Nitrate contamination in surface and groundwater remains a widespread problem in agricultural watersheds is primarily associated to high levels of percolation or leakage from fertilized soil, which allows easy infiltration from soil into groundwater. This study was aimed to predict canopy water content to determine the nitrate contamination index resulting from nitrogen fertilizer loss in surface and groundwater. The study used Geographically Weighted Regression (GWR) model using MODIS 006 MOD13Q1-EVI Earth observation data, crop information and rainfall data. Satellite data collection was synchronized with regional crop calendars and calibrated to plant biomass. The average plant biomass during observed plant growth stages was between 0.19 kg/m² at the minimum and 0.57 kg/m² at the maximum. These values are based on the growth stages of crops and provide a solid basis for monitoring and validating crop water productivity data. The simulation results were validated with a high correlation coefficient (R² = 0.996, P < 0.0005) for the observed rainfall in the growing zone compared to the predicted canopy water content. The nitrate contamination index assessment was conducted in 2004, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2018 and 2020. Canopy water content and root zone seasonal water content were measured in (%) per portion as indicators of the NO⁻₃-N-nitrate contamination index in these years (0.391, 0.316, 0.298, 0.389, 0.380, 0.339, 0.242, 0.342 and 0.356).

Cropland sub-watershed surface and groundwater contamination are pressing issues for water management. This study examines the impact of sediment transport indices (STI) on nitrate concentration in downstream water bodies. The research highlights rainfall's significant role in predicting seasonal nitrate levels. Employing GIS-SWPT tools and hydro-geomorphologic analysis, cropland sub-watersheds, particularly sub-watersheds one and three, are prioritized for their high contribution to downstream surface and groundwater nitrate contamination, with prioritization values of 105.58 and 180.63, respectively. Statistical analysis, conducted using Python's scikit-learn library, validated the findings of the study, with the model's F-statistic of 79.63 and a corresponding p-value of 0.0147 underscoring its overall significance. However, while STI alone showed a prioritization parametric correlation coefficient of 0.5077, suggesting other external factors also contribute to nitrate loading, a strong relationship between STI and nitrate concentration was revealed (R² of 0.993). This integrated approach enhances understanding of how geomorphologic parameters of cropland sub-watersheds influence water quality downstream. By clarifying the complex interactions between sediment transport and nitrate concentration, evidence-based strategies can be developed to mitigate surface and groundwater nitrate pollution. This research provides valuable insights into cropland sub-watershed pollution dynamics and informs targeted management interventions.

Crop canopy water content and crop root zone soil water content have been predicted and observed for estimation of crop water balance for the rainy season nitrate runoff and leaching index of the Bilate watershed cropland. For the calibration of earth data observation, the watershed rain gauge station estimated the pattern of rainfall for the main cropping season of crop growth. Cropping season (Apr, May and Jun) monthly mean rainfall between (125mm/month to 165 mm/month) of agro metrological station has predicted time series crop canopy water in the analysis for crop land nitrate-nitrogen leaching/runoff index for the quantification of groundwater and surface runoff nitrate from the Bilate watershed farming zone. May and June were the months in which crops grew in the cropland of the study area, as indicated by the crop biomass statistics from MODIS 006 MOD13Q1-EVIof (Mine 0.19 and Max, 0.57) which is the average crop coefficient Kc for crop growth curve analyzed for model crop parameter. Hyper-spectral indices derived from enhanced vegetation indices (EVI) have been calculated for the analysis of crop zonal biomass statistics (kc) and integrated into the prediction model. The objective of the current research was to predict crop canopy water content in order to determine crop water balance for farmland Nitrogen Nitrate (NO⁻ ₃-N) runoff\leaching index for surface and groundwater pollution in the Bilate downstream. The overall predicted result of crop canopy water content has been validated with a regression coefficient (R²) with observed crop root zone soil water content. And the crop land nitrogen balance has been used to confirm the nitrate-nitrogen leaching and runoff index for the study area by comparing the current result with the crop land nitrogen balance.