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A Remotely Sensed Framework for Spatially-Detailed Dryland Soil Organic Matter Mapping: Coupled Cross-Wavelet Transform with Fractional Vegetation and Soil-Related Endmember Time Series

文献类型： 外文期刊

作者： Sun, Qiangqiang 1 ; Zhang, Ping 1 ; Jiao, Xin 1 ; Lun, Fei 1 ; Dong, Shiwei 3 ; Lin, Xin 1 ; Li, Xiangyu 1 ; Sun, Danfeng 1 ;

作者机构： 1.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China

2.China Agr Univ, Res Ctr Land Use & Management, Beijing 100193, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Beijing 100097, Peoples R China

关键词： soil organic matter; dryland systems; cross-wavelet transform; endmember fraction; time series

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

ISSN：

年卷期： 2022 年 14 卷 7 期

页码：

收录情况： SCI

摘要： Soil organic matter (SOM) plays pivotal roles in characterizing dryland structure and function; however, remotely sensed spatially-detailed SOM mapping in these regions remains a challenge. Various digital soil mapping approaches based on either single-period remote sensing or spectral indices in other ecosystems usually produce inaccurate, poorly constrained estimates of dryland SOM. Here, a framework for spatially-detailed SOM mapping was proposed based on cross-wavelet transform (XWT) that exploits ecologically meaningful features from intra-annual fractional vegetation and soil-related endmember records. In this framework, paired green vegetation (GV) and soil-related endmembers (i.e., dark surface (DA), saline land (SA), sand land (SL)) sequences were adopted to extract 30 XWT features in temporally and spatially continuous domains of cross-wavelet spectrum. We then selected representative features as exploratory covariates for SOM mapping, integrated with four state-of-the-art machine learning approaches, i.e., ridge regression (RR), least squares-support vector machines (LS-SVM), random forests (RF), and gradient boosted regression trees (GBRT). The results reported that SOM maps from 13 coupled filtered XWT features and four machine learning approaches were consistent with soil-landscape knowledge, as evidenced by a spatially-detailed gradient from oasis to barren. This framework also presented more accurate and reliable results than arithmetically averaged features of intra-annual endmembers and existing datasets. Among the four approaches, both RF and GBRT were more appropriate in the XWT-based framework, showing superior accuracy, robustness, and lower uncertainty. The XWT synthetically characterized soil fertility from the consecutive structure of intra-annual vegetation and soil-related endmember sequences. Therefore, the proposed framework improved the understanding of SOM and land degradation neutrality, potentially leading to more sustainable management of dryland systems.

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