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

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.

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

“…More mixed image elements may be included at a spatial resolution of 30 m [ 64 ], which reduces the SOC prediction accuracy. Fabio, C. obtained similar results when estimating topsoil properties in agricultural fields [ 19 ]. The predictive performances of the models based on Landsat 8 and Sentinel-2A data were similar under the three ensemble learning algorithms.…”

“…In this study, we selected several vegetation indices, moisture indices and soil brightness index variables to which SOC is sensitive. Among the vegetation indices selected were the normalized difference vegetation index (NDVI) [ 46 , 47 ], enhanced vegetation index (EVI) [ 48 ], difference vegetation index (DVI) [ 49 ], ratio vegetation index (RVI) [ 50 ], transformation vegetation index (TVI) [ 51 ], soil-adjusted vegetation index (SAVI) [ 52 ], soil-adjusted total vegetation index (SATVI) [ 53 ] and normalized burn ratio 2 (NBR2) [ 19 ]. The moisture indices include land surface water index (LSWI) [ 54 ] and moisture stress index (MSI) [ 20 ].…”

“…Spectral indices exploit the synergy of two or more bands to effectively mine spectral information. Therefore, some spectral indices with strong correlations with SOC (e.g., the soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI) and normalized burn ratio 2 (NBR2)) are used to estimate SOC [ 2 , 19 ]. Because Asa Gholizadeh [ 20 ] demonstrated that of the Sentinel-2 multispectral sensor information (MSI) data, band 4 and band 5 (red-edge bands and red-edge information) are most sensitive to SOC, we hypothesize that there may be potential to construct spectral indices from red-edge bands to estimate SOC with high accuracy.…”

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

“…Since 2015 and then 2017, when Sentinel-2A, followed by Sentinel-2B were launched, the Sentinel-2 (S2) time-series equipped with the MultiSpectral Instrument (MSI, 13 spectral bands) provided not only wide spatial coverage over swaths of 290 km, but also 10 to 20 m resolution (10 spectral bands) and a 5-day revisit. The advent of such time-series favored the renewal of the satellite-derived spectral models and particularly for SOC, using either single date acquisitions [44,[47][48][49][50][51][52][53][54][55] or multi-date approaches [36,[56][57][58][59]. In addition, some authors used Sentinel-1 synthetic aperture radar (SAR) images in their approach, either separately [50,55,58] or directly as covariates within their modeling [55,60].…”

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

“…These findings were in concordance with the recent study of Zepp et al [78], where the influence of vegetation index thresholding on Landsat assessments of exposed soil masks was also studied. Conversely, Castaldi [79] highlighted that Sentinel-2 and Landsat-8 were not able to properly predict clay and CaCO 3 because of the low spectral resolution in the SWIR. In a previous research study, Castaldi et al [80] attained more promising results for SOC.…”