Abstract
Soil organic matter (SOM) content is an effective indicator of desertification; thus, monitoring its spatial‒temporal changes on a large scale is important for combating desertification. However, mapping SOM content in desertified land is challenging owing to the heterogeneous landscape, relatively low SOM content and vegetation coverage. Here, we modeled the SOM content in topsoil (0–20 cm) of desertified land in northern China by employing a high spatial resolution dataset and machine learning methods, with an emphasis on quarterly green and non-photosynthetic vegetation information, based on the Google Earth Engine (GEE). The results show: 1) the machine learning model performed better than the traditional multiple linear regression model (MLR) for SOM content estimation, and the Random Forest (RF) model was more accurate than the Support Vector Machine (SVM) model; 2) the quarterly information regarding green vegetation and non-photosynthetic were identified as key covariates for estimating the SOM content in desertified land, and an obvious improvement could be observed after simultaneously combining the Dead Fuel Index (DFI) and Normalized Difference Vegetation Index (NDVI) of the four quarters (R2 increased by 0.06, the root mean square error decreased by 0.05, the ratio of prediction deviation increased by 0.2, and the ratio of performance to interquartile distance increased by 0.5). In particular, the effects of the DFI in Q1 (the first quarter) and Q2 (the second quarter) on estimating low SOM content (<1%) were identified; finally, a timely (2019) and high spatial resolution (30 m) SOM content map for the desertified land in northern China was drawn which shows obvious advantages over existing SOM products, thus providing key data support for monitoring and combating desertification.
Highlights
Desertification is defined as land degradation in drylands resulting from various factors, including climatic variations and human activities (UNCCD, 1994), which has a major effects on carbon emissions through the loss of soil organic matter (SOM) when soil deplete from their original state (MEA – Millennium Ecosystem Assessment, 2005)
The results show that the quarterly information of the green and non-photosynthetic vegetation was identified as a key covariate for SOM content estimation in desertified land, and an obvious improvement was observed after simultaneously combining the Dead fuel index (DFI) and Normalized Difference Vegetation Index (NDVI) of the four quarters (i.e., R2 increased by 0.06, the root mean square error (RMSE) decreased by 0.05, ratio of prediction deviation (RPD) increased by 0.2, and ratio of performance to interquartile distance (RPIQ) increased by 0.5)
The importance of DFI in Q1 and Q2 for low SOM content estimation was illustrated in this study
Summary
Desertification is defined as land degradation in drylands resulting from various factors, including climatic variations and human activities (UNCCD, 1994), which has a major effects on carbon emissions through the loss of soil organic matter (SOM) when soil deplete from their original state (MEA – Millennium Ecosystem Assessment, 2005). Accurate and timely SOM content mapping is of great importance for assessing desertification (Sims et al, 2019). Few researches have focused on the large scale SOM content estimation of the desertified land of Northern China (Li et al, 2018; Sun et al, 2019). Soil sampling is the traditional method for determining the regional SOM content. The method yields more accurate results, it requires considerable effort, material resources, and time, especially for large areas (Wang et al, 2017; Hamzehpour et al, 2019; Lee et al, 2019; Yang et al, 2020). Digital Soil Mapping (DSM) (McBratney et al, 2003) is efficient and convenient for acquiring soil information based on mathematical or statistical relationships between field soil observations and related predictor variables (e.g., climate, vegetation, relief, parent material, and time) (Zhao et al, 2014; Liang et al, 2019b; Keskin et al, 2019)
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