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Relationship Between Field Measurement of Soil Moisture in the Effective Depth of Sugarcane Root Zone and Extracted Indices from Spectral Reflectance of Optical/Thermal Bands of Multispectral Satellite Images
Abstract
Estimation of soil moisture variables is very important in agriculture studies such as precision irrigation and its management, so knowledge of the variation would be highly beneficial. Effective irrigation is possible with regular monitoring of soil and plant water conditions and with the forecasting of future crop water requirement. This study explored the capability of indices for estimating soil moisture as an indicator of detection irrigation time in sugarcane farms located in the southwest of Iran. For this goal, Landsat 8 satellite images were got from May until September 2015. Concurrent with the satellite pass times, field measurements include soil moisture (SM) and canopy temperature was done at the predefined points in sugarcane farms. These farms were selected with different age and irrigation days. Various indices were calculated using the combination of optical and thermal infrared wavelengths, and their relationship to the amount of SM was studied. According to the results of this research, vegetation indices based on optical bands do not show a good coefficient of determination (R2). Results showed that soil moisture computed by crop water stress index (CWSI) and temperature vegetation dryness index (TVDI) have a similar trend, which showed that both indices can be used for irrigation scheduling, although they had some differences for computed soil moisture, TVDI had better correlation than to CWSI. TVDI indicated a good correlation with SM measurement data which R2 values range from 0.35 to 0.66, as well as inharmonious with the spatial distribution of (SM). It showed (RMSE) less than 0.2. Comparing recorded irrigation and (SM) in the farms shows that (SM) can be classified into three classes: low soil moisture (0.1 < SM ≤ 0.15), medium soil moisture (0.15 < SM ≤ 0.2) and high soil moisture (0.2 < SM ≤ 0.25). This classification can be utilized for precision irrigation scheduling in soils with heavy and semi-heavy texture (silty clay and silty clay loam). All of the results demonstrated that the TVDI can be utilized for assessment SM and determine irrigation time in agriculture lands, without each other in situ measurement of data.
... CWSI estimation Idso et al. (1981) empirically derived the CWSI using the lower and upper baselines in their study. The simplicity of this technique has led to its widespread application in research studies that employ CWSI irrigation water management (Akuraju et al., 2021;Alghory & Yazar, 2019;Argyrokastritis et al., 2015;Bijanzadeh & Emam, 2012;Erdem et al., 2010;Gölgül et al., 2022;Gontia & Tiwari, 2008;Irmak et al., 2000;Kar & Kumar, 2007;Khorsand et al., 2021;Kumar et al., 2019;Orta et al., 2004;Ru et al., 2020;Veysi et al., 2020). CWSI was estimated using Eq. 2. ...
... A similar correlation can be found in other studies. For instance, Veysi et al. (2020) demonstrated a correlation between CWSI and soil moisture in the range of 0.52 to 0.75 on various dates from May to September 2015 on Iranian sugarcane plantations. Paltineanu et al. (2013) observed a correlation of 0.65 between CWSI and soil moisture in peach orchards in southeastern Romania. ...
... Instead, incorporating additional parameters related to soil and weather conditions significantly improves CWSI prediction. An examination of the relationship between CWSI and soil moisture in a previous study by Veysi et al. (2020) revealed an R 2 of 0.540. In our investigation, this relationship was further strengthened, particularly in C4, where we achieved an R 2 of 0.737. ...
The Crop Water Stress Index (CWSI), a pivotal indicator derived from canopy temperature, plays a crucial role in irrigation scheduling for water conservation in agriculture. This study focuses on determining CWSI (by empirical method) for wheat crops in the semi-arid region of western Uttar Pradesh, India, subjected to varying irrigation treatments across two cropping seasons (2021–2022 and 2022–2023). The aim is to investigate further the potential of four machine learning (ML) models—support vector regression (SVR), random forest regression (RFR), artificial neural network (ANN), and multiple linear regression (MLR) to predict CWSI. The ML models were assessed based on determination coefficient (R²), mean absolute error (MAE), and root mean square error (RMSE) under diverse scenarios created from eight distinct input combinations of six variables: air temperature (Ta), canopy temperature (Tc), vapor pressure deficit (VPD), net solar radiation (Rn), wind speed (U), and soil moisture depletion (SD). SVR emerges as the top-performing model, showcasing superior results over ANN, RFR, and MLR. The most effective input combination for SVR includes Tc, Ta, VPD, Rn, and U (R² = 0.997, MAE = 0.901%, RMSE = 2.223%). Meanwhile, both ANN and MLR achieve optimal results with input combinations involving Tc, Ta, VPD, Rn, U, and SD (R² = 0.992, MAE = 2.031%, RMSE = 3.705%; R² = 0.759, MAE = 13.95%, RMSE = 19.98%, respectively). For RFR, the ideal input combination comprises Tc, Ta, VPD, and U (R² = 0.951, MAE = 5.023%, RMSE = 9.012%). The study highlights the considerable promise of ML models in predicting CWSI, proposing their future application in integration into an irrigation decision support system (IDSS) for crop stress mitigation and efficient water management in agriculture.
... The relationships between crop water stress indexes (CWSI), actual evapotranspiration (ET a ), crop water requirements (ET c ), crop coefficient (K c ), and stress coefficient (K s ) are justified in Equations 8 to 13. In recent years, there has been a significant amount of research focused on investigating CWSI based on canopy temperatures (T c ) obtained from thermal images (Alchanatis et al. 2010;Bellvert et al. 2016;Veysi, Naseri, and Hamzeh 2020). One of the benefits of dimensionless indicators like CWSI is their ability to estimate ET a based on their relationships with other dimensionless coefficients such as K s and K c (DeJonge et al. 2015). ...
... In sugarcane fields, surface irrigation is the common irrigation system and a combination of irrigated and nonirrigated pixels is often unavoidable. Figure 6(a,b) show the time series of the CWSI and K s of the sugarcane crop at the pixel level Landsat-8 satellite pass days based on sugarcane canopy temperature ratio methods in well-watered and water-stressed irrigated sugarcane fields (Bausch, Trout, and Buchleiter 2011;DeJonge et al. 2015;Veysi, Naseri, and Hamzeh 2020). Figure 6(a) illustrates that the 25th to 75th percentile range of CWSI fluctuates between values of 0.15 to 0.45. ...
The accurate estimation of evapotranspiration is crucial for enhancing crop water productivity and effectively managing water resources. This research offers a novel method that integrates satellite data and crop coefficients to calculate ETc and ETa on a daily basis and overcome the limitation of low temporal frequency of non-commercial satellite data. The study was carried out in the southern part of Khuzestan Province, Iran, on sugarcane crops in the Amirkabir Agro-industries area. The method involves obtaining Landsat-8 data with an 8-day temporal resolution, which was used to estimate Land Surface Temperature (LST) using a Single-Channel Algorithm. The estimated LST was then validated with in-situ canopy temperature measurements and used to predict the crop stress coefficient (Ks) based on its relationship with the crop water stress index (CWSI). The crop coefficient (Kc) was obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm, and both Ks and Kc were utilized to calculate daily ETa by multiplying by the daily reference evapotranspiration (ET0) obtained from local meteorological data. The results indicated that the crop coefficients of sugarcane in the initial and mid-stages were 12% and 18% higher, respectively, compared to the FAO56 guideline. The aggregated decadal and monthly ETa showed good agreement with the WaPOR datasets, with an RMSE of 8.7 and 1.93 mm, respectively. This approach offers a potential solution to the challenge of obtaining remote sensing data with a higher temporal frequency.
... This unique properties of TIR have made TIR imagery an increasingly used technique for different applications where the temperature of objects is crucial for detecting faults, diagnosing their state and provenance, and assessing their severity. Examples of these applications range from evaluating building energy efficiency [1], preserving heritage elements [2], diagnosing medical conditions [3] and sport injuries [4] in both humans and animals [5], determining the dryness of crops [6], and monitoring vegetation for fire prevention [7]. ...
