The refined spatiotemporal representation of soil organic matter based on remote images fusion of Sentinel-2 and Sentinel-3

• Published in: International journal of applied earth observation and geoinformation 2020-07, Vol.89, p.102094, Article 102094
• Main Authors: Lin, Chen, Zhu, A-Xing, Wang, Zhaofei, Wang, Xiaorui, Ma, Ronghua
• Format: Article
• Language: English
• Subjects: autumn; Environmental factors; Estimation model; Remote sensing; Sentinel 2/3; Soil organic matter; spatial data; spring; summer; winter
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2020.102094

•The refined spatiotemporal features of SOM were estimated according to remote sensing fusion.•The estimation accuracies of SOM are influenced by actual SOM contents.•The SOM content demonstrated similar spatial features in different temporal scales. Remote sensing technology is important for soil organic matter (SOM) estimation, but existing studies have mainly relied on a single data source. This limitation makes it difficult to simultaneously ensure high spatial resolution, high spectral accuracy and refined temporal granularity simultaneously, which cannot meet the requirements of the spatiotemporal dynamics representation. This study aimed to introduce a new remote sensing image source into SOM modeling and spatiotemporal estimation generated by fusing together Sentinel-2 and Sentinel-3 remote sensing images that have a 5-day revisit cycle; 10 m spatial resolution; and 21 different bands in blue, green, red and NIR spectral ranges. According to the image fusion process, a total of 52 available images were acquired between November 2016 and December 2018 in Donghai County, China. The fused images were used for SOM estimation model associated with 107 field samples. The results indicated that, first, the optimal model consisted of the band reflectivity (B20) and RVI (B18/B9), which were derived from the fused images, and the R2 approached 0.7 in the two phases of the synchronized data. Second, the modeling accuracy was influenced to some extent by the actual SOM content. The R2 values exceeded 0.75 when the SOM content was higher than 24 g/kg, while the R2 was even lower than 0.35 when the SOM content was lower. Third, the averaged SOM contents remained stable in general, while the seasonal variances can also be found during the two-year interval. The SOM contents maintained a low level during autumn and winter, while higher SOM levels were found in the spring and summer. Finally, the spatial variations could be described as ‘low in the west and high in the east’. In summary, the spatiotemporal dynamics of SOM highlighted the necessity of modeling with fused remote sensing images, and more effective modeling could be expected with the continued increase in SOM in future.