Monitoring impacts of soil bund on spatial variation of teff and finger millet yield with Sentinel-2 and spectroradiometric data in Ethiopia

Abstract

Data from remote sensing devices are essential for monitoring environmental protection practices and estimating crop yields. However, yield estimates in Ethiopia are based on time-consuming surveys. We used Sentinel-2, spectroradiometeric, and ground-truthing data to estimate the grain yield (GY) of two major crops, teff, and finger millet, in Ethiopia's Aba Gerima catchment in 2020 and 2021. At the flowering stage, we performed supervised classification on October Sentinel-2 images and spectral reflectance measurement. We used regression models to identify and predict crop yields, as evaluated by the coefficient of determination (adjusted R2) and root mean square error (RMSE). The enhanced vegetation index (EVI) and normalized-difference vegetation index (NDVI) provided the best fit to the data among the vegetation indices used to predict teff and finger millet GY. Soil bund construction increased the majority of vegetation indices and GY of both crops. We discovered a strong correlation between GY and the satellite EVI and NDVI. However, NDVI and EVI had the greatest influence on teff GY (adjusted R2 = 0.83; RMSE = 0.14 ton/ha), while NDVI had the greatest influence on finger millet GY (adjusted R2 = 0.85; RMSE = 0.24 ton/ha). Teff GY ranged from 0.64 to 2.16 ton/ha for bunded plots and 0.60 to 1.85 ton/ha for non-bunded plots using Sentinel-2 data. Besides, finger millet GY ranged from 1.92 to 2.57 ton/ha for bunded plots and 1.81 to 2.38 ton/ha for non-bunded plots using spectroradiometric data. Our findings show that Sentinel-2- and spectroradiometeric-based monitoring can help farmers manage teff and finger millet to achieve higher yields, more sustainable food production, and better environmental quality in the area. The study's findings revealed a link between VIs and soil management practices in soil ecological systems. Model extrapolation to other areas will necessitate local validation.

Keywords

• Crop yield prediction
• Sentinel-2
• Management practices
• Vegetation indices