The Multisensor Multiresolution Technique (MMT) is applied to unmixed thermal images from ASTER (90 m), using 30 m resolution images from Landsat 8-9 reflective channels. The technique allows for the retrieval of thermal radiance values of the features identified in the high-resolution reflective images and the generation of a high-resolution radiance image. Different alternatives of application of MMT are evaluated in order to determine the optimal methodology design: performance of the Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-means classification algorithms, with different initiation numbers of clusters, and computation of contributions of each cluster using moving windows with different sizes and with and without weight coefficients. Results show the K-means classification algorithm with five clusters, without matrix weighting, and utilizing a 5 × 5 pixel window for synthetic high-resolution image reconstruction. This approach obtained a maximum R² of 0.846 and an average R² of 0.815 across all cases, calculated through the validation of the synthetic high-resolution TIR image generated against a real Landsat 8-9 TIR image from the same area, same date, and co-registered. These values imply a 0.89% improvement regarding the second-best methodology design (K-means with five starting clusters with 7 × 7 moving window) and a 410.25% improvement regarding the worst alternative (K-means with nine initial clusters, weighting, and 3 × 3 moving window).
... Кроме того, уникальность изъятых образцов без привязки к физиологически релевантным условиям эксперимента и морфо-анатомическим дескрипторам, как правило, делает невозможным корреляционный мета-анализ данных, в контексте реального состояния растения в момент эксперимента или же выборки образцов. Поэтому, несмотря на обширные приложения современного статистического (Honghong, Zhengu, 1998), «data mining»-ового (Pradhan, Mezaal, 2018) и хемометрического (в том числе в анализа результатов агрохимических приложений лазерно-искровой эмиссионной спектроскопии (Erler et al., 2020)) программного обеспечения в лазерных исследования агрофизических систем или отдельных серий образцов растений на разных пространственных масштабах (от клеток до ландшафтов и экосистем, наблюдаемых методом мультиспектральной аэрофотосъѐмки и же с использованием спутниковых и суборбитальных систем регистрации, в том числе -для анализа корневых патологий либо недостаточной ирригации корней Espinoza et al., 2018;Veysi et al., 2020), корректный статистический корреляционный анализ с необходимым пространственным разрешением для корневых структур обычно, увы, не реализуется. Как следствие этого, и кластеризация корней по размерам и спектрозональным геометрически-морфометрическим показателям, выявляемым методами машинного обучения без учителя, на таких данных не осуществляется. ...
In this article, we propose to use real-time correlation-spectral analysis systems (developed in FEB RAS) to identify morphometric classes of plant roots and consider this solution as a full-fledged alternative to outdated analog laser/optical diffractometry and laser Fourier technology with the calculation of Lendaris parameters/indices and the construction of projection transformants. The proposed technique can be implemented using a PC, laptop or tablet with older operating systems. Simplicity and low cost make this technique accessible to users from the agricultural industry who are not specialists in the field of optics or laser physics. In this article, we tried, in clear language, without resorting to formulas, to explain to botanists and agricultural specialists what, in essence, the method under consideration is, what advantages it brings to practicing botanists and what information can be extracted using it (not excluding the description of artifacts and errors, which can lead to disappointment in the method, which is based on an error rather than the incorrectness of the proposed method). The second part of the article provides an atlas catalog of Fourier spectra and microphotographic images of corresponding botanical forms and structures.
... Los índices de vegetación fueron determinados en la calculadora Raster del software Quantum Gis (Qgis, 2016) para generar los mapas del área de estudio en donde se mostraron los valores de los índices de vegetación de las parcelas cañeras (Veysi et al., 2020). A continuación, se presentan los algoritmos correspondientes de los IVs en el Cuadro 1. Muestreo de calidad de jugos de caña de azúcar. ...
Resumen. El pronóstico de la calidad del jugo se hace a través de métodos destructivos y de laboratorio. Por ello, es necesario probar alternativas no destructivas, rápidas y económicas. En este sentido, la teledetección es una fuente potencial de datos para monitorear cultivos, proporcionando información espacial y temporal. El objetivo del trabajo fue estimar y mapear diferentes índices de vegetación para evaluar el estado de salud y pronosticar los parámetros de grados brix y sacarosa del jugo de caña de azúcar a través de Sentinel 2 y modelos de machine learning durante la zafra 2021-2022 en la Chontalpa, Tabasco. Las imágenes del Sentinel 2, se descargaron desde la web de Copernicus. Se ubicaron temporal y espacialmente los meses de enero a marzo de 2022, en un área de 80 ha de superficie. Las imágenes, se procesaron en el programa QGIs y se calcularon los índices de NDVI, SAVI, EVI, GWDI y GNDVI. En campo, se midió mensualmente en cada parcela parámetros de calidad de jugos de grados brix y sacarosa. Posteriormente, se realizó la estimación de parámetros de calidad de jugos de caña de azúcar con modelos de machine learning en Rstudio. Con los mapas generados de los índices de vegetación fue posible detectar variaciones temporales y espaciales en cuanto al estado de salud del cultivo. Se encontraron fuertes correlaciones (>0.70) entre los diferentes índices de vegetación. Asimismo, se reporta fuertes correlaciones entre los parámetros de calidad de jugos y NDVI, SAVI y GNDVI. Nuestros hallazgos demuestran que los índices de vegetación permiten monitorear el cultivo de caña de azúcar y pronosticar tres meses antes los parámetros de calidad de jugo.
Summary. Juice quality forecasting is done through destructive and laboratory methods. Therefore, it is necessary to test non-destructive, fast and economical alternatives. In this sense, remote sensing is a potential source of data to monitor crops, providing spatial and temporal information. The objective of the work was to estimate and map different vegetation indices to evaluate the health status and predict the brix and sucrose parameters of sugarcane juice through Sentinel 2 and machine learning models during the 2021-2022 harvest in Chontalpa, Tabasco. The Sentinel 2 images were downloaded from the Copernicus website. The months from January to March 2022 were located temporally and spatially, in an area of 80 ha. The images were processed in the QGIs program and the NDVI, SAVI, EVI, GWDI and GNDVI indices were calculated. In the field, juice quality parameters of brix and sucrose were measured monthly in each plot. Subsequently, the estimation of quality parameters of sugarcane juices was carried out with machine learning models in Rstudio. With the maps generated from the vegetation indices it was possible to detect temporal and spatial variations in the health status of the crop. Strong correlations (>0.70) were found between the different vegetation indices. Likewise, strong correlations are reported between juice quality parameters and NDVI, SAVI and GNDVI. Our findings demonstrate that vegetation indices allow monitoring sugarcane cultivation and predicting juice quality parameters three months in advance.
... Los índices de vegetación fueron determinados en la calculadora Raster del software Quantum Gis (Qgis, 2016) para generar los mapas del área de estudio en donde se mostraron los valores de los índices de vegetación de las parcelas cañeras (Veysi et al., 2020). A continuación, se presentan los algoritmos correspondientes de los IVs en el Cuadro 1. Muestreo de calidad de jugos de caña de azúcar. ...
Introducción. La teledetección es una fuente potencial de datos para monitorear cultivos, proporcionando información espacial y temporal. Los índices de vegetación son algoritmos que se basan en un arreglo de operaciones matemáticas entre las bandas espectrales, principalmente, las involucradas en la absorción y reflectividad de la luz solar que tiene influencia en el comportamiento espectral de cultivos. Objetivo. Estimar y mapear diferentes índices de vegetación para evaluar el estado de salud en el cultivo de caña de azúcar durante la zafra 2021-2022 en la Chontalpa, Tabasco, derivados del Sentinel 2. Materiales y Métodos. Las imágenes satelitales del Sentinel 2 del área de estudio, se descargaron desde la web de Copernicus. El criterio de búsqueda fue seleccionar aquellas imágenes con un porcentaje de nubes <20%. Se ubicaron temporal y espacialmente los meses de abril 2021 hasta abril 2022, en un área de 80 ha de superficie. Las imágenes, se procesaron en el programa QGIs. El procesamiento consistió en una corrección atmosférica y radiométrica a las imágenes de satélites para evitar ruido o efectos de sombra en las imágenes. En la calculadora ráster, se calcularon los índices de NDVI, SAVI, EVI, GWDI y GNDVI. En campo, En campo, se midió mensualmente en cada parcela parámetros de calidad de jugos de grados Brix°, sacarosa, pureza, fibra y azucares reductores. Se corrieron correlaciones entre los diferentes índices de vegetación y parámetros de calidad de jugos. Resultados y discusión. Con los mapas generados de los índices de vegetación fue posible detectar variaciones temporales y espaciales en cuanto al estado de salud del cultivo. Se encontraron fuertes correlaciones (>80) entre los diferentes índices de vegetación. Asimismo, se reporta fuertes correlaciones entre los parámetros de calidad de jugos y NDVI, SAVI y GNDVI. .Se encontró que los IVs permiten detectar variaciones temporales del proceso de maduración de caña de azúcar. Conclusiones. Nuestros hallazgos demuestran que los índices de vegetación permiten monitorear el cultivo de caña de azúcar.
