Comparison of global and zonal modeling strategies - A case study of soil organic matter and C:N ratio mapping in Altay, Xinjiang, China

Hongwu Liang; Guli Japaer; Tao Yu; Liancheng Zhang; Bojian Chen; Kaixiong Lin; Tongwei Ju; Yongyu Zhao; Ting Pei; Yimuranzi Aizizi

Ecological Informatics (Dec 2024)

Comparison of global and zonal modeling strategies - A case study of soil organic matter and C:N ratio mapping in Altay, Xinjiang, China

Hongwu Liang,
Guli Japaer,
Tao Yu,
Liancheng Zhang,
Bojian Chen,
Kaixiong Lin,
Tongwei Ju,
Yongyu Zhao,
Ting Pei,
Yimuranzi Aizizi

Affiliations

Hongwu Liang: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
Guli Japaer: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China; Sino-Belgian Joint Laboratory of Geo-information, 9000 Ghent, Belgium; Corresponding author at: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
Tao Yu: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China; Sino-Belgian Joint Laboratory of Geo-information, 9000 Ghent, Belgium
Liancheng Zhang: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China; Sino-Belgian Joint Laboratory of Geo-information, 9000 Ghent, Belgium
Bojian Chen: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
Kaixiong Lin: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
Tongwei Ju: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
Yongyu Zhao: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
Ting Pei: School of Tourism, Kashi University, Kashi 844000, China
Yimuranzi Aizizi: School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

Journal volume & issue: Vol. 84
p. 102882

Abstract

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Digital soil mapping (DSM) based on remote sensing is the dominant method for soil monitoring. Currently, the global modeling strategy (GMS) is used in most soil mapping studies. In the GMS, it is assumed that the relationship between soil and the landscape is the same throughout a region. However, the soil–landscape relationship varies in different geographic zones, such as among different land cover types. In the zonal modeling strategy (ZMS), a region is divided into multiple geographic zones on the basis of zoning rules, and each geographic zone is modeled individually, to fully capture the soil–landscape relationships within different zones. This study was conducted in Altay, Xinjiang, China. The soil organic matter (SOM) content and C:N ratio were mapped on the basis of the GMS and the ZMS to compare the performance differences between the two strategies. The ZMS mapping results exhibited better spatial heterogeneity across different land cover types. Moreover, the ZMS mapping results displayed lower uncertainty and were closer to the observed values than were the GMS results, which included more outliers. Overall, we recommend the ZMS. The accuracy validation results indicated that the accuracy of the ZMS is not necessarily higher than that of the GMS in some zones, but the overall accuracy is similar. Combining similar zones for modeling can improve the accuracy of the ZMS, surpassing that of the GMS. Moreover, the importance of synthetic aperture radar (SAR) data was analyzed. The results revealed that SAR data are highly important for mapping the SOM of bare land and cropland and the C:N ratio of bare land and forest. SAR data may provide soil nutrient information indirectly from moisture levels; therefore, we believe that SAR data have great potential for soil nutrient mapping.

Published in Ecological Informatics

ISSN: 1574-9541 (Print); 1878-0512 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Technology: Technology (General): Industrial engineering. Management engineering: Information technology; Science: Biology (General): Ecology
Website: https://www.sciencedirect.com/journal/ecological-informatics

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