Abstract
The image spectral data, particularly hyperspectral data, has been proven as an efficient data source for mapping of the spatial variability of soil organic carbon (SOC). Multispectral satellite data are readily available and cost-effective sources of spectral data compared to costly and technically demanding processing of hyperspectral data. Moreover, their continuous acquisition allows to develop a composite from time-series, increasing the spatial coverage of SOC maps. In this study, an evaluation of the prediction ability of models assessing SOC using real multispectral remote sensing data from different platforms was performed. The study was conducted on a study plot (1.45 km2) in the Chernozem region of South Moravia (Czechia). The adopted methods included field sampling and predictive modeling using satellite multispectral Sentinel-2, Landsat-8, and PlanetScope data, and multispectral UAS Parrot Sequoia data. Furthermore, the performance of a soil reflectance composite image from Sentinel-2 data was analyzed. Aerial hyperspectral CASI 1500 and SASI 600 data was used as a reference. Random forest, support vector machine, and the cubist regression technique were applied in the predictive modeling. The prediction accuracy of models using multispectral data, including Sentinel-2 composite, was lower (RPD range from 1.16 to 1.65; RPIQ range from 1.53 to 2.17) compared to the reference model using hyperspectral data (RPD = 2.26; RPIQ = 3.34). The obtained results show very similar prediction accuracy for all spaceborne sensors (Sentinel-2, Landsat-8, and PlanetScope). However, the spatial correlation between the reference mapping results obtained from the hyperspectral data and other maps using multispectral data was moderately strong. UAS sensors and freely available satellite multispectral data can represent an alternative cost-effective data source for remote SOC mapping on the local scale.
Highlights
The decreasing soil organic carbon (SOC) content in agriculture soils is generally considered a major threat to the sustainability of soil cultivation
This study aimed to evaluate the capability of multispectral Remote sensing (RS) data to predict the variability of SOC concentration in the topsoil in the study plot to assess the influence of spectral and spatial resolution on the prediction accuracy of models
The results of this study show that hyperspectral data provide better SOC estimations than multispectral data
Summary
The decreasing soil organic carbon (SOC) content in agriculture soils is generally considered a major threat to the sustainability of soil cultivation. The natural equilibrium of the soil environment is endangered due to external, primarily anthropogenic effects, which lead to the development of several degradation processes. These can affect the soil carbon stocks, especially in the topsoil layer. Monitoring, mapping, and describing the spatial variability of SOC (in landscape and within-field scale) are the key prerequisites for understanding the effects of agricultural practices on SOC changes. The data are provided in a sufficient spatial resolution suitable even for local monitoring and applications [10]
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