ABSTRACT. Introduction. Remote sensing is a potential source of data to monitor crops, providing spatial and temporal information. Vegetation indices are algorithms that are based on an arrangement of mathematical operations between spectral bands, mainly those involved in the absorption and reflectivity of sunlight that influences the spectral behavior of crops. Aim. Estimate and map different vegetation indices to evaluate the health status of sugarcane cultivation during the 2021-2022 harvest in Chontalpa, Tabasco, derived from Sentinel 2. Materials and Methods. Sentinel 2 satellite images of the study area were downloaded from the Copernicus website. The search criterion was to select those images with a percentage of clouds <20%. The months of April 2021 to April 2022 were located temporally and spatially, in an area of 80 ha. The images were processed in the QGIs program. The processing consisted of an atmospheric and radiometric correction to the satellite images to avoid noise or shadow effects in the images. In the raster calculator, the indices of NDVI, SAVI, EVI, GWDI and GNDVI were calculated. In the field, In the field, juice quality parameters of degrees Brix°, sucrose, purity, fiber and reducing sugars were measured monthly in each plot. Correlations were run between the different vegetation indices and juice quality parameters. Results and discussion. With the maps generated from the vegetation indices it was possible to detect temporal and spatial variations in the health status of the crop. Strong correlations (>80) were found between the different vegetation indices. Likewise, strong correlations are reported between juice quality parameters and NDVI, SAVI and GNDVI. .It was found that IVs allow detecting temporal variations in the sugarcane maturation process. Conclusions. Our findings demonstrate that vegetation indices allow monitoring sugarcane cultivation.
... Thus, they influence the spectral behavior of the leaf and become a great indicator of photosynthetic capacity (Croft et al. 2017), besides presenting a high correlation with nitrogen content (Schlemmer et al. 2013;Cheng et al. 2018). Other studies report the importance of the correlation among the spectral indices in the wavelengths of visible, red-edge and NIR, for the mapping and monitoring of the stress variations of the sugarcane culture, such as: productivity Sanches et al. 2018), water stress (Picoli et al. 2019) and soil moisture (Veysi et al. 2020). ...
Spectral indices are important tools for monitoring nitrogen levels in plants. This study assessed the potential application of spectral indices in monitoring the nitrogen nutritional status in sugarcane crops. Seven sugarcane varieties and three production environments were studied, with the SP813250 variety cultivated in all three experimental areas. Ammonium nitrate was used as the nitrogen source at doses of 0, 50, 100, and 150 kg ha−1. Leaf samples for hyperspectral analyses and Leaf Nitrogen Content (LNC) were collected during the maximum vegetative development phase of the crop. Based on reflectance data, 20 spectral indices were calculated and then subjected to simple linear regression (SLR) testing for LNC prediction. In the validation of prediction results, the coefficient of determination (R2) values, root mean square error (RMSE), and predicted relative error were used as reference. All models were calibrated using the 2012/13 crop data and validated using the 2013/14 crop data. Indices involving the 530–570 nm, 680–750 nm, and 750–1300 nm spectral ranges showed the best performance in model validation. Across all varieties and production environments, the most acceptable indices were: BNi (R2 > 0.66, RMSE < 3.50 g kg−1), GNDVI (R2 > 0.65, RMSE < 3.67 g kg−1), NDRE (R2 > 0.68, RMSE < 3.18 g kg−1), RI-1db (R2 > 0.69, RMSE < 3.66 g kg−1), and VOGa (R2 > 0.69, RMSE < 3.44 g kg−1). The environment significantly influenced the predictive potential for the SP813250 variety, with some cases showing up to a 50% reduction in R2.
... Remote sensing sensors in the visible-near-infrared band (0.4-0.2.5 µm) receive reflection information primarily from the surface, in response to solar short-wave radiation. It is in this spectral interval that different soil moistures directly lead to different spectral reflection characteristics [16]. Bowers studied soil moisture and soil reflectance in the short-wave range under bare soil conditions and showed that soil reflectance decreases with increasing soil moisture, while absorption increases with increasing soil moisture. ...
Soil moisture plays an important role in hydrology, climate, agriculture, and ecology, and remote sensing is one of the most important tools for estimating the soil moisture over large areas. Soil moisture, which is calculated by remote sensing inversion, is affected by the uneven distribution of vegetation and therefore the results cannot accurately reflect the spatial distribution of the soil moisture in the study area. This study analyzes the soil moisture of different vegetation covers in the Wushen Banner of Inner Mongolia, recorded in 2016, and using Landsat and MODIS images fused with multispectral bands. Firstly, we compared and analyzed the ability of the visible optical and short-wave infrared drought index (VSDI), the normalized differential infrared index (NDII), and the short-wave infrared water stress index (SIWSI) in monitoring the soil moisture in different vegetation cover soils. Secondly, we used the stepwise multiple regression analysis method in order to correlate the multispectral fusion bands with the field-measured soil water content and established a soil moisture inversion model based on the multispectral fusion bands. As the results show, there was a strong correlation between the established model and the measured soil water content of the different vegetation cover soils: in the bare soil, R2 was 0.86; in the partially vegetated cover soil, R2 was 0.84; and in the highly vegetated cover soil, R2 was 0.87. This shows that the established model could better reflect the actual condition of the surface soil moisture in the different vegetation covers.
... Nevertheless, constructing lysimeters and meteorological stations is not possible everywhere and can be costly (Geshnigani et al., 2021;Okechukwu, 2020). Therefore, evapotranspiration is measured on a point basis and in a limited number; since most studies are performed on a regional scale, it is necessary to generalize this information from measuring stations to the whole region (Galehban et al., 2021;Veysi et al., 2020). There are several methods for this purpose, including classical statistics, arithmetic means and regression. ...
This study aimed evaluating the ability of the FAO's WaPOR Product (FWP) to estimate reference evapotranspiration (RET) based on lysimetric data and RET equations and compare its accuracy with geostatistical methods in the Lake Urmia basin. Lysimetric RET was collected at two stations for 4 years. RET equations were then evaluated using the lysimetric data; the best equation was determined. The RET data obtained from FWP were evaluated at daily, monthly and annual scales using the results of the selected equation for the years 2010–2020. Finally, the accuracy of FWP in the spatial estimation of RET was compared with geostatistical methods. The results show that the FAO Penman–Monteith (FPM) equation was more accurate at both stations. Therefore, the results of the FPM were used to evaluate the reference evapotranspiration of FWP. The average nRMSE of the FWP to daily, monthly and annual data was 31, 25 and 16%, respectively. In general, 15% overestimation was observed in the FWP. Comparison of the FWP with geostatistical methods showed that the highest and lowest accuracy was observed in the experimental kriging method and FWP with an nRMSE value of 4.6 and 18%, respectively.
... Remote sensing-based estimation of soil moisture or salinity in irrigation areas has been carried out around the world using various modeling techniques (Casterad et al., 2018;Dari et al., 2020;González-Zamora et al., 2016;Guo et al., 2018;Hajj et al., 2017;Han et al., 2019;Veysi et al., 2020;Welle and Mauter, 2017). The optimal spectral indexes from procedural selection has become a vital step in the construction of inversion models. ...
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.
