and reduce expenses. Satellite remote sensing has been indispensable in understanding Earth and atmospheric dynamics over the last five decades. When compared to ground or aerial sensor acquisitions, satellite sensors have the ability to provide data at global sizes at a lower cost. With the support of satellite remote sensing, the scientific community has attained significant progress in recent years. In consideration of these efforts, the current study is intended to provide a comprehensive review of the function of remote sensing in assessing different water security challenges and other purposes. Crop production forecasting, drought assessment, cropping system analysis, horticultural assessment and development, crop development, thorough site analysis, satellite agro-meteorology, precision farming, crop insurance, and other operational big agricultural applications are examples. This research examines various uses as well as potential gaps in the market. Review Article Katkani et al.; IJECC, 12(4): 1-18, 2022; Article no.IJECC.81195 2
... The estimation of soil moisture and salinity content using optical satellite images has been investigated extensively. Soil salinization at a regional scale can be identified easily by RGB images (composed of the reflectance in Visible wavelengths, i.e., Red, Green and Blue) from optical satellite images (Sadeghi et al., 2017). Spectral reflectance of the wavelength at Short-Wave Infrared Reflectance (SWIR), which is usually derived from multispectral satellites sensors (e.g., MODIS, Landsat 8 and Sentinel-2), is feasible for reflecting relative changes in soil moisture and salinity content (El Harti et al., 2016;Rahimzadeh-Bajgiran et al., 2013). ...
... Spectral reflectance of the wavelength at Short-Wave Infrared Reflectance (SWIR), which is usually derived from multispectral satellites sensors (e.g., MODIS, Landsat 8 and Sentinel-2), is feasible for reflecting relative changes in soil moisture and salinity content (El Harti et al., 2016;Rahimzadeh-Bajgiran et al., 2013). By combining the SWIR with Land surface temperature (LST), the soil moisture content can be quantified by the empirical modeling methods based on semi-physical mechanism (Sadeghi et al., 2017;Bazzi et al., 2020). The spectral reflectance at wavelengths of Red and Near Infrared (NIR) usually falls in the spectrum boundaries fixed by a series of feature parameters. ...
Article
Soil moisture and salinity are both important environmental variables for crop growth in agricultural production areas. Optical remote-sensing datasets from different sensors are available for estimating soil moisture and salinity from different spatial-temporal scales. Given the co-regulation of soil spectral reflectance (SR) by soil moisture and salinity, the simultaneous estimation of moisture and salinity in saline soil may result in great bias and uncertainty. To address this problem, soil samples were collected in the salinized area during irrigation. Synchronously, processed multi-spectral images were acquired from Sentinel-2 satellite. The spectrum mechanism responsive to soil moisture and salinity was verified by statistical tests, and its corresponding mathematical model (MSS model) was developed to identify the dominant factors affecting SR and to inverse moisture and salinity. The result showed that the effects of moisture and salinity were temporally constant (facilitation) and changing (from inhibition to facilitation), respectively, during the irrigation stages. The dominant factors in the variation of SR shifted from salinity and moisture-salinity interaction to moisture. Reliable accuracy was achieved in the moisture and salinity estimation using inverse MSS model. The profile from the series of estimations can further reveal the dynamic changes of soil moisture and salinity content during irrigation, and provide guidance for local irrigation management.
... Synergistic use of optical and TIR bands is an important direction, which could provide vegetation water stress conditions and SM information. One of the most well-known approaches is the "trapezoid" or "triangle" method [25,26]. The temperature vegetation dryness index (TVDI), proposed by Sandholt et al. [27], is based on a triangle feature space established using a scatterplot of LST and a normalized difference vegetation index (NDVI). ...
Article
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Soil moisture is a key parameter in hydrological research and drought management. The inversion of soil moisture based on land surface temperature (LST) and NDVI triangular feature spaces has been widely used in various studies. Remote sensing provides regional LST data with coarse spatial resolutions which are insufficient for field scale (tens of meters). In this study, we bridged the data gap by adopting a Data Mining Sharpener algorithm to downscale MODIS thermal data with Vis-NIR imagery from Sentinel-2. To evaluate the downscaling algorithm, an unmanned aerial system (UAS) equipped with a thermal sensor was used to capture the ultra-fine resolution LST at three sites in the Tang River Basin in China. The obtained fine-resolution LST data were then used to calculate the Temperature Vegetation Dryness Index (TVDI) for soil moisture monitoring. Results indicated that downscaled LST data from satellites showed spatial patterns similar to UAS-measured LST, although discrepancies still existed. Based on the fine-resolution LST data, a 10-m resolution TVDI map was generated. Significant negative correlations were observed between the TVDI and in-situ soil moisture measurements (Pearson’s r of −0.67 and −0.71). Overall, the fine-resolution TVDI derived from the downscaled LST has a high potential for capturing spatial soil moisture variation.
... Te approach to monitoring SM with optical data is mainly based on the spatial shape of pixel distribution in Landsat-8 bands to estimate SM. Te SWIR 1 and SWIR 2 bands of Landsat-8 data (1650 nm corresponding to band 6, 2210 nm corresponding to band 7) are sensitive to SWC, especially at 2210 nm [43]. In this International boundary Boundary of province, autonomous region, or municipality directly under the central government Undefined LEGEND 1:60,000,000 ...
