Soil Erosion Satellite-Based Estimation in Cropland for Soil Conservation

Abstract

Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite images provided ten years of rainfall data and 35 years of exposed soil surface. The bare soil surface and agricultural land use were extracted, and the multi-temporal rainfall erosivity was assessed. We predict soil maps’ attributes (texture and organic matter) through innovative soil spectroscopy techniques to assess the soil erodibility and soil loss tolerance. The erosivity, erodibility, and topography obtained by the Earth observations were adopted to estimate soil erosion in four scenarios of sugarcane (Saccharum spp.) residue coverage (0%, 50%, 75%, and 100%) in five years of the sugarcane cycle: the first year of sugarcane harvest and four subsequent harvesting years from 2013 to 2017. Soil loss tolerance means 4.3 Mg ha−1 exceeds the minimum rate in 40% of the region, resulting in a total soil loss of ~6 million Mg yr−1 under total coverage management (7 Mg ha−1). Our findings suggest that sugarcane straw production has not been sufficient to protect the soil loss against water erosion. Thus, straw removal is unfeasible unless alternative conservation practices are adopted, such as minimum soil tillage, contour lines, terracing and other techniques that favor increases in organic matter content and soil flocculating cations. This research also identifies a spatiotemporal erosion-prone area that requests an immediately sustainable land development guide to restore and rehabilitate the vulnerable ecosystem service. The high-resolution spatially distribution method provided can identify soil degradation-prone areas and the cropland expansion frequency. This information may guide farms and the policymakers for a better request of conservation practices according to site-specific management variation.

Soil degradation in andean watersheds: a case study using remote sensing

“…The relation between measured Land Surface Temperature and Normalized Difference Vegetation Index was used to assess land degradation and desertification in India using Landsat 8 (Kumar et al, 2022). Spectroscopy technique and RUSLE model were used to evaluate soil loss by water erosion in a sugarcane cropland in Brazil (Gallo et al, 2023). Retrospective monitoring of soil and land cover, deep machine learning using convolutional neural networks, and cartographic analysis were used to study eroded areas in Russia (Rukhovich et al, 2023).…”

Updating of the Archival Large-Scale Soil Map Based on the Multitemporal Spectral Characteristics of the Bare Soil Surface Landsat Scenes

“…In several works [30,37,45,53], the application of multitemporal RSD series in the topic of agriculture and landscape classification is studied, which brings together the directions of our research. Of particular interest is the approach of applying multitemporal series of RSD with the use of the BSS spectral characteristics [37,45]. In other areas of knowledge, multitemporal series are used more widely [46][47][48][49][50][51][52][53][54].…”

“…Both VIs and BSS can use both individual frames of the RSD [39][40][41][42][43][44] and multitemporal series [24,41,[45][46][47][48][49]. Multitemporal RSD series are increasingly being used in various areas of economic activity (restoration after fires, snowmelt monitoring, forest dynamics, tundra classification, environmental vulnerability, etc.)…”

Time Series Analysis of Landsat Images for Monitoring Flooded Areas in the Inner Niger Delta, Mali

“…Time series analysis of Earth observation data has proven to be effective in the evaluation of landscape changes using several images covering the same area in various consecutive years. For instance, satellite-derived trends are used as monitoring methods in a wide variety of environmental applications: mapping and monitoring wetlands (Wu, 2018), assessment of deforestation and forest degradation (Haarpaintner and Hindberg, 2019;Mashhadi and Alganci, 2022;Masolele et al, 2021;Schneibel et al, 2017), monitoring wetland dynamics (Kovács et al, 2022;Xie et al, 2022), evaluation of vegetation cover fraction and soil depletion (Dube et al, 2017;Gallo et al, 2023), computing vegetation indices (Lemenkova and Debeir, 2022a;Liu et al, 2022;Venter et al, 2020), estimating variations in land surface temperature (Carrillo-Niquete et al, 2022), assessment of spatio-temporal variations in night lights emissions in urban studies (Rehman et al, 2021) and more.…”