Spatial prediction of soil organic matter content using multiyear synthetic images and partitioning algorithms

•The performance of bare soil images in different periods to predict soil organic matter (SOM) is different.•The performance of landsat-8 synthetic images in SOM mapping are better than sentinel-2 synthetic images.•The local regression method based on image partition algorithms can improve the accur...

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Vydáno v:Catena (Giessen) Ročník 211; s. 106023
Hlavní autoři: Luo, Chong, Wang, Yiang, Zhang, Xinle, Zhang, Wenqi, Liu, Huanjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.04.2022
Témata:
agricultural land
algorithms
catenas
China
cloud cover
Google Earth Engine
Internet
Landsat
Multiyear synthetic
Partitioning
prediction
regression analysis
Soil Organic Matter
soil types
sustainable development
China
Partitioning
Soil Organic Matter
Google Earth Engine
Multiyear synthetic
ISSN:0341-8162, 1872-6887
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Shrnutí:•The performance of bare soil images in different periods to predict soil organic matter (SOM) is different.•The performance of landsat-8 synthetic images in SOM mapping are better than sentinel-2 synthetic images.•The local regression method based on image partition algorithms can improve the accuracy of SOM mapping.•The mapping effect of SOM prediction model based on soil type partitioning is affected by the sample distribution. Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development. However, due to the strong spatial variability in soil, there is still a lack of robust SOM mapping methods. The use of remote sensing satellite technology to map soil parameters has been effectively applied in many areas. Soil mapping in Northeast China with less cloud cover, higher planting intensity and longer bare soil period of cultivated land is expected. In this study, multiyear Landsat 8 and Sentinel-2 images of bare soil periods were used to generate median synthetic images according to different time intervals in Google Earth Engine (GEE). Then, the spectral index and image band were used as input variables, and the prediction accuracies of different combinations were evaluated by the random forest (RF) algorithm. Finally, the best combination of two partitioning methods (based on different soil types and cascade simple K-means clustering) was used for SOM prediction of local regression and mapping. The results show that 1) the best time window for SOM prediction in the Songnen Plain is in May, but precipitation will affect the prediction accuracy of SOM; 2) Sentinel-2 synthetic images are not superior to Landsat 8 synthetic images for SOM mapping, although Sentinel-2 has better temporal and spatial resolution; and 3) compared with the global regression model, the local regression method based on two partitioning methods can improve the accuracy of SOM mapping, but the actual mapping effect based on the soil type partitioning algorithms is affected by the distribution of soil samples. This study extends the application of GEE to soil mapping and improve the accuracy of large-scale regional SOM mapping by partitioning images.
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ISSN:0341-8162
1872-6887
DOI:10.1016/j.catena.2022.106023