... The index for canopy water content (e.g., NDWI) can be used to quantify root zone soil moisture [44]. However, similar to the NDVI, the NDWI can also be affected by background interferences. ...
In this study, we aim to develop an inexpensive site-specific irrigation advisory service for resolving disadvantages related to using immobile soil moisture sensors and to the differences in irrigation needs of different tea plantations affected by variabilities in cultivars, plant ages, soil heterogeneity, and management practices. In the paper, we present methodologies to retrieve two biophysical variables, surface soil water content and canopy water content of tea trees from Sentinel-2 (S2) (European Space Agency, Paris, France) images and consider their association with crop water availability status to be used for making decisions to send an alert level. Precipitation records are used as auxiliary information to assist in determining or modifying the alert level. Once the site-specific alert level for each target plantation is determined, it is sent to the corresponding farmer through text messaging. All the processes that make up the service, from downloading an S2 image from the web to alert level text messaging, are automated and can be completed before 7:30 a.m. the next day after an S2 image was taken. Therefore, the service is operated cyclically, and corresponds to the five-day revisit period of S2, but one day behind the S2 image acquisition date. However, it should be noted that the amount of irrigation water required for each site-specific plantation has not yet been estimated because of the complexities involved. Instead, a single irrigation rate (300 t ha−1) per irrigation event is recommended. The service is now available to over 20 tea plantations in the Mingjian Township, the largest tea producing region in Taiwan, free of charge since September 2020. This operational application is expected to save expenditures on buying irrigation water and induce deeper root systems by decreasing the frequency of insufficient irrigation commonly employed by local farmers.
The Crop Water Stress Index (CWSI) is a widely used method for quantifying crop water status and predicting yield. However, its evaluation across different irrigation methods and its stage‐specific response to crop yield is rarely evaluated. In this study, controlled field experiments were conducted on winter wheat using drip irrigation (DI) and flood irrigation (FI) during the 2021–2022 and 2022–2023 seasons in western Uttar Pradesh, India. The irrigation treatments included 50% MAD (maximum allowable depletion) (DI), 55% MAD (DI), 60% MAD (DI), 50% MAD (FI), local farmer's field replication (FI), rain‐fed, and well‐watered treatment (DI). The derived mean CWSI values for the irrigation treatments ranged from 0.03 to 0.66 in season 1 and 0.06 to 0.57 in season 2 across treatments. The seasonal mean CWSI for 50% MAD (DI) was 0.12 (season 1) and 0.11 (season 2), while 50% MAD (FI) yielded higher mean CWSI values of 0.29 (season 1) and 0.22 (season 2). The 50% MAD (DI) treatment produced the highest grain yield and water use efficiency in both seasons. A comprehensive analysis of stage‐specific CWSI values and grain yields revealed that grain yield was more sensitive to post‐heading CWSI as compared to pre‐heading CWSI values. Among the growth stages, CWSI values during the flowering stage were the most critical for predicting wheat yield. The study recommends that the CWSI values in the flowering and post‐heading stages are more relevant in predicting wheat yield accurately as compared to the pre‐heading and seasonal mean CWSI.
This study presents a comprehensive framework for analyzing water productivity products provided by the FAO Water Productivity Open-access portal (WaPOR), focusing on various crops cultivated in both rainfed and irrigated areas within a semi-arid basin in Iran. Two indices, namely Gross Water Productivity (GWP) and Net Water Productivity (NWP), were introduced to quantify water productivity across crop fields. However, these indices may mislead decision-makers, because they aggregate water productivity for all crops and exacerbate the challenges posed by water scarcity. Therefore, mapping crop types seems necessary to enhance the interpretation of these indices and develop a dimensionless index for comparing different crops. The results demonstrated a fundamental change when comparing dimensionless water productivity with GWP and NWP products. Surprisingly, some pixels initially exhibiting high water productivity ranked as low water-productive pixels based on the derived dimensionless index, and vice versa. Based on dimensionless indicators, rainfed crops, particularly rainfed cereals, ranked as the most water-productive crops. The areas with dimensionless values below 0.5 warrant heightened attention to curtail non-beneficial water consumption and elevate water productivity. This research emphasizes the significance of mapping cultivation types as supplementary layers to facilitate precise, data-driven decision-making and enable comparisons of crops based on dimensionless water productivity indices.
As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0.
Estimating of soil moisture is very important for water resources studies and irrigation scheduling, and for this
purpose the use of satellite imagery is very helpful. The present research was conducted to evaluate the
capability of WCM model based on the use of radar and optical data of Sentinel 1 and 2 to estimate soil
moisture and irrigation scheduling in sugarcane cultivation lands located in the southwest of Iran. For this
purpose, the soil moisture was measured from May 18 to September 27, 2020, simultaneously with 7 passes of
Sentinel 1 and 2 satellites at 337 ground control points located in 18 sugarcane fields using a TDR350
moisture meter. After performing the necessary processing, the soil moisture was estimated using the WCM
model using different polarizations of radar images as well as different plant indices and the results were
evaluated using field observations. The obtained results show that the WCM model using LAI index and VV
polarization is more accurate in estimating soil moisture. Also, the accuracy of the results for the case where
the desired model is calculated for each image separately is higher than when a general model is developed for
the entire sugarcane growth period. The RMSE of this model for a single image using the LAI index is in the
range of [1-13 %] and its correlation coefficient is [0.98-0.34]. If a model is developed for the entire period,
the RMSE is between [17 -22 %] and its correlation coefficient is [0/1-0/2].
Satellites, drones, and on-ground sensors are essential in providing data for the digital ecosystem for agriculture innovation. Usually, all three modes of data collection are executed in isolation; however, they should work in coordination to leverage the strength of remote sensing. For example, making a classification model using remote sensing and ground data is mandatory. The current approach is to collect data through mobile phones by field officers at a specific time of crop harvesting. However, a more efficient method would be using an on-ground sensor to provide ground truth data continuously. Frequent training of the machine learning model will produce a time-sensitive digital crop signature on the ground. It will significantly impact how we do crop classification and yield estimation. This chapter evaluates and proposes a reference architecture for the orchestration platform that provides a coordination mechanism for the data collected from on-ground sensors, drones, and remote sensing satellites. The benefits of agriculture are examined through several use cases in this chapter.KeywordsAgriculture innovationCrop classificationDroneRemote sensing dataSatelliteSensorOrchestration platform
Soil moisture is one of the key parameters in water resources studies and irrigation remote planning. Measuring soil moisture on a large scale is costly and very time consuming. Traditional methods of measuring soil moisture at farm level cannot show the spatial changes of moisture in the best way. Various new methods have been developed to use satellite data to model soil moisture based on thermal images. This study was conducted in 2020 with the aim of investigating the ability of thermal satellite imagery to estimate soil moisture and to plan irrigation rounds of lands in sugarcane industry of Amirkabir located in the south of Khuzestan province. For this purpose, during growing season of sugarcane, soil moisture content was calculated for 9 crossings Landsat 8 satellite and evaluated using 180 ground control points, and also daily irrigation data of 32 farms (25-hectare) were recorded during the study period. The results showed that the accuracy of the model is suitable for estimating soil moisture with the measured values at the farm level. The mean square root of normalized error (NRMSE) was 12.9% and the coefficient of determination (R²) was 0.82. Also, the results of soil moisture in irrigation management of sugarcane fields showed that thermal trapezoidal model is effective due to using thermal bands to environmental factors such as relative humidity percentage, average air temperature, pest (leaf dryness) and plant temperature, and somewhat in June and July causes errors in irrigation planning of sugarcane fields. The mean square root of normalized error (NRMSE) during soil water stress was 24.32%, during irrigation time was 22.20%, at average humidity was 11.7%, during high humidity was 13.20% and during irrigation was 8.86%. Consequently, the accuracy of thermal trapezoidal model for planning irrigation of farms in estimating soil water stress and field irrigation time in some periods of growing season is moderate and for fields having sufficient soil moisture is well. Cite this article: Mohammadi Moalezade, J., Hamze, S., Naseri, A. A. (2022) Estimating soil surface moisture content and investigating irrigation schedule of sugarcane fields using thermal trapezoidal model, http://doi.org/10.22059/ijswr.
