Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine

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.

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

“…In addition, the results showed that the spatial distribution of soil properties can be successfully estimated with machine learning algorithms by using remote sensing data and environmental variables. The finding confirmed that, similar to the conclusions of Zhang et al [ 100 ], ANNs can be used to interpret the nonlinear relationship between SOC content and ancillary variables. In addition, the spatial distribution map of SOC content generated by ANN-kriging was significantly affected by the auxiliary variables and revealed more details than other interpolation methods in the northern, central, southwestern and southeastern parts of the study area.…”

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

“…Many researchers have thus turned to RS imagery and/or ML to map soil organic matter, but there is still some difficulty in selecting the right input data or ML model for prediction. To determine how different datasets and ML models perform on GEE in predicting soil organic matter, an ANN, RF, and SVR model was compared in [222] with MODIS, Sentinel-2A, and DEM data as input.…”

“…However, the authors found that a lack of cloud-free VHR imagery led Appendix C.12. Textual Summaries for Soil To determine how different datasets and ML models perform in predicting soil organic matter, the authors in [222] compared an ANN, RF, and SVR model with MODIS, Sentinel-2A, and DEM data as input. They found that for all models, Sentinel-2A data were better for model performance due to its higher spectral and spatial resolution.…”

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

“…Since the 1970s, a series of satellites (e.g., Landsat, SPOT, and Sentinel) equipped with multispectral sensors have been put into operation. From then, satellite remote sensing images have been widely used in large-scale SSC and SOM estimation for their convenient acquisition, easy processing, and large coverage area [ 22 , 23 ]. Ma et al used Sentinel-1A and Sentinel-2A data to retrieve the distribution map of soil salinization in the Ogan-Kuqa River Oasis located in the Tarim Basin in Xinjiang, China [ 24 ].…”