Abstract
Many studies have attempted to predict soil organic matter (SOM), whereas mapping high-precision and high-resolution SOM maps remains a challenge due to the difficulty of selecting appropriate satellite data sources and prediction algorithms. This study aimed to investigate the influence of different remotely sensed images and machine learning algorithms on SOM prediction. We constructed two comparative experiments, i.e., full-band and common-band variable datasets of Sentinel-2A and MODIS images using Google Earth Engine (GEE). The predictive performances of random forest (RF), artificial neural network (ANN), and support vector regression (SVR) algorithms were evaluated, and the SOM map was generated for the Songnen Plain. Results showed that the model based on the full-band Sentinel-2A dataset achieved the best performance. The application of Sentinel-2A data resulted in mean relative improvements (RIs) of 7.67% and 5.87%, respectively. The RF achieved a lower root mean squared error (RMSE = 0.68%) and a higher coefficient of determination (R2 = 0.67) in all of the predicted scenarios than ANN and SVR. The resultant SOM map accurately characterized the SOM spatial distribution. Therefore, the Sentinel-2A data have obvious advantages over MODIS due to their higher spectral and spatial resolutions, and the combination of the RF algorithm and GEE is an effective approach to SOM mapping.
Highlights
Soil is an integral part of terrestrial ecosystems and plays a vital role in the circulation of energy and materials between the atmosphere and the biosphere [1,2]
In two sets of comparative experiments, we found that the model performances of the three machine learning algorithms based on Sentinel-2A
In two sets of comparative experiments, we found that the model performances of the three machine learning algorithms based on Sentinel-2A images were better than those of the algorithms based on the Moderate Resolution Imaging Spectroradiometer (MODIS) images
Summary
Soil is an integral part of terrestrial ecosystems and plays a vital role in the circulation of energy and materials between the atmosphere and the biosphere [1,2]. Soil organic matter (SOM) is an important component of soil that affects soil fertility and carbon sequestration [3,4,5,6,7] and plays an important role in improving crop yield [8,9,10] and maintaining agricultural sustainability and food security [11]. Accurately predicting and quantifying the spatial distribution of SOM is vital for promoting the improvement of soil fertility, the development of a sustainable regional ecosystem, and the implementation of soil management policies [12]. SOM prediction models and accomplishing mapping of spatial distribution, it remains a challenge to accurately map SOM content due to the difficulty of selecting appropriate satellite data sources and prediction algorithms for a specific region. 4.0/).
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