ESTIMATING ABOVE GROUND BIOMASS, EVAPOTRANSPIRATION, AND CROP WATER PRODUCTIVITY OF RICE ACROSS DIVERSE IRRIGATION AND FERTILIZER RATES USING UAV REMOTE SENSING

Abstract

Precision agriculture requires the precise estimation of crucial crop parameters including Above Ground Biomass (AGB), Actual Crop Evapotranspiration (ETa), and Crop Water Productivity (CWP). AGB is the key indicator of grain productivity, which describes the health condition of the crop. CWP is an important indicator for optimizing water use and yield in agriculture. Unmanned Aerial Vehicles (UAVs) are powerful tools for site-specific estimation of AGB, ETa, and CWP in precision agriculture. In this study, an unmanned aerial vehicle (UAVs) equipped with a multispectral camera was used for developing AGB prediction model and estimating CWP of rice crop. Field experiment was conducted in Roorkee, India, where rice was cultivated under two irrigation levels (continuous flooding (CF), and alternate wetting and drying (AWD)) and three nitrogen treatments (high nitrogen (HN): 150 kg ha-1, medium nitrogen (MN): 120 kg ha-1, and low nitrogen (LN): 60 kg ha-1). There were a total of seven treatments (T0 = rainfed, T1= CF-HN, T2= CF-MN, T3= CF-LN, T4= AWD-HN, T5= AWD-MN, and T6= AWD-LN. Seven UAV-based vegetation indices (VIs), ratio vegetation index (RVI), normalized difference vegetation index (NDVI), normalized green-red difference index (NGRDI), Green normalized difference vegetation index (GNDVI), Transformed normalized difference vegetation index (TNDVI), Normalized difference red edge (NDRE), and Leaf chlorophyll index (LCI) were used to develop AGB prediction model. The model developed by the enter method in IBM SPSS has R2 and RMSE of 0.78 and 0.77 t ha-1 and the stepwise method has R2 and RMSE of 0.81 and 0.72 t ha-1 during the testing of the model. Both models significantly estimate the AGB but the model developed from the enter method is more reliable due to its nearly equal R2 values in both the training and testing phases. UAV-derived Normalized Difference Vegetation Index (NDVI) was used for the estimation of actual crop evapotranspiration (ETa) and CWP. The highest seasonal ETa was found in treatment T4 (316.06 mm) while the lowest was in treatment T0 (311.49 mm). The above-ground biomass (AGB) and grain yield of rice were calculated using the radiation utilization efficiency (RUE) model combined with NDVI. The model estimated the AGB with R² of 0.63 and RMSE of 0.61 t ha-1, and grain yield with R² of 0.95 and RMSE of 0.41 t ha-1. The CWP was highest in treatment T5 (1.13 kg m-3) and lowest in treatment T0 (0.76 kg m-3). For treatments, T1, T2 T3, T4, and T6, the CWPs were 1.13, 1.13, 1.07, 1.05, and 1.12 kg m-3, respectively. Considering the global CWP categories for rice crop as low (≤0.70 kg m-3), medium (>0.70 to ≤1.25 kg m-3), and high (>1.25 kg m-3), the CWP in the present study was within the medium category. CWP varied according to different nitrogen application rates and irrigation levels.