Soil moisture is an essential environmental parameter affecting hydrological cycles, agricultural productivity, and climate systems. Conventional in situ measurements are precise but do not provide the spatiotemporal coverage for large applications. This research provides an extensive framework for estimating and mapping surface soil moisture by integrating Sentinel-1 Synthetic Aperture Radar (SAR) data with machine learning in the Google Earth Engine (GEE) cloud platform. The study area is the agricultural region of Perambalur district in Tamil Nadu State, India. The research took place between September 2018 and January 2019. The dual-polarized (VV and VH) Sentinel-1 C-band images were collected in tandem with ground truth soil moisture data collected through the gravimetric method. A set of SAR indices and engineered features were extracted from the backscattering coefficients (σ°). A random forest (RF) machine learning model was used in this study to estimate soil moisture. The RF model incorporating the complete set of engineered features showed a coefficient of determination (R²) of 0.694 and a root mean square error (RMSE) of 1.823 (Soil moisture %). The complete processing and modeling workflow was encapsulated in the GEE-based software tool (version 1) providing an accessible, user-friendly platform for generating near-real-time maps of soil moisture. This research proves that the combination of Sentinel-1 data with clever machine-learning algorithms in the GEE cloud platform provides a scalable, efficient, and potent tool for operational soil moisture mapping serving applications in precision agriculture and in the management of the water resource.

Google Earth Engine (GEE) has revolutionised remote sensing. The GEE cloud platform lets users quickly analyse large satellite imagery datasets with custom programmes, enhancing global-scale analysis. Crop condition monitoring using GEE would greatly help in decision-making and precision agriculture. Estimating canopy chlorophyll content (CCC) is an effective way to monitor crops using remote sensing because leaf chlorophyll is a key indicator. A hybrid model that combines radiative transfer models (RTMs), such as PROSAIL, with Gaussian Process Regression (GPR) can effectively estimate crop biophysical parameters using remote sensing images. GPR has proven to be one of the best methods for this purpose. This study aimed to develop a hybrid model to estimate CCC from S2 imagery and transfer it to the GEE platform for efficient data processing. In this work, the CCC (g/cm²) data from the S2 biophysical processor toolbox for the S2 imagery of the ICAR-Indian Agricultural Research Institute (IARI) on 23 February 2023 were used as observation data to train the hybrid algorithm. The hybrid model was successfully validated against the 155 input data with an R2 of 0.94, RMSE of 10.02, and NRMSE of 5.04%. The model was integrated into GEE to successfully generate a CCC-estimated map of IARI using S2 imagery from 23 February 2023. An R2 value of 0.96 was observed when GEE-estimated CCC values were compared against CCC values estimated locally. This establishes that the GEE-based CCC estimation with the PROSAIL + GPR hybrid model is an effective and accurate method for monitoring vegetation and crop conditions over large areas and extended periods.

Evapotranspiration (ETo), vital for agricultural and environmental management, faces challenges from climate change and spatial variability. Accurate Land Use-specific ETo estimates are essential for sectors like agriculture, forestry, and water management. Leveraging remote sensing technology, particularly optical remote sensing, holds promise in overcoming limitations posed by scarce weather station data and cloud cover issues. The study encompasses a wide array of meteorological parameters, including Solar Radiation (SR), Temperature (T), Relative Humidity (RH), Wind Speed (WS), and Rainfall (RF), gathered from the archive of Public Works Department archives for the period 2016-2017. Employing the FAO Penman-Monteith method, we calculated reference ETo, representing ETo under standard conditions. This involved intricate steps, such as determining mean T, vapor pressure, the slope of the vapor pressure curve, psychrometric constant, net radiation, and, ultimately, ETo. To enhance our understanding, we employed Partial Least Squares Regression (PLSR) to model the relationship between predictor variables (VV and VH Polarized sigma naught values from Sentinel-1A) and ETo. We generated equations for both monthly mean datasets and overall study period mean, offering insights into short-term fluctuations and long-term trends. Comparative analyses across land cover types unveiled intriguing patterns. Urban transportation areas exhibited stability, while deciduous forests and wetlands showcased temporal variations. In the ETo comparative analysis, each land cover category exhibited distinctive patterns, providing valuable insights into the dynamics of ETo. Among the land cover parameters, ETo was significantly impacted by relative humidity (RH) (70.80% to 89.89%), and temperature (T). Urban vegetated areas had stable T values (29.37°C), while forests showed dynamic variations in T (24.24°C to 28.94°C). The VH polarization captured a diverse range of climatic influences, resulting in a broader range of dynamic ETo values (7.38 to 10.76 mm/day) compared to the VV polarization (6.74 to 9.34 mm/day). The performance of the VH sensor varied; moderate accuracy was observed in October 2016 (R 2 = 0.50) with slight underestimation (Bias = -0.08), whereas exceptional accuracy was seen in December 2017 (R 2 = 1.00) with positive bias (0.57) and excellent agreement (KGE = 0.92). The VV sensors in October 2016 had a firm fit (R 2 = 0.55), moderate underestimation (Bias = -0.87), and December 2017 showed a good fit (R 2 = 0.57), slight overestimation (Bias = 0.44), and good agreement (KGE = 0.44). Thus, integrating machine learning and satellite imagery improves ETo accuracy for real-time monitoring in adaptive management amid climate change, showcasing sensor-specific variations. For precise estimation of ETo, future research should integrate multi-source satellite data and machine learning, which is crucial for adaptive environmental management.

