Quantifying the Sensitivity of NDVI-Based C Factor Estimation and Potential Soil Erosion Prediction using Spaceborne Earth Observation Data

Dawit A Ayalew,Daniel Doktor,Detlef Deumlich,Bořivoj Šarapatka

doi:10.3390/rs12071136

Abstract

The Normalized Difference Vegetation Index (NDVI), has been increasingly used to capture spatiotemporal variations in cover factor (C) determination for erosion prediction on a larger landscape scale. However, NDVI-based C factor (Cndvi) estimation per se is sensitive to various biophysical variables, such as soil condition, topographic features, and vegetation phenology. As a result, Cndvi often results in incorrect values that affect the quality of soil erosion prediction. The aim of this study is to multi-temporally estimate Cndvi values and compare the values with those of literature values (Clit) in order to quantify discrepancies between C values obtained via NDVI and empirical-based methods. A further aim is to quantify the effect of biophysical variables such as slope shape, erodibility, and crop growth stage variation on Cndvi and soil erosion prediction on an agricultural landscape scale. Multi-temporal Landsat 7, Landsat 8, and Sentinel 2 data, from 2013 to 2016, were used in combination with high resolution agricultural land use data of the Integrated Administrative and Control System, from the Uckermark district of north-eastern Germany. Correlations between Cndvi and Clit improved in data from spring and summer seasons (up to r = 0.93); nonetheless, the Cndvi values were generally higher compared with Clit values. Consequently, modelling erosion using Cndvi resulted in two times higher rates than modelling with Clit. The Cndvi values were found to be sensitive to soil erodibility condition and slope shape of the landscape. Higher erodibility condition was associated with higher Cndvi values. Spring and summer taken images showed significant sensitivity to heterogeneous soil condition. The Cndvi estimation also showed varying sensitivity to slope shape variation; values on convex-shaped slopes were higher compared with flat slopes. Quantifying the sensitivity of Cndvi values to biophysical variables may help improve capturing spatiotemporal variability of C factor values in similar landscapes and conditions.

Highlights

Soil erosion is a major global land degradation threat that can result in the loss of soil productivity of agricultural land and in the reduction of the delivery of ecosystem services [1]
A further aim is to quantify the effect of biophysical variables such as slope shape, erodibility, and crop growth stage variation on Cndvi and soil erosion prediction on an agricultural landscape scale
Correlations between Cndvi and Clit improved in data from spring and summer seasons; the Cndvi values were generally higher compared with Clit values

Summary

Introduction

Soil erosion is a major global land degradation threat that can result in the loss of soil productivity of agricultural land and in the reduction of the delivery of ecosystem services [1] It is an inevitable natural phenomenon, soil erosion is often aggravated by anthropogenic interference in land use and changes in vegetation land cover [2,3]. The USLE, an empirical model, was designed to estimate long-term average annual soil erosion rates of agricultural land [4,6] It predicts annual soil loss as a product of six factors: rainfall erosivity, soil erodibility (K), topography (slope length (L) and slope steepness (S)), cover and management (C), and support practice (P). Apart from the USLE, several process-based models such as the Soil and Water Assessment Tool (SWAT) through the Modified Universal Soil Loss Equation [9,10], and the Agricultural Non-Point Source Pollution model (Young et al [11]) employ C factor for erosion prediction

Objectives

Methods

Results