Geospatial modeling and mapping of soil organic carbon and texture from spectroradiometric data in Nile basin

Abstract

Rapid soil properties monitoring and modeling are crucial for sustainable soil management interventions. The analysis of soil properties in the laboratory using standard procedures is time-consuming and requires the use of chemicals. The research aimed to develop a regression model based on the visible-near-infrared-short-wave infrared (Vis-NIR-SWIR, 0.35–2.5 μm) spectral reflectance of soils in Aba Gerima (425 ha), a highland of the Ethiopian Nile Basin. Soil samples were obtained at a 0–20 cm depth from 101 plots in the study area and analyzed for soil organic carbon (SOC) and texture using standard laboratory procedures. Soil reflectance was measured using a spectroradiometer on the air-dried, ground, and sieved soils. ArcGIS software 10.5 was used to conduct the semi-variogram and geostatistical methods—Ordinary Kriging (OK) and Least Distance Weighting (IDW). Strong degrees of spatial dependence are depicted through semi-variogram models. This outcome illustrates a strong spatial dependence and continuity between nearby soil samples. The prediction accuracy of the model based on the values of coefficient of determination (R2) and root mean square error (RMSE) was explained. For clay content, the R2 and RMSE obtained in the Vis-NIR-SWIR were excellent (0.91) and 2.93. An R2 of 0.85 with an RMSE of 1.96 was obtained in predicting sand. The SOC model established indicated an R2 of 0.94 and an RMSEC of 0.01. The generated maps showed that the soil characteristics' spatial variability was adequate to forecast the values of soil fertility indicators in the research area and nearby regions. The soil maps may assist farmers and policymakers in identifying soil nutrient status, improving soil management strategies, rising crop productivity, and guaranteeing ecological integrity. Moreover, further study is required to corroborate our findings and improve the prediction performance of Vis-NIR-SWIR spectroscopy to help better land management interventions.