Appropriate determination of actual evapotranspiration (ET a ) is crucial to improve crop water productivity and optimizing water resource consumption. Satellite data enables us to calculate ET a for a large spatial extent with higher granularity, but the temporal frequency of non-commercial satellite data is often a limitation. This research proposes a method that combines crop coefficients with satellite data to fill temporal data gaps and calculate ET a on a daily basis. The study was conducted on sugarcane crops in the Amirkabir Agro-industries area in the southern part of Khuzestan Province, southwestern Iran. First, Landsat-8 data with the 8- day temporal resolution is acquired to estimate Land Surface Temperature (LST) using Single-Channel Algorithm. The estimated LST is validated with the in-situ canopy temperature measurement via Infrared Thermometer (IRT). Then, the validated LST is used to predict the crop stress coefficient (K s ) based on its relationship with the crop water stress index (CWSI). The crop coefficient (K c ) is obtained from the Surface Energy Balance Algorithm for Land (SEBAL) algorithm. The predicted Ks and Kc with the 8-day temporal resolution are assumed to be constant during the eight days and are utilized to calculate daily ET a by multiplying by the daily reference evapotranspiration (ET 0 ) obtained from local meteorological data. The calculated K s based on the LST result showed that nRMSE ranged from 0.03 to 0.07 from April to September. The results indicate that the crop coefficients of sugarcane in the initial and mid-stage are 12% and 18%, respectively, higher than the proposed figures by the FAO56 guideline. The aggregated decadal and monthly ET a have shown remarkable similarity with the WaPOR datasets, represented by an RMSE of 8.7 and 1.93 mm, respectively. We think this naval approach can significantly overcome the challenge of remote sensing data availability with the desired higher temporal resolution.
Soil moisture and salinity are two crucial coastal saline soil variables, which influence the soil quality and agricultural productivity in the reclaimed coastal region. Accurately characterizing the spatial variability of these soil parameters is critical for the rational development and utilization of tideland resources. In the present study, the spatial variability of soil moisture and salinity in the reclaimed area of Hangzhou gulf, Shangyu City, Zhejiang Province, China, was detected using the data acquired from radar image and the proximal sensor EM38. Soil moisture closely correlates radar scattering coefficient, and a simplified inversion model was built based on a backscattering coefficient extracted from multi-polarization data of ALOS/PALSAR and in situ soil moisture measured by a time domain reflectometer to detect soil moisture variations. The result indicated a higher accuracy of soil moisture inversion by the HH polarization mode than those by the HV mode. Soil salinity is reflected by soil apparent electrical conductivity (ECa). Further, ECa can be rapidly detected by EM38 equipment in situ linked with GPS for characterizing the spatial variability of soil salinity. Based on the strong spatial variability and interactions of soil moisture and salinity, a cokriging interpolation method with auxiliary variable of backscattering coefficient was adopted to map the spatial variability of ECa. When compared with a map of ECa interpolated by the ordinary kriging method, detail was revealed and the accuracy was increased by 15.3%. The results conclude that the integrating active remote sensing and proximal sensors EM38 are effective and acceptable approaches for rapidly and accurately detecting soil moisture and salinity variability in coastal areas, especially in the subtropical coastal zones of China with frequent heavy cloud cover.
This study outlines a method for the estimation of regional patterns of surface moisture availability (M(sub 0)) and fractional vegetation (Fr) in the presence of spatially variable vegetation cover. The method requires relating variations in satellite-derived (NOAA, Advanced Very High Resolution Radiometer (AVHRR)) surface radiant temperature to a vegetation index (computed from satellite visible and near-infrared data) while coupling this association to an inverse modeling scheme. More than merely furnishing surface soil moisture values, the method constitues a new conceptual and practical approach for combining thermal infrared and vegetation index measurements for incorporating the derived values of M(sub 0) into hydrologic and atmospheric prediction models. Application of the technique is demonstrated for a region in and around the city of Newcastle upon Tyne situated in the northeast of England. A regional estimate of M(sub 0) is derived and is probabbly good for fractional vegetation cover up to 80% before errors in the estimated soil water content become unacceptably large. Moreover, a normalization scheme is suggested from which a nomogram, `universal triangle,` is constructed and is seen to fit the observed data well. The universal triangle also simplifies the inclusion of remotely derived M(sub 0) in hydrology and meteorological models and is perhaps a practicable step toward integrating derived data from satellite measurements in weather forecasting.
A historical climatology of continuous satellite-derived global land surface soil moisture is being developed. The data consist of surface soil moisture retrievals derived from all available historical and active satellite microwave sensors, including Nimbus-7 Scanning Multichannel Microwave Radiometer, Defense Meteorological Satellites Program Special Sensor Microwave Imager, Tropical Rainfall Measuring Mission Microwave Imager, and Aqua Advanced Microwave Scanning Radiometer for EOS, and span the period from November 1978 through the end of 2007. This new data set is a global product and is consistent in its retrieval approach for the entire period of data record. The moisture retrievals are made with a radiative transfer-based land parameter retrieval model. The various sensors have different technical specifications, including primary wavelength, spatial resolution, and temporal frequency of coverage. These sensor specifications and their effect on the data retrievals are discussed. The model is described in detail, and the quality of the data with respect to the different sensors is discussed as well. Examples of the different sensor retrievals illustrating global patterns are presented. Additional validation studies were performed with large-scale observational soil moisture data sets and are also presented. The data will be made available for use by the general science community.
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.
We review various recent approaches to making digital soil maps based on geographic information systems (GIS) data layers, note some commonalities and propose a generic framework for the future. We discuss the various methods that have been, or could be, used for fitting quantitative relationships between soil properties or classes and their ‘environment’. These include generalised linear models, classification and regression trees, neural networks, fuzzy systems and geostatistics. We also review the data layers that have been, or could be, used to describe the ‘environment’. Terrain attributes derived from digital elevation models, and spectral reflectance bands from satellite imagery, have been the most commonly used, but there is a large potential for new data layers. The generic framework, which we call the scorpan-SSPFe (soil spatial prediction function with spatially autocorrelated errors) method, is particularly relevant for those places where soil resource information is limited. It is based on the seven predictive scorpan factors, a generalisation of Jenny’s five factors, namely: (1) s: soil, other or previously measured attributes of the soil at a point; (2) c: climate, climatic properties of the environment at a point; (3) o: organisms, including land cover and natural vegetation; (4) r: topography
There is currently a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science and global change. Spectral models and indices are being developed to improve vegetation sensitivity by accounting for atmosphere and soil effects. The soil-adjusted vegetation index (SAVI) was developed to minimize soil influences on canopy spectra by incorporating a soil adjustment factor L into the denominator of the normalized difference vegetation index (NDVI) equation. For optimal adjustment of the soil effect, however, the L factor should vary inversely with the amount of vegetation present. A modified SAVI (MSAVI) that replaces the constant L in the SAVI equation with a variable L function is presented in this article. The L function may be derived by induction or by using the product of the NDVI and weighted difference vegetation index (WDVI). Results based on ground and aircraft-measured cotton canopies are presented. The MSAVI is shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a “vegetation signal” to “soil noise” ratio.
The normalized difference vegetation index (NDVI) has been widely used for remote sensing of vegetation for many years. This index uses radiances or reflectances from a red channel around 0.66 μm and a near-IR channel around 0.86 μm. The red channel is located in the strong chlorophyll absorption region, while the near-IR channel is located in the high reflectance plateau of vegetation canopies. The two channels sense very different depths through vegetation canopies. In this article, another index, namely, the normalized difference water index (NDWI), is proposed for remote sensing of vegetation liquid water from space. NDWI is defined as , where ϱ represents the radiance in reflectance units. Both the 0.86-μm and the 1.24-μm channels are located in the high reflectance plateau of vegetation canopies. They sense similar depths through vegetation canopies. Absorption by vegetation liquid water near 0.86 μm is negligible. Weak liquid absorption at 1.24 μm is present. Canopy scattering enhances the water absorption. As a result, NDWI is sensitive to changes in liquid water content of vegetation canopies. Atmospheric aerosol scattering effects in the 0.86–1.24 μm region are weak. NDWI is less sensitive to atmospheric effects than NDVI. NDWI does not remove completely the background soil reflectance effects, similar to NDVI. Because the information about vegetation canopies contained in the 1.24-μm channel is very different from that contained in the red channel near 0.66 μm, NDWI should be considered as an independent vegetation index. It is complementary to, not a substitute for NDVI. Laboratory-measured reflectance spectra of stacked green leaves, and spectral imaging data acquired with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) over Jasper Ridge in California and the High Plains in northern Colorado, are used to demonstrate the usefulness of NDWI. Comparisons between NDWI and NDVI images are also given.
