Asian Journal of Advances in Agricultural Research

The practice of planning and controlling irrigation water applications to satisfy crop water needs without wasting water, soil, or plant nutrients is known as irrigation water management. Usage of the FAO-56 bulletin's instructions, which use tabular crop coefficients (Kc), is popular when evaluating agricultural water requirements. Crop evapotranspiration (ETc), which is directly dependent on crop coefficient curves, is based on point-based crop coefficients. The trend of multispectral vegetation indices (VIs) produced from remote sensing is comparable to that of crop coefficients (Kc). As such, VIs can serve as a Kc alternative and be used to estimate crop coefficients. The use of VI may provide Kc a spatial dimension, allowing for the accurate recording of the spatial variability in water requirements. Therefore, the main objective of the current study, was to determine which VI is most appropriate for the rabi wheat crop for the 2022–2023 crop year and has a tight correlation with crop coefficients.

The study was carried out in the North Maharashtra district of Nashik. The Sentinel 2A, MIS sensor's multi-date and multi-spectral images were utilized to create the multi-temporal vegetation indices (NDVI, NDWI, SAVI, and MSAVI2) for the 2022–2023 year. The agricultural acreages were determined by applying hybrid classification utilizing K means clustering and visual analysis of remote sensing. These estimates for wheat showed 3.83 percent variations from the Department of Agriculture's predictions for the year 2022–2023. The multidate vegetation index data for NDVI, NDWI, SAVI, and MSAVI 2 were ordered for the years 2022–2023 based on age given in weeks. To establish a relationship with VIs, week wise crop coefficients (Kc), as recommended by MPKV Rahuri, were used. Through the use of linear regression analysis, the correlations were created into prediction models. In the situations of wheat year 2022–2023 among all the VIs, the NDWI model performed best it shown that NDWI showed the greatest collaboration with the crop coefficient. Its R² and D values were highly significant, at 0.9365 and 0.986, respectively. It also had the lowest PD, SE, and RMSE values-0.095, 0.0891, and 3.34, respectively. Thus, it is possible to calculate the geographical crop coefficients for wheat using the NDWI-Kc model (Kc = 4.2302NDWI + 0.3716).

The NDWI-Kc model was utilized to derive the week-wise crop coefficients (Kc) for wheat, and the FAO Penman-Monteith method was used to estimate the reference evapotranspiration (ETo). After that, the crop evapotranspiration (ETc) was estimated by multiplying the corresponding Kc values by ETo. 397.13 mm of water was anticipated to be needed overall for the wheat crop in the Nashik district. Thus, it was determined that the study area's total water need for wheat was 22907238.307 ha.cm, or 229.07 Mm3. Because there is less rainfall during the rabi season, the amount of water required for irrigation nearly equals the amount needed for crops in the area.

"This study investigates the use of multispectral remote sensing data from Sentinel-2A to estimate wheat evapotranspiration (ETc) using GIS-based crop coefficients. Vegetation indices such as NDVI, NDWI, SAVI, and MSAVI2 were evaluated to identify the most accurate model for predicting crop water demand. The results show that the NDWI-Kc model demonstrated the strongest correlation and accuracy in estimating spatial crop coefficients".