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.
Highlights
Terrestrial ecosystem is an important carbon pool, most of which is stored in soil [1].There is an exchange of carbon dioxide (CO2 ) between the atmosphere and soil, which leads to the sequestration or emission of CO2 by soil [2]
Remote sensing data were used to predict soil organic carbon (SOC) content based on multiple land use types, while the results showed that the accuracy of SOC prediction using satellite remote sensing data was lower than that using airborne remote sensing and laboratory reflectance spectra data [19,45]
For different land use types, the average contents of SOC in orchard and paddy field were higher. This is because the time of soil sampling in orchard is in the fertilization period after blood orange picking, whereas paddy field is conducive to the accumulation of SOC in the process of cultivation and fertilization [57]
Summary
Terrestrial ecosystem is an important carbon pool, most of which is stored in soil [1].There is an exchange of carbon dioxide (CO2 ) between the atmosphere and soil, which leads to the sequestration or emission of CO2 by soil [2]. Is closely related to soil fertility, plant growth, conservation of soil and water, aggregate stability, and soil nutrient cycling capability [3]. Both natural factors (soil type, climate, and topography) and human activities (land use and farming practices) affect the content of SOC [4]. The carbon storage in soil is limited and the carbon content in the soil will gradually reach saturation with the increase of SOC storage [5]. It is necessary to monitor the content of SOC. In order to solve these problems, scholars gradually began to explore the use of robust and cost-effective approaches to predict SOC content
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