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Most of the approaches to retrieve surface soil moisture (SSM) by optical and thermal infrared (TIR) spectroscopies are purposed to calculate various characteristic bands/indices and then to establish the regression relationship between them in combination with the measurement data. However, due to the combined impact of many factors, the regression relationship often shows nonlinearity. Moreover, the relationship between the single temporal image and the measured data are not transplantable in time and space, which makes it difficult to construct a more general model for the remote sensing (RS) estimation of SSM. In order to solve this problem, the back propagation (BP) neural network (NN) with an excellent nonlinear mapping ability is introduced to determine the relationship between the characteristic band/index and the measurement data. In the BPNN model, the optical and TIR RS data in different periods were taken as the input parameters, and the in situ soil moisture data were treated as the output parameter. There are 12 schemes designed to retrieve SSM. The key findings of study were as follows: (1) the BPNN model could retrieve SSM with a high accuracy that indicates the correlation coefficient between the estimated and measured soil moisture as 0.9001 and (2) the SSM retrieval model based on the BPNN can be applied to estimate the SSM with different spatial resolution values.
... However, the main limitation of this procedure is to know the crop-soil water holding capacity, which is not usually available for commercial fields and it is crop dependent. Assessment of water stress from satellite image alone, by combining thermal bands or other multispectral data (Sadeghi et al., 2017), deserves further research. Relationships between spectral response and soil properties and moisture are well documented in the literature by using of remote sensing of optical, thermal and radar data. ...
Article
Crop yield monitoring provides highly appreciated information by decision-makers and end-users, i.e., policymakers, insurance companies or professional farmers. Currently, the dense time series of remote sensing (RS) satellite images allow to accurately describe the spatial and temporal evolution of the canopy, providing valuable information for crop monitoring and yield estimation. In this paper, we present the basis of the integration of RS into the classical approaches for the estimation of biomass production and its partitioning. The proposed approach is based on the well-documented relationships among yield components, i.e. total aboveground biomass and harvest index, and accumulated biophysical variables (radiation absorption, transpiration and crop transpiration coefficient) estimated using widely accepted methodologies based on RS data. The model developed (MYRS: Mapping Yield Remote Sensing-based) provides a mechanistic and quantitative tool for the study of the impact of crop growth and development in the variables determining the final yield in grain crops. While the MYRS model relies on previous studies that demonstrated the feasibility of RS-based approaches to estimate the crop biomass accumulation (Campos et al., 2018a,b) and harvest index (Campoy et al., 2020) in cereal crops, this paper described the operational implementation of these sub-models and the evaluation of the model at field and sub-field scales in commercial fields planted with wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) in Albacete, Ciudad Real, Cuenca, Córdoba and Sevilla (South of Spain). The results revealed the potential of the proposed MYRS model to capture the within and inter-field variability of yield in commercial fields under different environmental and management conditions and with limited requirements for input data. In addition, we discussed in this paper further applications of the model for the evaluation of management strategies and their application in precision agriculture.
... Por ello, son capaces de capturar parcialmente el efecto del estrés hídrico sobre la cubierta vegetal, en concreto aquel que causa una disminución de la expansión foliar, reducción del ritmo de crecimiento de la cubierta y aceleración de la senescencia.Sin embargo, el impacto del estrés hídrico que reduce el ritmo de transpiración de la cubierta no produce cambios evidentes en los valores de reflectividad de la cubierta, en particular aquellos procedentes de las bandas del infrarrojo cercano (NIR) y del rojo (RED), usadas en la elaboración del NDVI. No obstante, estudios recientes han evidenciado una relación entre el estado hídrico del suelo y la respuesta espectral de la cubierta vegetal en las bandas del infrarrojo de onda corta (SWIR) que se encuentran disponibles en los satélites Landsat 8 y Sentinel 2(Sadeghi et al., 2017), aunque este enfoque requiere de trabajos de validación que se desarrollarán en futuras líneas de investigación.En el presente trabajo, el impacto del estrés hídrico que reduce el ritmo de transpiración, se estima como el ratio de la transpiración real respecto a la Capítulo II. Teledetección y su uso en la estimación del rendimiento final de los cultivos. ...
Thesis
La presente Tesis Doctoral se centra en el desarrollo y evaluación de una metodología operativa que permita la modelización del rendimiento final en los cultivos de grano desarrollados en parcelas comerciales bajo una amplia variedad de condiciones ambientales, y de manejo de agua y nutrientes. Dado que unos de los principales factores que limitan las producciones de los cultivos a nivel mundial es la escasez de agua, existe un especial interés en evaluar dicha metodología en cultivos que se desarrollan en condiciones de déficit hídrico. Para ello, se propone el desarrollo y evaluación del modelo MYRS (Mapping Yield Remote Sensing-based) basado en la integración de los datos meteorológicos y las medidas de reflectividad de la cubierta vegetal derivadas de las imágenes de satélite en las bases de los modelos de crecimiento de cultivo (CGMs) para la estimación de cada una de las componentes, biomasa seca total acumulada (B) e índice de cosecha (HI), que conforman el rendimiento final (Y) de los cultivos, y además, incluyendo el impacto del estrés hídrico en cada una de estas componentes. Un aspecto innovador del modelo propuesto es la inclusión de una metodología