Spatially distributed soil moisture profiles are required for watershed applications such as drought and flood prediction, crop irrigation scheduling, pest management, and determining mobility with lightweight vehicles. Satellite-based soil moisture can be obtained from passive microwave, active microwave, and optical sensors, although the coarse spatial resolution of passive microwave and the inability to obtain vertically resolved information from optical sensors limit their usefulness for watershed-scale applications. Active microwave sensors such as synthetic aperture radar (SAR) currently represent the best approach for obtaining spatially distributed surface soil moisture at scales of 10-100 m for watersheds ranging from 1 000 to 25 000 km2. Although SAR provides surface soil moisture, the applications listed above require vertically resolved soil moisture profiles. To obtain distributed soil moisture profiles, a combined approach of calibration and data assimilation in soil vegetation atmosphere transfer (SVAT) models based on recent advances in soil physics is the most promising avenue of research. This review summarizes the state of the science using current satellite-based sensors to determine watershed-scale surface soil moisture distribution and the state of combining SVAT models with data assimilation and calibration approaches for the estimation of profile soil moisture. The basic conclusion of this review is that currently orbiting SAR sensors combined with available SVAT models could provide distributed profile soil moisture information with known accuracy at the watershed scale. The priority areas for future research should include image-based approaches for mapping surface roughness, determination of soil moisture in densely vegetated sites, active and passive microwave data fusion, and joint calibration and data assimilation approaches for a combined remote sensing - modeling system. For validation, a worldwide in situ soil moisture monitoring program should be implemented. Finally, to realize the full potential of satellite-based soil moisture estimation for watershed applications, it will be necessary to continue sensor development, improve image availability and timely delivery, and reduce image cost.
The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural crop characteristics at different growth stages. Research was conducted on four crops (corn, soybean, wheat, and canola) over eight years grown under different tillage practices and nitrogen management practices that varied rate and timing. Six different vegetation indices were found most useful, depending on crop phenology and management practices: (a) simple ratio for biomass, (b) NDVI for intercepted PAR, (c) SAVI for early stages of LAI, (d) EVI for later stages of LAI, (e) CIgreen for leaf chlorophyll, (f) NPCI for chlorophyll during later stages, and (g) PSRI to quantify plant senescence. There were differences among varieties of corn and soybean for the vegetation indices during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural crop characteristics.
Precision agriculture comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems. Adapting production inputs site-specifically within a field and individually for each animal allows better use of resources to maintain the quality of the environment while improving the sustainability of the food supply. Precision agriculture provides a means to monitor the food production chain and manage both the quantity and quality of agricultural produce.
The proper assessment of evapotranspiration and soil moisture content are fundamental in food security research, land management, pollution detection, nutrient flows, (wild-) fire detection, (desert) locust, carbon balance as well as hydrological modelling; etc. This paper takes an extensive, though not exhaustive sample of international scientific literature to discuss different approaches to estimate land surface and ecosystem related evapotranspiration and soil moisture content. This review presents:
(i)a summary of the generally accepted cohesion theory of plant water uptake and transport including a shortlist of meteorological and plant factors influencing plant transpiration;(ii)a summary on evapotranspiration assessment at different scales of observation (sap-flow, porometer, lysimeter, field and catchment water balance, Bowen ratio, scintillometer, eddy correlation, Penman-Monteith and related approaches);(iii)a summary on data assimilation schemes conceived to estimate evapotranspiration using optical and thermal remote sensing; and(iv)for soil moisture content, a summary on soil moisture retrieval techniques at different spatial and temporal scales is presented.
Concluding remarks on the best available approaches to assess evapotranspiration and soil moisture content with and emphasis on remote sensing data assimilation, are provided.
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.
The unique thermal and dielectric properties of water afford two possibilities for remotely sensing the moisture content in the surface layer of the soil. Observations of the diurnal range of surface temperature, the microwave brightness temperature (emissivity) and radar backscatter of the soil have shown correlations of up to 0.9 with the moisture in the surface layer (about 5 cm thick). The microwave techniques appear to maintain their sensitivity to moisture variations in the presence of a crop canopy. Observations of microwave brightness temperature from satellite platforms have qualitatively confirmed this sensitivity for a wide range of conditions.
This study was conducted to develop the relationship between canopy-air temperature difference and vapour pressure deficit for no stress condition of wheat crop (baseline equations), which was used to quantify crop water stress index (CWSI) to schedule irrigation in winter wheat crop (Triticum aestivum L.). The randomized block design (RBD) was used to design the experimental layout with five levels of irrigation treatments based on the percentage depletion of available soil water (ASW) in the root zone. The maximum allowable depletion (MAD) of the available soil water (ASW) of 10, 40 and 60 per cent, fully wetted (no stress) and no irrigation (fully stressed) were maintained in the crop experiments. The lower (non-stressed) and upper (fully stressed) baselines were determined empirically from the canopy and ambient air temperature data obtained using infrared thermometry and vapour pressure deficit (VPD) under fully watered and maximum water stress crop, respectively. The canopy-air temperature difference and VPD resulted linear relationships and the slope (m) and intercept (c) for lower baseline of pre-heading and post-heading stages of wheat crop were found m = -1.7466, c = -1.2646 and m = -1.1141, c = -2.0827, respectively. The CWSI was determined by using the developed empirical equations for three irrigation schedules of different MAD of ASW. The established CWSI values can be used for monitoring plant water status and planning irrigation scheduling for wheat crop.
Leaf chlorophyll content provides valuable information about physiological status of plants. Reflectance measurement makes it possible to quickly and non-destructively assess, in situ, the chlorophyll content in leaves. Our objective was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation in leaves with a wide range of pigment content and composition using reflectance in a few broad spectral bands. Spectral reflectance of maple, chestnut, wild vine and beech leaves in a wide range of pigment content and composition was investigated. It was shown that reciprocal reflectance (R lambda)-1 in the spectral range lambda from 520 to 550 nm and 695 to 705 nm related closely to the total chlorophyll content in leaves of all species. Subtraction of near infra-red reciprocal reflectance, (RNIR)-1, from (R lambda)-1 made index [(R lambda)(-1)-(RNIR)-1] linearly proportional to the total chlorophyll content in spectral ranges lambda from 525 to 555 nm and from 695 to 725 nm with coefficient of determination r2 > 0.94. To adjust for differences in leaf structure, the product of the latter index and NIR reflectance [(R lambda)(-1)-(RNIR)-1]*(RNIR) was used; this further increased the accuracy of the chlorophyll estimation in the range lambda from 520 to 585 nm and from 695 to 740 nm. Two independent data sets were used to validate the developed algorithms. The root mean square error of the chlorophyll prediction did not exceed 50 mumol/m2 in leaves with total chlorophyll ranged from 1 to 830 mumol/m2.
