Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Abstract

Soil organic carbon (SOC) is a key property for evaluating soil quality. SOC is thus an important parameter of agricultural soils and needs to be regularly monitored. The aim of this study is to explore the potential of synthetic aperture radar (SAR) satellite imagery (Sentinel-1), optical satellite imagery (Sentinel-2), and digital elevation model (DEM) data to estimate the SOC content under different land use types. The extreme gradient boosting (XGboost) algorithm was used to predict the SOC content and evaluate the importance of feature variables under different land use types. For this purpose, 290 topsoil samples were collected and 49 features were derived from remote sensing images and DEM. Feature selection was carried out to prevent data redundancy. Coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), percent root mean squared error (%RMSE), ratio of performance to interquartile range (RPIQ), and corrected akaike information criterion (AICc) were employed for evaluating model performance. The results showed that Sentinel-1 and Sentinel-2 data were both important for the prediction of SOC and the prediction accuracy of the model differed with land use types. Among them, the prediction accuracy of this model is the best for orchard (R2 = 0.86 and MSE = 0.004%), good for dry land (R2 = 0.74 and MSE = 0.008%) and paddy field (R2 = 0.66 and MSE = 0.009%). The prediction model of SOC content is effective and can provide support for the application of remote sensing data to soil property monitoring.

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

“…Sentinel-1A, as freely available SAR data, offers better opportunities for soil property estimation. Most of the current studies use a single optical image combined with SAR data, thus obtaining better results [ 81 , 82 , 83 ]. Combinations including more sensor data should be considered in the future, but the uniformity of spatial resolution between different sensors will be a challenge for image processing by image calculation and mapping.…”

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

“…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]. Over very large areas or at national scales, other authors used coarse resolution satellite series, being either MODIS with 250 or 500 m resolution [61,62] or Sentinel-3 equipped with the Ocean and Land Colour Instrument (OLCI) with 300 m resolution [63].…”

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

“…The second example is the Soil Composite Mapping Processor (ScMAP) that del ered exposed soil masks that were run on high-performance local computing clusters [4 Leveraging the application of current multispectral EO data for mapping croplan soils several applications have been recorded on the regional [43][44][45], national [46], an continental scale [30]. A significant number of these studies were implemented in Bra [47], India [48], Indonesia [49], and China [50] since detailed information about soils abundant in those countries. This data were acquired to address the challenges generat by the cropland expansion there during recent decades.…”

“…Microwave (1 mm to 1 m) RS has also been used to effectively monitor soil moisture and roughness. Many multi-temporal approaches have recently been applied [50,67,68] using synthetic aperture radar-derived products to infer disturbance to soil reflectance due to the presence of moisture. However, given the local nature of disturbances, many of these studies provide site-specific information.…”