operativa basada en medidas de teledetección para la modelización del índice de cosecha (HI) en cultivos de trigo desarrollados en parcelas comerciales gestionadas en una amplia variedad de manejos de agua y nutrientes. Los resultados obtenidos revelan el desempeño del modelo MYRS para modelizar con precisión el rendimiento final y sus componentes (biomasa total acumulada e índice de cosecha) en cultivos desarrollados en distintos ambientes y sometidos a distintos grados de estrés hídrico. Aunque las series temporales de imágenes multiespectrales de satélite son capaces de recoger parcialmente el efecto del estrés hídrico sobre la cubierta vegetal, como la reducción en el ritmo de crecimiento de la cubierta y/o aceleración de la senescencia, el impacto del estrés hídrico que reduce el ritmo de transpiración requiere de la estimación del coeficiente de estrés hídrico (Ksw),que es modelado a través del balance diario de agua en el suelo explorado por las raíces siguiendo la metodología descrita en FAO-56 (Allen et al., 1998) y asistido por teledetección. Los resultados muestran que la inclusión de Ksw en la estimación de las variables acumuladas (APAR, T, Kt·Kst) es indispensable para conseguir modelar con precisión la acumulación de biomasa y el índice de cosecha en aquellos cultivos sometidos a condiciones de déficit hídrico. Los resultados conseguidos en la modelización de la distribución espacial del rendimiento final de los cultivos y sus componentes, indican la capacidad del modelo MYRS para reproducir con precisión la variabilidad intraparcelaria tanto en cultivos sometidos a estrés hídrico como manejados en condiciones óptimas de agua. Además, se abre la posibilidad de elaborar mapas de distribución espacial del rendimiento final de manera operativa, que sirven para la caracterización del potencial productivo de la parcela, y que constituyen una herramienta indispensable para la implementación de una agricultura de precisión que permita un manejo diferencial. Por otro lado, la aplicación del modelo MYRS en extensas áreas, permite comparar la evolución de los cultivos en diferentes campañas a escala comarcal y cuantificar los posibles daños en la producción final producidos por el efecto de una sequía prolongada. A partir de los resultados y análisis realizados se concluye que el modelo propuesto MYRS (Mapping Yield Remote Sensing-based) es una herramienta operativa valiosa y validada que permite la modelización del rendimiento final en cultivos de grano desarrollados en parcelas comerciales bajo un amplio rango de condiciones ambientales, y para diferentes manejos de agua y grados de estrés hídrico. Además, el modelo propuesto presenta una aplicabilidad que abarca desde la escala intraparcelaria, lo que posibilita el estudio de la distribución espacial del potencial productivo de la parcela, así como a escala de parcela y para extensas áreas, lo que permite la monitorización de la evolución del cultivo a escala de comarca y la posibilidad de cuantificar los daños producidos en el rendimiento final debido al impacto de la sequía.
... Various studies have shown that different soil textures, depending on whether they are fine or coarse, can affect soil water holding capacity (Gholami Bidkhani and Mobasheri, 2018). Other soil properties such as salinity, organic matter, saturated conductivity, field capacity, wilting point, etc. can also affect the accuracy of SWI modeling results in different areas (Fathololoumi et al., 2021;Sadeghi et al., 2017). But not only the map of these parameters is not available for different areas, but ground measurement of these parameters at large scale is time consuming and costly. ...
Article
One of the limitations of daily Soil Water Index (SWI) products obtained from satellite imagery is the low spatial resolution, limiting their precise applications. The purpose of this study was to present a machine learning based approach to improve the spatial resolution of the SWI obtained from the Advanced Scatterometer (ASCAT). Surface biophysical, topographic, and geographical properties (environmental parameters) maps of three field sites from the United States of America (USA), France, and Iran were prepared with a spatial resolution of 30, 1,000 and 10,000 m and their effects on SWI were investigated. A SWI estimation model was constructed based on a Random Forest (RF) regression using effective environmental parameters and used to map SWI at 1,000 and 30 m spatial resolutions. The final SWI map with an improved spatial resolution was prepared after applying a correction due to a residual error. Finally, the efficiency of the proposed model was evaluated based on measured soil moisture (SM) data recorded at ground stations. The results showed that land surface temperature had the greatest effect on the spatial distribution of SWI. The impact of surface biophysical properties on the SWI was greater than topographical and geographical properties. The mean SWI error in USA, France, and Iran at spatial resolution of 10,000 (improved 1,000 m) for warm season were 23.6 % (15.8 %), 14.2 % (9.8 %) and 10.7 % (7.4 %), respectively. These values for cold season were 27.9 % (17.2 %), 15.3 % (13.2 %) and 15.5 % (8.8 %), respectively. Mean of R² and RMSE between measured SM values and SWI 10,000 m (1,000 m and 30 m) were 0.13 (0.43 and 0.73) and 17.6 (12.1 and 7.2 %), respectively. These values for cold season were 0.10 (0.39, 0.67), and 20.7 (14.3, 7.2 %), respectively. The proposed machine learning based approach showed strong potential in improving the spatial resolution of SWI and giving the opportunity for various precise applications.
... In addition to using alternative soil moisture products as baseline maps, covariates that were not included in this study should be examined in terms of their potential to improve model performance and transferability. For example, the shortwave infrared band (SWIR) which is available from most remote sensors is proposed to be useful for improving soil moisture estimates (Sadeghi et al., 2017). A number of RS-derived indices, including Leaf Area Index (LAI), temperature vegetation condition index (TVDI), and crop water stress index (CWSI) (Patel et al., 2009;Sánchez et al., 2012;Chen, Willgoose & Saco, 2015;Akuraju, Ryu & George, 2021), have also been reported to be suitable covariates for soil moisture modeling. ...
