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Accurate evapotranspiration (ET) assessment is crucial for agricultural water management, encompassing crop water requirements, irrigation scheduling, water budgeting and drought monitoring. This study integrates remote sensing-based surface energy balance model with in-situ ET measurements to evaluate surface energy fluxes and ET in Pantnagar, Tarai region. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model, using high-resolution remote sensing data, was validated against observations from an ET station equipped with large aperture scintillometer and micrometeorological tower, situated in sugarcane farm at Govind Ballabh Pant University of Agriculture & Technology (GBPUA&T), Pantnagar. On November 13, 2021, METRIC and Landsat-9 satellite data estimated an instantaneous ET of 7.39 mm day-1, closely aligned with the observed value of 6.72 mm day-1 recorded by the ET station. The findings confirm the METRIC model's high accuracy for spatial ET estimation and its associated micrometeorological variables. This study underscores the utility of the METRIC model, ET station and remote sensing in determining ET and energy flux which may be further utilised in the estimation of crop water requirement, energy fluxes and irrigation water management for sugarcane cultivation in the Tarai region. [ABSTRACT FROM AUTHOR]

Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management. [ABSTRACT FROM AUTHOR]

Accurate mapping of coarse cereals envisaged maize to be discriminated from co-existing Kharif crops using multiplatform, multiparametric SAR and Optical data in various polarization combination from Sentinel-1 and RADARSAT-2 in synergism with Sentinel-2, under machine learning algorithms viz. support vector machine (SVM) and random forest (RF) and knowledge-based Decision Tree (DT). SVM over-estimates the area of maize whereas RF and DT performed similarly (60–75%) but DT being closest to field conditions (68–78%). A 4 date Sentinel-1 under DT classified Soybean to 62% accuracy and Cotton to 76% on a regional scale in Maharashtra. Inclusion of 1-date optical image improved sugarcane accuracy. The similar-structured crops, such as Maize and Pearl Millet were discriminated with their unique phenological response in crop-calendar using quad-pol RADARSAT-2. With inclusion of crop responsive model-based polarimetric decomposition (Yamaguchi Volume and Double-Bounce) and Eigen-based parameters (Entropy and Alpha-Angle), maize classification accuracy elevated to 84%. [ABSTRACT FROM AUTHOR]

In view with the importance of coarse cereals, it was proposed to discriminate pearl millet crop in a mixed crop scenario using different combinations of time series multipolarized SAR with available optical data. Five date dual polarized Sentinel-1 DT classification was found to be optimum, as Bajra classification accuracy increased by 7.6% over four date. The improved Bajra accuracy of 85% was achieved when 2 date Sentinel-1was integrated with higher value quad polarized Radarsat-2 data. Similar structured crops like Sorghum and maize could be discriminated using difference in crop calendar and polarization response. Single date Sentinel-2 (near harvest stage as available) with 2 date Sentinel-1 yields 66% accuracy for Bajra crop but the attempt to extract temporal phenological profile was failed due to persistent cloud cover in the previous dates. Radarsat-2 data increased Bajra accuracy up to 89.5% when responsive polarimetric parameters were used in conjunction with multiple linear polarizations. [ABSTRACT FROM AUTHOR]

Potential of Sentinel-1A SAR data was assessed for the time-series analysis of orchard biophysical parameters and crop system. The study revealed characteristics variations in the backscatter coefficient with respect to time and polarization for age in VH polarization than in VV and ratio of VV/VH polarization showing discrimination of young orchard particularly in VV polarization. The parameter of the orchard (age, DBH, canopy radius and visual height) shows a promising relationship with backscatter coefficient. Out of several regression models, VV channel responds with a fair regression coefficient of 0.54, 0.52, 0.48 and 0.44 for height with rmse of 0.5, 1.3, 0.7 and 0.6 for age, DBH, canopy radius and visual height, respectively. Multiple regression coefficient of 0.61 was observed for January 2018 in VV polarization as best date for study. These empirical relationships have potential for the inverse backscatter modelling. [ABSTRACT FROM AUTHOR]

Estimation and monitoring of crop evapotranspiration (ETc) or consumptive water use over large-area holds the key to irrigation management plans and regional drought preparedness. The objective of this study was to estimate ETc by applying the simplified-surface energy balance index (S-SEBI) model to Landsat-8 data for the 2014–2015 period in parts of North India. An average ETc was estimated 2.72 and 2.47 in mm day−1 with 0.22, 0.18 standard deviation and 0.11, 0.07 standard error for Kharif and Rabi crops, respectively. On validation part, a close relationship was observed between S-SEBI derived and scintillometer observed evaporative fraction with 0.85 correlation coefficient and 0.86 agreement index. The statistical analysis also endorses the results accuracy and reliability with 0.026 and 0.602, relative root-mean square errors and model efficiency for wheat crop, respectively. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of ETc. [ABSTRACT FROM AUTHOR]