Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands

Fabio Castaldi

doi:10.3390/rs13173345

Abstract

The spatial and temporal monitoring of soil organic carbon (SOC), and other soil properties related to soil erosion, is extremely important, both from the environmental and economic perspectives. Sentinel-2 (S2) and Landsat-8 (L8) time series increase the probability to observe bare soil fields in croplands, and thus, monitor soil properties over large regions. In this regard, this work suggests an automated pixel-based approach to select only pure soil pixels in S2 and L8 time series, and to make a synthetic bare soil image (SBSI). The SBSIs and the soil properties measured in the framework of the European LUCAS survey were used to calibrate SOC, clay, and CaCO3 prediction models. The results highlight a high correlation between laboratory soil spectra and the SBSIs median spectra, especially for the SBSI obtained by a three-year S2 collection, which provides satisfactory results in terms of SOC prediction accuracy (RPD: 1.74). The comparison between S2 and L8 results demonstrated the higher capability of the S2 sensor in terms of SOC prediction accuracy, mainly due to the greater spatial resolution of the bands in the visible region. Whereas, neither S2 nor L8 could accurately predict the clay and CaCO3 content. This is because of the low spectral and spatial resolution of their SWIR bands that prevent the exploitation of the narrow spectral features related to these two soil attributes. The results of this study prove that large S2 time series can estimate and monitor SOC in croplands using an automated pixel-based approach that selects pure soil pixels and retrieves reliable synthetic soil spectra.

Highlights

Soil organic matter loss, and soil erosion, is one of the main processes of land degradation in croplands, often due to increasingly intensive agricultural management [1,2]
This work investigates the capability of the S2 and L8 multi-temporal collection to estimate soil properties by extracting bare soil spectral data from a composite image: The synthetic bare soil image (SBSI)
Since one of the main challenges for automating the soil properties mapping and monitoring from imaging spectroscopy is the minimization of disturbing factors, an automated pixel-based approach to select exposed soil pixels not affected by clouds, vegetation, and high soil moisture was proposed, with the aim to clearly define the conditions under which reliable soil properties map can be produced over a large region

Summary

Introduction

Soil erosion, is one of the main processes of land degradation in croplands, often due to increasingly intensive agricultural management [1,2] In this regard, The Voluntary Guidelines for Sustainable Soil Management published by Intergovernmental Technical Panel on Soils [3] indicated the loss of soil organic carbon (SOC) as one of the main causes of soil degradation and lay down a set of good practices to enhance the soil organic matter content and improve the soil fertility. The Voluntary Guidelines for Sustainable Soil Management published by Intergovernmental Technical Panel on Soils [3] indicated the loss of soil organic carbon (SOC) as one of the main causes of soil degradation and lay down a set of good practices to enhance the soil organic matter content and improve the soil fertility These practices will be used as guidelines for cross-compliance rules for Common Agricultural. This is due to chemical components or chromophores interacting with visible and infrared radiations and showing well-defined

Methods

Results

Discussion

Conclusion