Article
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Background High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. In-situ measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale. This study aimed to provide calibrated soil moisture models and methodology for generating gridded estimates of soil moisture at multiple depths, according to user-defined temporal periods, spatial resolution and extent. Methods We applied nearly one million national library soil moisture records from over 100 sites, spanning the U.S. Midwest and West, to build Quantile Random Forest (QRF) calibration models. The QRF models were built on covariates including soil moisture estimates from North American Land Data Assimilation System (NLDAS), soil properties, climate variables, digital elevation models, and remote sensing-derived indices. We also explored an alternative approach that adopted a regionalized calibration dataset for the Western U.S. The broad-scale QRF models were independently validated according to sampling depths, land cover type, and observation period. We then explored the model performance improved with local samples used for spiking. Finally, the QRF models were applied to estimate soil moisture at the field scale where evaluation was carried out to check estimated temporal and spatial patterns. Results The broad-scale QRF model showed moderate performance (R ² = 0.53, RMSE = 0.078 m ³ /m ³ ) when data points from all depth layers (up to 100 cm) were considered for an independent validation. Elevation, NLDAS-derived moisture, soil properties, and sampling depth were ranked as the most important covariates. The best model performance was observed for forest and pasture sites (R ² > 0.5; RMSE < 0.09 m ³ /m ³ ), followed by grassland and cropland (R ² > 0.4; RMSE < 0.11 m ³ /m ³ ). Model performance decreased with sampling depths and was slightly lower during the winter months. Spiking the national QRF model with local samples improved model performance by reducing the RMSE to less than 0.05 m ³ /m ³ for grassland sites. At the field scale, model estimates illustrated more accurate temporal trends for surface than subsurface soil layers. Model estimated spatial patterns need to be further improved and validated with management data. Conclusions The model accuracy for top 0–20 cm soil depth (R ² > 0.5, RMSE < 0.08 m ³ /m ³ ) showed promise for adopting the methodology for soil moisture monitoring. The success of spiking the national model with local samples showed the need to collect multi-year high frequency ( e.g. , hourly) sensor-based field measurements to improve estimates of soil moisture for a longer time period. Future work should improve model performance for deeper depths with additional hydraulic properties and use of locally-selected calibration datasets.
... The revisit period of the two complementary satellites is about five days or shorter. Providing free high-resolution remote sensing images, Sentinel-2 has been applied in the observations of diverse variables such as land cover (Griffiths et al., 2019), vegetation (Chrysafis et al., 2017), and soil moisture (Sadeghi et al., 2017). Here, to retrieve vegetation and surface wetness information across the QTP, six multispectral bands of Sentinel-2A/B including the blue (496.6 nm/492.1 nm), green (560 nm/559 nm), red (664.5 nm/ 665 nm), NIR (835.1 nm/833 nm), short-wave infrared 1 (SWIR1, 1613.7 nm/1610.4 ...
Article
Thermokarst lakes (TLs) widespread in thawing permafrost can degrade vegetation through thermomechanical erosion and waterlogging. However, whether TLs change water sources and alleviate water stress for plants is unknown, and previous knowledge about regional TL effects on surrounding vegetation greenness is rare. Here, we synthesized field investigations, stable isotopes, and remote sensing images from an unmanned aerial vehicle and Sentinel-2 to determine the effects of over 160,000 TLs on their surrounding growing-season normalized difference vegetation index (NDVI) in the Qinghai-Tibet Plateau. The results are as follows: 1) With the largest water source shifting from 0 to 10 cm soil water to lake water, TL-affected plants can grow better than TL-unaffected plants, which is associated with soil texture and the development stage of the lake. 2) Overall, the median affecting distances of TLs on surrounding surface moisture (tasseled cap wetness, TCW) and NDVI were 50 m with their 25th–75th percentiles of 40–70 m and 40–60 m, respectively; compared with the unaffected areas, TLs increased peripheral TCW but reduced NDVI with their median change rates of 19.84% and −10.42%, respectively. 3) However, the nonlinear response of NDVI to the TCW gradient was dominant, which was featured by a local peak NDVI due to the coexistence of the physical destruction and improved water availability, and both TCW and NDVI at the peak area were explicitly larger than those at the unaffected area. 4) Along southeast-northwest environmental gradients (topographic, climatic, and edaphic factors), the affected range and degradation ratio of NDVI were greater under drier climate, larger lake area, sand-richer soil, fewer soil nutrients, and worse vegetation type (e.g., alpine desert). Briefly, the net adverse consequence on NDVI suggests that TLs primarily represent permafrost-supported ecosystems deteriorating, but the clear nonlinear effects advance the knowledge of this landscape; additionally, these local vegetation changes can help interpret the regional greening/browning and promote the research on the biogeochemical interaction among TL, soil, and plants under climate warming and permafrost thawing.
... A perspective solution is remote sensing with satellite imagery (Petropoulos et al., 2015;Sabaghy et al., 2018;Tajik et al., 2019;Zhuang et al., 2020) and the cheaper, newly developed devices such as Uncrewed Aerial Vehicles (UAVs) with multispectral and thermal cameras Hsu and Chang, 2019;Lu et al., 2020), it also can be a versatile way of SWC mapping. Satellite images had been successfully applied in SWC estimation both using thermal band or optical multispectral bands with the Thermal Optical Trapezoid Model (TOTRAM) (Sadeghi et al., 2017;Yadav et al., 2019) and the Optical Trapezoidal Model (OPTRAM) (Babaeian et al., 2018;Hassanpour et al., 2020), but depending on the required data, the spatial resolutions of these models are 20, 30 and 100 m (Sentinel-2, or Landsat-5 and Landsat-8/9, respectively). If higher spatial resolution is needed, cameras mounted on UAVs are the most effective choices (Tmušić et al., 2020). ...
