Articles | Volume 19, issue 11
https://doi.org/10.5194/gmd-19-4775-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-4775-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A unified Hapke-HSR + MARMIT-2 soil radiative transfer model for reflectance simulation under varying moisture conditions
Anxin Ding
Key Laboratory of Humid Subtropical Eco-Geographical Process (Ministry of Education), Fujian Normal University, Fuzhou 350000, China
School of Geographical Sciences, School of Carbon Neutrality Future Technology, Fujian Normal University, Fuzhou 350000, China
Jockey Club STEM Laboratory of Quantitative Remote Sensing, Department of Geography, University of Hong Kong, Hong Kong 999077, China
Jockey Club STEM Laboratory of Quantitative Remote Sensing, Department of Geography, University of Hong Kong, Hong Kong 999077, China
Ziti Jiao
State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Faculty of Geographical Science, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China
Alexander A. Kokhanovsky
Philipps-Universität Marburg Department of Geography Laboratory for Climatology and Remote Sensing F|14, Deutschhausstr. 12, 35032 Marburg, Germany
Hanyu Shi
School of Geospatial Artificial Intelligence, East China Normal University, Shanghai 200241, China
Rui Xie
International Institute for Earth System Sciences, and School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
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Earth Syst. Sci. Data, 15, 2055–2079, https://doi.org/10.5194/essd-15-2055-2023, https://doi.org/10.5194/essd-15-2055-2023, 2023
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Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
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Earth Syst. Sci. Data, 15, 869–895, https://doi.org/10.5194/essd-15-869-2023, https://doi.org/10.5194/essd-15-869-2023, 2023
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Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
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Jianglei Xu, Shunlin Liang, and Bo Jiang
Earth Syst. Sci. Data, 14, 2315–2341, https://doi.org/10.5194/essd-14-2315-2022, https://doi.org/10.5194/essd-14-2315-2022, 2022
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Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang
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Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
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Short summary
Wet soil are difficult to model because water changes how sunlight interacts with soil surfaces and alters reflectance in different viewing directions. We developed a unified physical model to simulate soil reflectance under varying moisture conditions. The results show that soil moisture not only darkens soil but also changes their directional reflectance, especially in the near-infrared region. This work can improve soil moisture retrieval and future remote sensing applications.
Wet soil are difficult to model because water changes how sunlight interacts with soil surfaces...