In this study, the capability of crop water stress index (CWSI) based on satellite thermal infrared data for estimating water stress and irrigation scheduling in sugarcane fields was evaluated. For this purpose, eight Landsat 8 satellite images were acquired during the sugarcane growing season (May–September 2015). Simultaneous with the satellite overpass times, in-situ measurements of canopy temperature and vegetation water content (VWC) were conducted in forty points located in eight sugarcane fields per image (in total 320 observation points in 32 fields). These fields were selected with different ages (Plant, Ratoon 1, Ratoon 2, and Ratoon 3) and irrigation schedule. Then, the CWSI was calculated in three different ways including: 1) based on the Idso method and using the handheld infrared thermometer, 2) based on the Idso method and thermal infrared data of Landsat 8 satellite imagery, 3) using a new proposed procedure for retrieving CWSI from the satellite imagery with using the hot and cold pixels. Results show a good relationship between the calculated CWSI based on field measurement and new CWSI based on satellite data with the coefficient of determination of 0.49–0.85 and the root mean square error (RMSE) of 0.12–0.29 for different images. Further, a negative relationship between VWC and CWSI, with R² values of 0.42–0.78, was observed. This relationship increases with developing sugarcane canopy, and decreases with an increasing plant age. Comparing recorded irrigation events in the fields, estimated CWSI and VWC shows that water stress can be classified into three critical classes including high water stress (0.70 < VWC ≤ 0.75), medium water stress (0.75 < VWC ≤ 0.8), and low stress (0.8 < VWC ≤ 0.85). This classification can be used as a part of an operational procedure for appropriate irrigation scheduling. All of the aforementioned results indicate that the CWSI based on the proposed approach in this study can be used effectively for monitoring water stress and irrigation scheduling in sugarcane fields using satellite imagery without any need for ground ancillary data.
This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4 dS/m), moderate salinity (3.5–5.9 dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R2 of 0.68 and RMSE of 1.15 dS/m for Hyperion followed by Landsat data with an R2 and RMSE of 0.56 and 1.75 dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable.
The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R2 = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model.
Eu2O3 in divided groups in pores in a mesoporous Al2O3 (amorphous) has a confined growth in nanocrystals (average, 30 nm diameter) in a new polymorph of R3̄c hexagonal crystal structure with lattice parameters a = 0.5468 nm and c 1.6950 nm. This occurs on reacting dispersed Eu3+ cations (in water) with a mesoporous AlO(OH)·αH2O powder. A pore incorporates Eu3+ cations in a confined group depending on its size and governs controlled Eu2O3 nucleation and growth in a self-confined dimension in a nanocrystal. This new lattice involves a 5.60, 2.99 and 1.03 times larger volume V0, enclosed in a reduced S0 5.41 nm2 surface per unit volume relative to 9.27, 9.28 and 5.52 nm2, in the bulk hexagonal Eu2O3, monoclinic Eu2O3 and cubic Eu2O3 polymorphs respectively. From the pressure-volume isotherm, the large V0 implies that it grows quickly under the influence of a reduced effective pressure in the pores so that it balances the ΔP → 0 pressure gradient as early as possible. The results are discussed with a proposed model of nucleation and growth in a self-confined dimension under the influence of a reduced pressure.
Canopy temperatures were measured on durum wheat grown in six
differentially irrigated plots. Soil water content was measured by using
a neutron-scattering technique at two locations within each plot. Water
contents, in 20-cm increments to 160 cm, were determined two to five
times per week. Using a sliding cubic smoothing technique, we calculated
daily water contents and thus water depletion rates for the entire
growing season. Canopy temperatures were measured daily between 1330 and
1400 hours. Air temperatures measured at 150 cm above the soil surface
were subtracted from the canopy temperatures to form the difference
Tc - Ta. The summation of Tc -
Ta over time yielded a factor termed the `stress degree day'
(SDD). The SDD concept shows promise as an indicator for determining the
times and amounts of irrigations. An expression relating
evapotranspiration (ET) to net radiation and Tc -
Ta was simplified and tested by using ET measurements with a
lysimeter. The expression was used to predict water use by wheat in the
six plots. Predicted ET and measured water used agreed reasonably well.
The expression may be useful in determining amounts of irrigation water
to apply.
Granger causality (GC) is used in the econometrics literature to identify the presence of one- and two-way coupling between terms in noisy multivariate dynamical systems. Here we test for the presence of GC to identify a soil moisture (S) feedback on precipitation (P) using data from Illinois. In this framework S is said to Granger cause P if F(Pt|Ωt−Δt)≠F(Pt|Ωt−Δt−St−Δt) where F denotes the conditional distribution of P, Ωt−Δt represents the set of all knowledge available at time t−Δt, and Ωt−Δt−St−Δt represents all knowledge except S. Critical for land–atmosphere interaction research is that Ωt−Δt includes all past information on P as well as S. Therefore that part of the relation between past soil moisture and current precipitation which results from precipitation autocorrelation and soil water balance will be accounted for and not attributed to causality. Tests for GC usually specify all relevant variables in a coupled vector autoregressive (VAR) model and then calculate the significance level of decreased predictability as various coupling coefficients are omitted. But because the data (daily precipitation and soil moisture) are distinctly non-Gaussian, we avoid using a VAR and instead express the daily precipitation events as a Markov model. We then test whether the probability of storm occurrence, conditioned on past information on precipitation, changes with information on soil moisture. Past information on precipitation is expressed both as the occurrence of previous day precipitation (to account for storm-scale persistence) and as a simple soil moisture-like precipitation-wetness index derived solely from precipitation (to account for seasonal-scale persistence). In this way only those fluctuations in moisture not attributable to past fluctuations in precipitation (e.g., those due to temperature) can influence the outcome of the test. The null hypothesis (no moisture influence) is evaluated by comparing observed changes in storm probability to Monte-Carlo simulated differences generated with unconditional occurrence probabilities. The null hypothesis is not rejected (p>0.5) suggesting that contrary to recently published results, insufficient evidence exists to support an influence of soil moisture on precipitation in Illinois.
Leaf-area index of a forest can be measured by determining the ratio of light at 800 mμ to that at 675 mμ on the forest floor. It is based on the principle that leaves absorb relatively more red than infrared light, and therefore, the more leaves that are present in the canopy, the greater will be the ratio.
Eu2O3 in divided groups in pores in a mesoporous Al2O3 (amorphous) has a confined growth in nanocrystals (average, 30 nm diameter) in a new polymorph of R3c hexagonal crystal structure with lattice parameters a = 0.5468 nm and c = 1.6950 nm. This occurs on reacting dispersed Eu cations (in water) with a mesoporous AlO(OH) · α2O powder. A pore incorporates Eu cations in a confined group depending on its size and governs controlled Eu2O3 nucleation and growth in a self-confined dimension in a nanocrystal. This new lattice involves a 5.60, 2.99 and 1.03 times larger volume V0, enclosed in a reduced S0 = 5.41 nm surface per unit volume relative to 9.27, 9.28 and 5.52 nm, in the bulk hexagonal Eu2O3, monoclinic Eu2O3 and cubic Eu2O3 polymorphs respectively. From the pressure–volume isotherm, the large V0 implies that it grows quickly under the influence of a reduced effective pressure in the pores so that it balances the ▵P → 0 pressure gradient as early as possible. The results are discussed with a proposed model of nucleation and growth in a self-confined dimension under the influence of a reduced pressure.
The spectral bands most sensitive to salt-stress across diverse plants have not yet been defined; therefore, the predictive ability of previous vegetation indices (VIs) may not be satisfied for salinization monitoring. The hyperspectra of seven typical salt-sensitive/halophyte species and their root-zone soil samples were collected to investigate the relationship between vegetation spectra and soil salinity in the Yellow River Delta (YRD) of China. Several VIs were derived from the recorded hyperspectra and their predictive power for salinity was examined. Next, a univariate linear correlogram as well as multivariate partial least square (PLS) regression was employed to investigate the sensitive bands. VIs examination and band investigation confirmed that the responses of the vegetation differed from species to species, which explained the vibrations of the VIs in many study cases. These differences were primarily between salt-sensitive and halophyte plants, with the former consistently having higher sensitivity than the latter. With the exception of soil adjusted vegetation index (SAVI), most VIs were found to have weak relationships with soil salinity (with average R2 of 0.28) and some were not sensitive to all species [e.g. photochemical reflectance index (PRI) and red edge position (REP)], which verified that most currently available VIs are not adequate indicators of salinity for various species. PLS was validated as a more useful tool than linear correlogram for identification of sensitive bands due to well dealing with multicollinear spectral variables. From PLS, wavelengths at 395–410, 483–507, 632–697, 731–762, 812–868, 884–909, and 918–930 nm were determined to be the most sensitive bands. By combining the most sensitive bands in a SAVI form, we finally proposed four soil adjusted salinity indices (SASIs) for all species. Satisfactory relationships were observed between ECe and four SASIs for all species, with largely improved R2 values ranging from 0.50 to 0.58. Our findings indicate the potential to monitor soil salinity with the hyperspectra of salt-sensitive and halophyte plants.