Article
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Soil water content (SWC) estimation is a crucial issue of agricultural production, and its mapping is an important task. We aimed to study the efficacy of UAV-based thermal (TH) and multispectral (MS) cameras in SWC mapping. Soil samples were collected and the SWC content was determined in a laboratory as reference data and four machine learning regression algorithms (Random Forest [RF], Elastic Net [ENR], General Linear Model [GLM], Robust Linear Model [RLM]) were tested for the prediction efficacy, combined with three pixel value extraction methods (single pixel, mean of 20 and 30 cm radius buffer). We found that MS cameras ensured better input data than TH cameras: R²s were 0.97 vs 0.71, mean-normalized root mean square errors (nRMSE) were 10 vs 25 %, respectively. Best models were obtained by the RF (0.97 R²) and ENR (0.88 R²) in case of MS camera. Relationship between SWC and thermal data was exponential, which was incorrectly handled by the GLM (>40 % nRMSE; furthermore, RLM and ENR was not working with only one variable), thus, TH data was acceptable only with the RF (24.4 % nRMSE). Single pixel extraction provided the best input for the estimations, mean of buffered areas did not perform better in the models. Maps provided appropriate SWC estimations according to the nRMSEs, with high spatial resolution. In spite of potential inaccuracies, visualizing the spatial heterogeneities can be a great help to farmers to increase the efficacy of planning irrigation in precision agriculture.
... A combination of optical, thermal, and microwave RS data can provide a better estimate of SSM by eliminating the drawbacks of a single technique. While studies have attempted to fuse optical and microwave (Bao et al., 2018;Bousbih et al., 2018;Rawat et al., 2020) or optical-thermal combinations (Mallick et al., 2009;Sadeghi et al., 2017) or different microwave soil moisture products (Dorigo et al., 2017), the direct fusion of microwave-optical-thermal remote sensing data using machine learning (ML) approaches for soil moisture retrieval remains understudied. Digital soil moisture mapping using ML approaches can provide advantages over physical or semi-empirical models as being more flexible, straightforward and entirely data-driven (Greifeneder et al., 2021). ...
Article
Soil moisture information is key to irrigation water management, drought monitoring, and yield prediction. It plays a vital role in the water cycle and energy budget between the earth's surface and atmosphere. Hence, its monitoring is crucial for both natural and anthropogenic environments. While the current remote sensing-based global SM products available at coarser resolution (3/15 km) are unsuitable for field-level operations, the most widely used microwave remote sensing suffers from model complexities and in-situ data requirements. Weather conditions limit the alternate approaches such as optical/thermal. This study aims to map surface soil moisture (SSM) at 30 m spatial resolution in a semi-arid region by fusing optical, thermal, and microwave remote sensing data using bagging, boosting, and stacking machine learning approaches. The reference data were collected using a soil moisture meter. The covariates included radar backscatter from Sentinel-1, visible, near-infrared, shortwave infrared, land surface temperature, and spectral indices derived from Landsat 8. Boruta algorithm was used for feature selection which identified radar backscatter, modified normalized difference water index, and land surface temperature as the most critical covariates impacting the SSM. The random forest (RF) showed the highest correlation coefficient (r = 0.71), and least root mean square error (RMSE = 5.17%). The cubist model had the least mean bias error (MBE = 0.21%) during independent validation. Stacking of cubist, gradient boosting machine (GBM), and RF using elastic net (ELNET) as meta-learner further reduced the MBE (0.18%) and RMSE (5.03%) during the validation. Overall, stacking multiple machine learning models improved model prediction and can be recommended to improve the digital soil moisture mapping.
... High spatial resolution is necessary for analyzing soil moisture [101]. Thereby, satellites are the principal instruments for characterization and monitoring soil moisture with an accuracy of approximately 5 cm [102]. Ahlmer et al. [103] demonstrated that using satellite data enhances the reliability of flood forecasting. ...
Chapter
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Soil salinity and the water crisis are imposing significant challenges to more than 100 countries as dominant factors of agricultural productivity decline. Given the rising trend of climate change and the need to increase agricultural production, it is crucial to execute appropriate management strategies in farmlands to address salinity and water deficiencies. Ground-based soil moisture and salinity sensors, as well as remote sensing technologies in satellites and unmanned aerial vehicles, which can be used for large-scale soil mapping with high accuracy, play a pivotal role in precision agriculture as advantageous soil condition monitoring instruments. Several barriers, such as expensive rates and a lack of systematic networks, may hinder or even adversely impact the progression of agricultural digitalization. As a result, integrating proximal equipment with remote sensing and Internet of things (IoT) capabilities has been shown to be a promising approach to improving soil monitoring reliability and efficiency. This chapter is an attempt to describe the pros and cons of various soil sensors, with the objective of promoting IoT technology in digital agriculture and smart farming.
... From the perspective of each data's contribution to accuracy improvement, according to Tables 2 and 3, NDVI provides the most improvement in accuracy in terms of RMSE while using only one auxiliary data, followed by DEM and slope. NDVI, representing the vegetation status, affect SM by soil evapotranspiration and the depth of penetration of microwaves into soil, while this is also an essential component in the method of optical remote sensing to inverse SM [51,72]. The DEM and slope are a quantitative description of the terrain. ...
