Evaluating the effectiveness of remote sensing-based vegetation indices in estimating the spatio-temporal distribution of Nitrogen rates

Abstract

Evaluating the effectiveness of remote sensing-based vegetation indices in estimating the spatio-temporal distribution of Nitrogen rates One of the main objectives of precision agriculture is to optimize nitrogen fertilizer application management. This study aimed to assess the efficacy of different vegetation indices derived from satellite data in estimating soil ammonium and nitrate values during the corn growing season. To achieve this, multi-temporal Sentinel-2 images and ground data including ammonium and soil nitrate values measured at specific ground stations throughout the corn growing season for Hunter field, Canada, were utilized. Firstly, various vegetation indices including NDVI, EVI, MSAVI, ARVI, GNDVI, and OSAVI were calculated for different dates throughout the crop growing season. Subsequently, the Pearson correlation between these vegetation indices and temporal variations in soil ammonium and nitrate values during the growing season was examined. Moreover, the relationship between vegetation indices at the crop growth peak and the amount of fertilizer applied to the soil during planting was investigated. The findings indicated that the average correlation coefficients between total soil nitrate and ammonium values throughout the growing season and the NDVI, EVI, MSAVI, ARVI, GNDVI, and OSAVI indices were -0.67, -0.72, -0.69, -0.68, -0.73, and -0.70, respectively. Furthermore, the average correlation coefficients between these indices at the growth peak and the cumulative ammonium and nitrate applied at planting were 0.60, 0.60, 0.64, 0.60, 0.68, and 0.64, respectively. The correlation coefficient and root mean square error (RMSE) between the measured and modeled sum of ammonium and nitrate, based on the six vegetation indices in a multivariate form, were 0.89 and 17.3 mg, respectively.