Synergy of remotely sensed data in spatiotemporal dynamic modeling of the crop and cover management factor

Abstract

Soil erosion is a threat to the water quality constituents of sediments and nutrients and can cause long-term environmental damages. One important parameter to quantify the risk of soil loss from erosion is the crop and cover management factor (C-factor), which represents how cropping and management practices affect the rates and potential risk of soil erosion. We developed remotely sensed data-driven models for dynamic predictions of C-factor by implementing dynamic land cover modeling using the SWAT (Soil and Water Assessment Tool) model on a watershed scale. The remotely sensed processed variables included the enhanced vegetation index (EVI), the fraction of photosynthetically active radiation absorbed by green vegetation (FPAR), leaf area index (LAI), soil available water content (AWC), slope gradient (SG), and ratio of area (AR) of every hydrologic response unit (HRU) to that of the total watershed, comprising unique land cover, soil type, and slope gradient characteristics within the Fish River catchment in Alabama, USA between 2001 and 2014. Linear regressions, spatial trend analysis, correlation matrices, forward stepwise multivariable regression (FSMR), and 2-fold cross-validation were conducted to evaluate whether there were possible associations between the C-factor and EVI with the successive addition of remotely sensed environmental factors. Based on the data analysis and modeling, we found a significant association between the C-factor and EVI with the synergy of the environmental factors FPAR, LAI, AWC, AR, and SG (predicted R 2 ( R 2 pred ) = 0.51; R 2 = 0.68, n = 3 220, P < 0.15). The results showed that the developed FSMR model constituting the non-conventional factors AWC ( R 2 pred = 0.32; R 2 = 0.48, n = 3 220, P < 0.05) and FPAR ( R 2 pred = 0.13; R 2 = 0.28, n = 3 220, P = 0.31) was an improved fit for the watershed C-factor. In conclusion, the union of dynamic variables related to vegetation (EVI, FPAR, and LAI), soil (AWC), and topography (AR and SG) can be utilized for spatiotemporal C-factor estimation and to monitor watershed erosion.