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Surface soil moisture (SM), as a crucial ecological element, is significant to monitor in semiarid mining areas characterized by aridity and little rainfall. The passive microwave remote sensing, which is not affected by weather, provides more accurate SM information, but the resolution is too coarse for mining areas. The existing downscaling method is usually pointed to natural scenarios like agricultural fields rather than mining areas with high-intensity mining. In this paper, combined with geoinformation related to SM, we designed a convolutional neural network (SM-Residual Dense Net, SM-RDNet) to downscale SMAP/Sentinel-1 Level-2 radiometer/radar soil moisture data (SPL2SMAP_S SM) into 10 m spatial resolution. Based on the in-site measured data, the root mean square error (RMSE) was utilized to verify the downscaling accuracy of SM-RDNet. In addition, we analyzed its performance for different data combinations, vegetation cover types and the advantages compared with random forest (RF). Experimental results show that: (1) The downscaling from the 3 km product with the combination of auxiliary data NDVI + DEM + slope performs best (RMSE 0.0366 m3/m3); (2) Effective data combinations can improve the downscaling accuracy at the range of 0.0477–0.1144 m3/m3 (RMSE); (3) The SM-RDNet shows better spatial completeness, details and accuracy than RF (RMSE improved by 0.0905 m3/m3). The proposed SM-RDNet can effectively obtain the fine-grained SM in semiarid mining areas. Our method bridges the gap between coarse-resolution microwave SM products and ecological applications of small-scale mining areas, and provides data and technical support for future research to explore how the mining effect SM in semiarid mining areas.
... In practical applications, SM data are used in Numerical Weather Prediction (NWP) [5], agriculture monitoring [6], and stream-flow forecasting [7]. At present, optical [8] and microwave remote sensing sensors [9] are used for large-scale SM retrieval. However, optical remote sensing has a high spatial resolution but with a relatively long revisit time, and it is easily affected by clouds and mist. ...
Article
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A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient ( R ) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.
... There are numerous methods for determining SWC such as the oven-dried method, TDR (time-domain reflectometry), neutron meter for discrete point's measurement, and the remote sensing method for large-scale monitoring of dynamic changes [3,9,10]. Among them, the oven-dried method is considered the standard due to its accuracy [4], but it consumes energy and time as it requires steps including sampling, drying, and weighting. ...
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Soil water content (SWC) is a critical indicator for engineering construction, crop production, and the hydrologic cycle. The rapid and accurate assessment of SWC is of great importance. At present, digital images are becoming increasingly popular in environmental monitoring and soil property analysis owing to the advantages of non-destructiveness, cheapness, and high-efficiency. However, the capture of high-quality digital image and effective color information acquisition is challenging. For this reason, a photographic platform with an integrated experimental structure configuration was designed to yield high-quality soil images. The detrimental parameters of the platform including type and intensity of the light source and the camera shooting angle were determined after systematic exploration. A new method based on Gaussian fitting gray histogram for extracting RGB image feature parameters was proposed and validated. The correlation between 21 characteristic parameters of five color spaces (RGB, HLS, CIEXYZ, CIELAB, and CIELUV) and SWC was investigated. The model for the relationship between characteristic parameters and SWC was constructed by using least squares regression (LSR), stepwise regression (STR), and partial least squares regression (PLSR). Findings showed that the camera platform equipped with 45° illumination D65 light source, 90° shooting angle, 1900~2500 lx surface illumination, and operating at ambient temperature difference of 5 °C could produce highly reproducible and stable soil color information. The effects of image scale had a great influence on color feature extraction. The entire area of soil image, i.e., 3,000,000 pixels, was chosen in conjunction with a new method for obtaining color features, which is beneficial to eliminate the interference of uneven lightness and micro-topography of soil samples. For the five color spaces and related 21 characteristic parameters, RGB and CIEXYZ spaces and characteristic parameter of lightness both exhibited the strongest correlation with SWC. The PLSR model based on soil specimen images ID had an excellent predictive accuracy and the best stability (R2 = 0.999, RMSE = 0.236). This study showed the potential of the application of color information of digital images to predict SWC in agriculture and geotechnical engineering.
... In addition to common local ground measurement techniques, a multitude of physically-based (Ma and Li, 2020;Li et al., 2021), semiphysical (Vergopolan et al., 2021;Sadeghi et al., 2017;Li et al., 2021) and data-driven (Döpper et al., 2022;Holtgrave et al., 2018;Liu et al., 2020cLiu et al., , 2021Pasolli et al., 2014) remote sensing approaches have proven their ability to monitor SMC at different spatial resolutions over larger areas. However, the spatial resolution of space-borne remote sensing observations comprises a significant scale gap compared the local (point) ground reference. ...
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... Landsat-8 imagery plays an important role in studies on land cover classification [13], land surface temperature and climate change [14,15], agriculture [16], and vegetation property retrieval [17][18][19]. More specifically, Landsat-8 has been used for studies on the retrieval of soil properties, e.g., soil moisture [20,21] and soil salinity [22], while there are few applications of Landsat-8 data for soil HMC retrieval due to their limited spectral sensitivity for soil heavy metal components. Landsat-8 data have a lower spectral resolution compared to hyperspectral data, so to improve detection of Cu concentrations, we combine MTIs for HMC retrieval. ...
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... Land surface temperature (LST) derived from TIR imagery is an asset for understanding climate change, hydrological cycles, and surface-atmosphere interactions (water and energy flow) on different scales [19,20]. It is worth noting that the evaporation-regulated surface cooling/warming inferred from LST and NDVI characteristics can also be used to observe and monitor changes in soil moisture content [21,22]. ...
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... Compared with Landsat TM and other remote-sensing images, Sentinel-2A remote-sensing imaging has higher spectral and spatial resolutions and a shorter revisit cycle (the cycle is five days); it is primarily utilized in global ecological environment monitoring [29]. Morteza Sadeghi investigated soil moisture approaches using Sentinel-2 and Landsat-8 satellites and discovered that Sentinel-2 was more appropriate for the task [30]. Sentinel-2 is suitable for monitoring and mapping soil organic matter, but not soil texture (clay, silt, and sand content) [31]. ...
