Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water management
Multi-layer soil moisture is an important factor in predicting agricultural droughts and waterlogging, with significant implications for the growth, development, and yield prediction of rain fed crops. However, soil moisture datasets or algorithms fail to simultaneously meet the requirements of mult...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Agricultural Water Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425000678 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832557565985161216 |
|---|---|
| author | Liwen Chen Boting Hu Jingxuan Sun Y. Jun Xu Guangxin Zhang Hongbo Ma Jingquan Ren |
| author_facet | Liwen Chen Boting Hu Jingxuan Sun Y. Jun Xu Guangxin Zhang Hongbo Ma Jingquan Ren |
| author_sort | Liwen Chen |
| collection | DOAJ |
| description | Multi-layer soil moisture is an important factor in predicting agricultural droughts and waterlogging, with significant implications for the growth, development, and yield prediction of rain fed crops. However, soil moisture datasets or algorithms fail to simultaneously meet the requirements of multi-layer, high spatiotemporal resolution soil moisture information for large-scale agricultural production areas. To fill this gap, we propose a novel framework for estimation high spatial resolution multi-layer soil moisture data. Firstly, utilizing the Google Earth Engine (GEE) platform and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), we achieve the fusion of multi-source remote sensing data at large scales to obtain high spatiotemporal resolution Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data. Secondly, leveraging the Extreme Gradient Boosting (XGBoost) model along with reanalysis and in-situ measurements, we estimate soil moisture information across depths of 0–100 cm depth by 10 cm interval over large geographical extents. Finally, the accuracy of the soil moisture model is assessed using metrics such as Pearson correlation coefficient, root mean square error (RMSE), unbiased RMSE (ubRMSE), and bias. To assess the applicability of our research methodology, we selected the typical black soil zone in Northeast of China, which is one of the four major black soil regions globally and characterized by intensive agricultural activities. We estimated the long-term time series of soil moisture information during the growing seasons from 2000 to 2020 in this study area. We found that the soil moisture simulation based on the XGBoost model the worst values of R, RMSE, ubRMSE, and Bias values for the training set are 0.86，1.49，1.49 and −0.039 respectively. For the validation set, the worst value of R is 0.83. The proposed methodology in this study enables the acquisition of soil moisture information with both large-scale coverage and high spatiotemporal resolution. This advancement holds significant promise for fine-scale research and applications in agricultural, hydrological, and environmental fields. |
| format | Article |
| id | doaj-art-1c841ff287484cb3ba295403b6111b9c |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-1c841ff287484cb3ba295403b6111b9c2025-02-03T04:16:29ZengElsevierAgricultural Water Management1873-22832025-03-01309109353Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water managementLiwen Chen0Boting Hu1Jingxuan Sun2Y. Jun Xu3Guangxin Zhang4Hongbo Ma5Jingquan Ren6School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China; Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, ChinaSchool of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, USANortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; Corresponding author.Jilin Meteorological Observatory, Changchun 130062, ChinaInstitute of Meteorological Science of Jilin Province, Changchun 130062, ChinaMulti-layer soil moisture is an important factor in predicting agricultural droughts and waterlogging, with significant implications for the growth, development, and yield prediction of rain fed crops. However, soil moisture datasets or algorithms fail to simultaneously meet the requirements of multi-layer, high spatiotemporal resolution soil moisture information for large-scale agricultural production areas. To fill this gap, we propose a novel framework for estimation high spatial resolution multi-layer soil moisture data. Firstly, utilizing the Google Earth Engine (GEE) platform and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), we achieve the fusion of multi-source remote sensing data at large scales to obtain high spatiotemporal resolution Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data. Secondly, leveraging the Extreme Gradient Boosting (XGBoost) model along with reanalysis and in-situ measurements, we estimate soil moisture information across depths of 0–100 cm depth by 10 cm interval over large geographical extents. Finally, the accuracy of the soil moisture model is assessed using metrics such as Pearson correlation coefficient, root mean square error (RMSE), unbiased RMSE (ubRMSE), and bias. To assess the applicability of our research methodology, we selected the typical black soil zone in Northeast of China, which is one of the four major black soil regions globally and characterized by intensive agricultural activities. We estimated the long-term time series of soil moisture information during the growing seasons from 2000 to 2020 in this study area. We found that the soil moisture simulation based on the XGBoost model the worst values of R, RMSE, ubRMSE, and Bias values for the training set are 0.86，1.49，1.49 and −0.039 respectively. For the validation set, the worst value of R is 0.83. The proposed methodology in this study enables the acquisition of soil moisture information with both large-scale coverage and high spatiotemporal resolution. This advancement holds significant promise for fine-scale research and applications in agricultural, hydrological, and environmental fields.http://www.sciencedirect.com/science/article/pii/S0378377425000678Soil moistureMachine learningESTARFMMulti-layerBlack soil region |
| spellingShingle | Liwen Chen Boting Hu Jingxuan Sun Y. Jun Xu Guangxin Zhang Hongbo Ma Jingquan Ren Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water management Agricultural Water Management Soil moisture Machine learning ESTARFM Multi-layer Black soil region |
| title | Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water management |
| title_full | Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water management |
| title_fullStr | Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water management |
| title_full_unstemmed | Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water management |
| title_short | Using remote sensing and machine learning to generate 100-cm soil moisture at 30-m resolution for the black soil region of China: Implication for agricultural water management |
| title_sort | using remote sensing and machine learning to generate 100 cm soil moisture at 30 m resolution for the black soil region of china implication for agricultural water management |
| topic | Soil moisture Machine learning ESTARFM Multi-layer Black soil region |
| url | http://www.sciencedirect.com/science/article/pii/S0378377425000678 |
| work_keys_str_mv | AT liwenchen usingremotesensingandmachinelearningtogenerate100cmsoilmoistureat30mresolutionfortheblacksoilregionofchinaimplicationforagriculturalwatermanagement AT botinghu usingremotesensingandmachinelearningtogenerate100cmsoilmoistureat30mresolutionfortheblacksoilregionofchinaimplicationforagriculturalwatermanagement AT jingxuansun usingremotesensingandmachinelearningtogenerate100cmsoilmoistureat30mresolutionfortheblacksoilregionofchinaimplicationforagriculturalwatermanagement AT yjunxu usingremotesensingandmachinelearningtogenerate100cmsoilmoistureat30mresolutionfortheblacksoilregionofchinaimplicationforagriculturalwatermanagement AT guangxinzhang usingremotesensingandmachinelearningtogenerate100cmsoilmoistureat30mresolutionfortheblacksoilregionofchinaimplicationforagriculturalwatermanagement AT hongboma usingremotesensingandmachinelearningtogenerate100cmsoilmoistureat30mresolutionfortheblacksoilregionofchinaimplicationforagriculturalwatermanagement AT jingquanren usingremotesensingandmachinelearningtogenerate100cmsoilmoistureat30mresolutionfortheblacksoilregionofchinaimplicationforagriculturalwatermanagement |