UAV-mounted multispectral sensors are widely used to study crop health. Utilising the same cameras to capture close-up images of crops can significantly improve crop health evaluations through multispectral technology. Unlike RGB cameras that only detect visible light, these sensors can identify additional spectral bands in the red-edge and near-infrared (NIR) ranges. This enables early detection of diseases, pests, and deficiencies through the calculation of various spectral indices. In this work, the ability to use UAV-multispectral sensors for close-proximity imaging of crops was studied. Images of plants were taken with a Micasense Rededge-MX from top and side views at a distance of 1 m. The camera has five sensors that independently capture blue, green, red, red-edge, and NIR light. The slight misalignment of these sensors results in a shift in the swath. This shift needs to be corrected to create a proper layer stack that could allow for further processing. This research utilised the Oriented FAST and Rotated BRIEF (ORB) method to detect features in each image. Random sample consensus (RANSAC) was used for feature matching to find similar features in the slave images compared to the master image (indicated by the green band). Utilising homography to warp the slave images ensures their perfect alignment with the master image. After alignment, the images were stacked, and the alignment accuracy was visually checked using true colour composites. The side-view images of the plants were perfectly aligned, while the top-view images showed errors, particularly in the pixels far from the centre. This study demonstrates that UAV-mounted multispectral sensors can capture images of plants effectively, provided the plant is centred in the frame and occupies a smaller area within the image.

This study provides a comprehensive evaluation of eight high spatial resolution gridded precipitation products in Semi-Arid regions of Tamil Nadu in India, particularly focusing on Coimbatore, Madurai, Tiruchirapalli and Tuticorin. The study regions lack sufficiently long-term and spatially representative observed precipitation data, which is a crucial component for hydrological management. Hence, the present study evaluates the accuracy of five remote sensing-based precipitation products viz. Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Records (PERSIANN CDR), CPC MORPHing technique(CMORPH), Global Precipitation Measurement (GPM) and Multi-Source Weighted-Ensemble Precipitation (MSWEP) and three reanalysis-based precipitation products viz. National Center for Environmental Prediction (NCEP2) Reanalysis 2, European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis version 5 Land (ERA 5 Land), Modern-Era Retrospective analysis for Research and Application version 2 (MERRA 2) against the station data obtained from the archives of respective Public Works Department. Initially, precipitation products and ground station data were gridded to a common spatial resolution of 0.1 by linear interpolation. The products were then statistically evaluated at multiple spatial (grid and district-wise) and temporal (daily, weekly, monthly and yearly) resolutions for the period 2003-2014. We found that district-wise analysis at monthly and yearly temporal resolution provided better correlation and significantly reduced biases and errors. Evaluation results showed that in terms of overall statistical metrics, ERA 5 Land, MSWEP, PERSIANN CDR and GPM were the best-performing precipitation products, while NCEP2 performed the worst. ERA 5 Land and MSWEP better represented the daily rainfall characteristics with lower Mean Absolute Error and Root Mean Square Error. This study has significant implications for managing hydrological resources by providing valuable guidance when choosing alternative precipitation products in data-scarce regions.