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Sentinel-2A multi-spectral remote sensing image data underwent high-efficiency differential processing to extract spectral information, which was then matched to soil organic matter (SOM) laboratory test values from field samples. From this, multiple-linear stepwise regression (MLSR) and partial least square (PLSR) models were established based on a differential algorithm for surface SOM modeling. The original spectra were subjected to basic transformations with first- and second derivative processing. MLSR and PLSR models were established based on these methods and the measured values, respectively. The results show that Sentinel-2A remote sensing imagery and SOM content correlated in some bands. The correlation between the spectral value and SOM content was significantly improved after mathematical transformation, especially square-root transformation. After differential processing, the multi-band model had better predictive ability (based on fitting accuracy) than single-band and unprocessed multi-band models. The MLSR and PLSR models of SOM had good prediction functionality. The reciprocal logarithm first-order differential MLSR regression model had the best prediction and inversion results (i.e., most consistent with the real�world data). The MLSR model is more stable and reliable for monitoring SOM content, and provides a feasible method and reference for SOM content-mapping of the study area.
... In contrast, thermal infrared (TIR) wavebands are closely associated with soil thermal properties [110]. Numer-ous studies have also exhibited the feasibility of combining optical and thermal sensors to determine SM variabilities [111][112][113][114]. ...
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Food and water security are considered the most critical issues globally due to the projected population growth placing pressure on agricultural systems. Because agricultural activity is known to be the largest consumer of freshwater, the unsustainable irrigation water use required by crops to grow might lead to rapid freshwater depletion. Precision agriculture has emerged as a feasible concept to maintain farm productivity while facing future problems such as climate change, freshwater depletion, and environmental degradation. Agriculture is regarded as a complex system due to the variability of soil, crops, topography, and climate, and its interconnection with water availability and scarcity. Therefore, understanding these variables’ spatial and temporal behavior is essential in order to support precision agriculture by implementing optimum irrigation water use. Nowadays, numerous cost- and time-effective methods have been highlighted and implemented in order to optimize on-farm productivity without threatening the quantity and quality of the environmental resources. Remote sensing can provide lateral distribution information for areas of interest from the regional scale to the farm scale, while geophysics can investigate non-invasively the sub-surface soil (vertically and laterally), mapping large spatial and temporal domains. Likewise, agro-hydrological modelling can overcome the insufficient on-farm physicochemical dataset which is spatially and temporally required for precision agriculture in the context of irrigation water scheduling.
... There are few reports on the application of VIIRS for highresolution ET estimations involving: the estimation of land surface heat fluxes based on 375-m VIIRS data (Li et al., 2017), DisALEXI disaggregation (pyDisALEXI) of 375-m ET pixels to higher spatial resolutions using Landsat data (GloDET) (Schull et al., 2018), or VIIRS sharpening using the Harmonized Landsat-Sentinel-2 (HLS) data (Xue et al., 2020) at 30-m spatial resolution. Furthermore, there are limited studies on the synergistic use of Sentinel-2, even though Sentinel-2 may have some advantages over other SR data in terms of spectral, spatial, and temporal resolutions (Battude et al., 2016;Sadeghi et al., 2017). ...
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... ;Conforti et al., (2015) usou uma relação entre propriedades físicas e químicas, baseadas entre a reflectância do espectro visível e o infravermelho do sensor Sentinel para estimar a textura do solo;Lucà et al. (2017) aplicou imagens do mesmo sensor para estimar o carbono orgânico e total do solo e nitrogênio em seu outro estudo(LUCÀ et al., 2015).Sadeghi et al. (2017) propõem um método que integra dados dos sensoresLandsat 8 e Sentinel 2 aplicado a o modelo OPtical TRApezoid Model (para determinação da umidade do solo. Existem sensores mais apropriados como os de micro-ondas para a análise deste parâmetro, porém a complicada interpretação dos dados desses sensores acaba se restringindo a estudos mete ...
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CAPÍTULO 4 : RELAÇÃO SOLO-ÁGUA-PLANTA-ATMOSFERA Procurou-se abordar os principais tópicos envolvidos tanto no conhecimento de base para entender essas relações, como apresentar tópicos de pesquisas atuais que vem sendo conduzidas no estado de Pernambuco. Por ser um tema que, normalmente, é explorado de forma individualizada em termos de solo, planta e atmosfera, esperamos que esse material seja útil para estudantes de graduação, docentes e pesquisadores que se interessam pelo tema de forma conjunto.
... The European Space Agency (ESA) under the Copernicus Program provides Sentinel-2 with temporal and spatial (10-20 m) resolution of 5 days and 10-20 m, respectively, which has opened new vistas for many applications for having higher resolution than both MODIS and Landsat [78][79][80][81][82][83][84][85]. Sentinel-2 is a multispectral operational imaging mission for worldwide land observation. ...
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The trapezoid configuration constituted by spatially distributed land surface temperature (LST) and fraction vegetation cover (FVC) has been widely used for estimating surface soil moisture (SSM) with satellite data. However, the trapezoid-based methods still suffer from a major challenge that the determination of the trapezoid pixel window is always subjective, because it requires quasi-identical atmospheric conditions to make SSM retrievals for each pixel spatially comparable. Meanwhile, the trapezoid pixel window should also contain full vegetation coverages and SSM dynamics to fulfill the inscape of the trapezoid. Due to the heterogeneity of atmospheric variables and underlying surfaces, it is commonly difficult to determine a proper trapezoid pixel window in practices. The development of the pixel-to-pixel scheme of the trapezoid aims to decrease the dependence of trapezoid-based SSM retrieval on the subjective requirements of quasi-identical atmospheric conditions, full vegetation coverages and SSM dynamics. In the present study, for each pixel over the North China Plain, a unique virtual trapezoid was created and the theoretical boundaries were determined following the principle of surface energy balance where satellite-derived essential atmospheric variables of air temperature, net surface shortwave radiation and relative humidity were provided as inputs. Based on the proposed approach, a daily/5-km SSM dataset during the growing season from April to October in 2016 was produced over the North China Plain. A preliminary evaluation reveals an overall acceptable accuracy of the estimated SSM with root mean square error of ∼ 0.06 m³/m³ and ∼ 0.08 m³/m³ when comparing to in-situ measurements and microwave SSM product, respectively.