Water stored in the soil serves as a reservoir for the evapotranspiration (ET) process on land surfaces, therefore knowledge of the soil moisture content is important for partitioning the incoming solar radiation into latent and sensible heat components. There is no remote sensing technique which directly observes the amount of water in this reservoir, however microwave remote sensing at long wavelengths (λ>10 cm) can give estimates of the moisture stored in the surface 5-cm layer of the soil. This approach is based on the large dielectric contrast between water and dry soil, resulting in emissivity changes from 0.96 for a dry smooth soil to less than 0.6.
In this paper, basic relationships between soil moisture and emissivity are described using both theory and observations from various platforms. The ability of the approach to be extended to large regions has been demonstrated in several aircraft mapping experiments, e.g., FIFE, Monsoon 90, Washita 92 and HAPEX Sahel. Some results from Monsoon 90 are presented here. Applications of these soil moisture maps in runoff prediction, rainfall estimation, determining the direct evaporation from the soil surface and serving as a boundary condition for soil profile models are presented.
It has previously been reported that canopy water loss by cowpea (Vigna unguiculata) decreases with small depletions in soil water. In these studies, under field conditions, it was demonstrated that with small changes in soil water status leaf conductance of cowpea decreases in a manner which is consistent with the sensitive regulation of canopy water loss.
However, treatments which differed in leaf conductance, and presumably stomatal aperture, had similar leaf water potentials. It is hypothesized that the stomatal closure which results from soil water depletion is mediated by changes in root water status through effects on the flow of information from root to shoot. An efficient mechanism of this type could be partially responsible for the extreme drought avoidance exhibited by this plant.
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 main goal of this research was to evaluate the potential of the dual approach of FAO-56 for estimating actual crop evapotranspiration (AET) and its components (crop transpiration and soil evaporation) of an olive (Olea europaea L.) orchard in the semi-arid region of Tensift-basin (central of Morocco). Two years (2003 and 2004) of continuous measurements of AET with the eddy-covariance technique were used to test the performance of the model. The results showed that, by using the local values of basal crop coefficients, the approach simulates reasonably well AET over two growing seasons. The Root Mean Square Error (RMSE) between measured and simulated AET values during 2003 and 2004 were respectively about 0.54 and 0.71 mm per day. The basal crop coefficient (Kcb) value obtained for the olive orchard was similar in both seasons with an average of 0.54. This value was lower than that suggested by the FAO-56 (0.62). Similarly, the single approach of FAO-56 has been tested in the previous work (Er-Raki et al., 2008) over the same study site and it has been shown that this approach also simulates correctly AET when using the local crop coefficient and under no stress conditions.
We evaluated the initial 12 months of vegetation index product availability from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Earth Observing System-Terra platform. Two MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are produced at 1-km and 500-m resolutions and 16-day compositing periods. This paper presents an initial analysis of the MODIS NDVI and EVI performance from both radiometric and biophysical perspectives. We utilize a combination of site-intensive and regionally extensive approaches to demonstrate the performance and validity of the two indices. Our results showed a good correspondence between airborne-measured, top-of-canopy reflectances and VI values with those from the MODIS sensor at four intensively measured test sites representing semi-arid grass/shrub, savanna, and tropical forest biomes. Simultaneously derived field biophysical measures also demonstrated the scientific utility of the MODIS VI. Multitemporal profiles of the MODIS VIs over numerous biome types in North and South America well represented their seasonal phenologies. Comparisons of the MODIS-NDVI with the NOAA-14, 1-km AVHRR-NDVI temporal profiles showed that the MODIS-based index performed with higher fidelity. The dynamic range of the MODIS VIs are presented and their sensitivities in discriminating vegetation differences are evaluated in sparse and dense vegetation areas. We found the NDVI to asymptotically saturate in high biomass regions such as in the Amazon while the EVI remained sensitive to canopy variations.
Using a new technique referred to as the triangle method, surface soil water content and fractional vegetation cover were derived from surface radiant temperature measurements and normalized difference vegetation index (NDVI). Application of the technique is made with reference to NS001 multispectral scanner measurements made by a C-130 aircraft over the Mahantango Watershed in Pennsylvania. The derived surface soil water content values were compared with those obtained from the Push Broom Microwave Radiometer (PBMR) aboard the same aircraft and with in-situ ground measurements. A large disparity was found to exist between all three measurements, suggesting that the surface becomes decoupled from the deeper substrate in regions of rapid drying, where large vertical gradients in soil water content may exist near the surface.
Given the relative dearth of, and the huge demand for, quantitative spatial soil information, it is timely to develop and implement methodologies for its provision. We suggest that digital soil mapping, which can be defined as the creation, and population of spatial soil information systems (SSINFOS) by the use of field and laboratory observational methods, coupled with spatial and non-spatial soil inference systems, is the appropriate response. Problems of large extents and soil-cover complexity and coarse resolutions and short-range variability representation carry over from conventional soil survey to digital soil mapping. Meeting users’ requests and demands and the ability to deal with spatially variable and temporally evolving datasets must be the key features of any new approach.In this chapter, we present a generic framework that recognises the procedures required. Within quantitatively defined physiographic regions, SSINFOS must be populated and spatial soil inference systems (SSINFERS) must be developed. When combined this will allow users to derive the data they require. Further work is required on the development of these systems, and on the data requirements, the optimal forms of inference and the appropriate representation of the products of digital soil mapping.
A transformation technique is presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. Graphically, the transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. For cotton (Gossypium hirsutum L. var DPI-70) and range grass (Eragrosticslehmanniana Nees) canopies, underlain with different soil backgrounds, the transformation nearly eliminated soil-induced variations in vegetation indices. A physical basis for the soil-adjusted vegetation index (SAVI) is subsequently presented. The SAVI was found to be an important step toward the establishment of simple °lobal” that can describe dynamic soil-vegetation systems from remotely sensed data.
The relationships between various linear combinations of red and photographic infrared radiances and vegetation parameters are investigated. In situ spectrometers are used to measure the relationships between linear combinations of red and IR radiances, their ratios and square roots, and biomass, leaf water content and chlorophyll content of a grass canopy in June, September and October. Regression analysis shows red-IR combinations to be more significant than green-red combinations. The IR/red ratio, the square root of the IR/red ratio, the vegetation index (IR-red difference divided by their sum) and the transformed vegetation index (the square root of the vegetation index + 0.5) are found to be sensitive to the amount of photosynthetically active vegetation. The accumulation of dead vegetation over the year is found to have a linearizing effect on the various vegetation measures.
The theory of microwave thermal emission from a nonscattering half-space medium is developed for application to regions with nonuniform subsurface soil-moisture and temperature variations. A coherent stratified model is presented which is valid for nonuniform temperature profiles and rapidly varying moisture profiles, under which conditions the commonly used emissivity and radiative-transfer approaches become inaccurate. For naturally occurring profiles the stratified model gives more accurate results than the other approaches at frequencies below about 4 GHz. Experimental results from ground-based radiometric observations of a controlled target area compare systematically with brightness temperatures predicted from the theoretical model to within approximately 10 K. Results of dielectric-constant measurements of the sand are given at seven frequencies in the microwave range and for moisture contents in the range from 0% to 30% by volume. By using this model, the thermal microwave emission spectrum is computed for a number of representative moisture and temperature profiles in the frequency range from 0.25 to 25 GHz.
Determination of water deficits in plant tissues. Water deficit and plant growth
- H D Barrs
Barrs, H. D. (1968). Determination of water deficits in plant tissues.
Water deficit and plant growth (pp. 235-368).
Using the USGS Landsat8 Product
- Missions
- Landsat
Missions, USGS Landsat. (2016). Using the USGS Landsat8 Product.
US Department of the Interior-US Geological Survey-NASA.