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Reliable soil moisture information (SMI) is crucial for adequate food production, especially in developing countries where crop production is largely dependent on natural rainfall. This chapter demonstrates how SPOT-6 images can be used to provide this information for areas where climate-driven changes in rainfall are imposing limits on crop production. We do this by using these images to rank and spatialize farm-level soil moisture in a selected local municipality in South Africa. This illustration is useful because it provides a cost-effective and user-friendly remote sensing-based methodology to provide timely and reliable SMI in support of farm-level decision making for data-scarce areas under changing climatic conditions.
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Spatio-temporal dynamic monitoring of soil moisture is highly important to management of agricultural and vegetation ecosystems. The temperature-vegetation dryness index based on the triangle or trapezoid method has been used widely in previous studies. However, most existing studies simply used linear regression to construct empirical models to fit the edges of the feature space. This requires extensive data from a vast study area, and may lead to subjective results. In this study, a Modified Temperature-Vegetation Dryness Index (MTVDI) was used to monitor surface soil moisture status using MODIS (Moderate-resolution Imaging Spectroradiometer) remote sensing data, in which the dry edge conditions were determined at the pixel scale based on surface energy balance. The MTVDI was validated by field measurements at 30 sites for 10 d and compared with the Temperature-Vegetation Dryness Index (TVDI). The results showed that the R2 for MTVDI and soil moisture obviously improved (0.45 for TVDI, 0.69 for MTVDI). As for spatial changes, MTVDI can also better reflect the actual soil moisture condition than TVDI. As a result, MTVDI can be considered an effective method to monitor the spatio-temporal changes in surface soil moisture on a regional scale.
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Accurate dryness monitoring is important for formulating reasonable response measures to reduce social and economic losses caused by drought. The land surface temperature (LST), shortwave infrared (SWIR) reflectance, and vegetation index (VI) are popular remote sensing (RS) indices that can be individually used to characterize surface dryness. Given the interactions of these factors, limitations are inevitably associated with using a single factor. Integrated dryness indices that combine LST or SWIR reflectance with the VI have thus been successively proposed and applied for dryness monitoring and soil moisture (SM) retrieval work. However, the advantages of these three indicators have not yet been combined to construct a more comprehensive dryness index. In this study, we integrated the LST, enhanced vegetation index (EVI), and SWIR reflectance and developed an integrated satellite-based dryness index with simple calculations, called the temperature vegetation shortwave infrared reflectance dryness index (TVSDI). The proposed TVSDI was thoroughly assessed in the continental United States (CONUS) using the following data: the soil moisture active passive (SMAP) SM; six commonly used dryness indices (i.e., temperature vegetation soil moisture dryness index (TVMDI), temperature vegetation dryness index (TVDI), modified perpendicular dryness index (MPDI), perpendicular dryness index (PDI), standardized precipitation evapotranspiration index (SPEI), and standardized precipitation index (SPI)); in situ SM data collected from 24 Cosmic-ray neutron probe (CRNP) sites covering different climates, soil types, and land cover types; and the United States Drought Monitor (USDM) maps. The results demonstrated that the TVSDI was significantly correlated with SMAP SM (R = -0.75, p < 0.01) and exhibited better performance than the use of LST, EVI, and SWIR reflectance individually. Moreover, the TVSDI and the other six commonly used dryness indices exhibited good spatiotemporal consistency, all with consistency areas greater than 60%. The evaluation based on in situ SM from 24 CRNP sites indicated that the TVSDI exhibited more stability and accuracy than other satellite-based agricultural dryness indices (TVMDI, MPDI, PDI, and TVDI). Moreover, the spatial patterns of TVSDI maps were not only well-matched with SMAP SM maps but also provided more detailed spatial information. TVSDI maps could capture more dryness and drought variations in natural ecosystems and areas with less intensive human activities than USDM maps. Furthermore, the application of the TVSDI for dryness monitoring in the CONUS revealed that the dryness distributions differed greatly across different geographic regions at monthly and annual scales. In conclusion, the TVSDI was found to be a reliable and accurate satellite-based dryness index.
Chapter
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Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expe- ted that the SMOS estimates will improve.
Conference Paper
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The triangle/trapezoid method is a well known method for retrieving spatialized soil moisture from remotely sensed temperature and vegetation index (NDVI). We selected three approaches with different requirements for ancillary data (triangle empirical method by Sandholt et al. [7], trapezoid method by Moran et al. [2], SVAT triangle method by Carlson et al. [3]-[6]). The empirical inversion is well suited when no information on meteorological data is available. Otherwise, one can build theoretical isolines in the T-NDVI-moisture space by applying the Penman-Monteith equation or by using a detailed SVAT model. The three approaches were compared by using remote sensing data obtained during the airborne HyEurope 2007 campaign over Camargue, south of France. We showed that the SVAT-triangle method has a potential for separately identifying surface moisture and root zone moisture.