Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China
Soil moisture is a key parameter for the circulation of water and energy exchange between surface and the atmosphere, playing an important role in hydrology, agriculture, and meteorology. Traditional methods for monitoring soil moisture suffer from spatial discontinuity, time-consuming processes, an...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/17/4214 |
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| author | Quanshan Liu Zongjun Wu Ningbo Cui Xiuliang Jin Shidan Zhu Shouzheng Jiang Lu Zhao Daozhi Gong |
| author_facet | Quanshan Liu Zongjun Wu Ningbo Cui Xiuliang Jin Shidan Zhu Shouzheng Jiang Lu Zhao Daozhi Gong |
| author_sort | Quanshan Liu |
| collection | DOAJ |
| description | Soil moisture is a key parameter for the circulation of water and energy exchange between surface and the atmosphere, playing an important role in hydrology, agriculture, and meteorology. Traditional methods for monitoring soil moisture suffer from spatial discontinuity, time-consuming processes, and high costs. Remote sensing technology enables the non-destructive and efficient retrieval of land information, allowing rapid soil moisture monitoring to schedule crop irrigation and evaluate the irrigation efficiency. Satellite data with different resolutions provide different observation scales. Evaluating the accuracy of estimating soil moisture based on open and free satellite data, as well as exploring the comprehensiveness and adaptability of different satellites for soil moisture temporal and spatial observations, are important research contents of current soil moisture monitoring. The study utilized three types of satellite data, namely GF-1, Landsat-8, and GF-4, with respective temporal and spatial resolutions of 16 m (every 4 days), 30 m (every 16 days), and 50 m (daily). The gray relational analysis (GRA) was employed to identify vegetation indices that selected sensitivity to soil moisture at varying depths (3 cm, 10 cm, and 20 cm). Then, this study employed random forest (RF), Extra Tree (ETr), and linear regression (LR) algorithms to estimate soil moisture at different depths with optical satellite data sources. The results showed that the accuracy of soil moisture estimation was different at different growth stages. The model accuracy exhibited an upward trend during the middle and late growth stages, coinciding with higher vegetation coverage; however, it demonstrated a decline in accuracy during the early and late growth stages due to either the absence or limited presence of vegetation. Among the three satellite images, the vegetation indices derived from GF-1 exhibited were more sensitive to vegetation characteristics and demonstrated superior soil moisture estimation accuracy (with R<sup>2</sup> ranging 0.129–0.928, RMSE ranging 0.017–0.078), followed by Landsat-8 (with R<sup>2</sup> ranging 0.117–0.862, RMSE ranging 0.017–0.088). The soil moisture estimation accuracy of GF-4 was the worst (with R<sup>2</sup> ranging 0.070–0.921, RMSE ranging 0.020–0.140). Thus, GF-1 is suitable for vegetated areas. In addition, the ETr model outperformed the other models in both accuracy and stability (ETr model: R<sup>2</sup> ranging from 0.117 to 0.928, RMSE ranging from 0.021 to 0.091; RF model: R<sup>2</sup> ranging from 0.225 to 0.926, RMSE ranging from 0.019 to 0.085; LR model: R<sup>2</sup> ranging from 0.048 to 0.733, RMSE ranging from 0.030 to 0.144). Utilizing GF-1 is recommended to construct the ETr model for assessing soil moisture variations in the farming land of northern China. Therefore, in cases where there are limited ground sample data, it is advisable to utilize high-spatiotemporal-resolution remote sensing data, along with machine learning algorithms such as ETr and RF, which are suitable for small samples, for soil moisture estimation. |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:14:37Z |
| publishDate | 2023-08-01 |
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| series | Remote Sensing |
| spelling | doaj.art-af4b163da84a46d7bf0a2d08e5bea5762023-11-19T08:46:03ZengMDPI AGRemote Sensing2072-42922023-08-011517421410.3390/rs15174214Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern ChinaQuanshan Liu0Zongjun Wu1Ningbo Cui2Xiuliang Jin3Shidan Zhu4Shouzheng Jiang5Lu Zhao6Daozhi Gong7State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaSoil moisture is a key parameter for the circulation of water and energy exchange between surface and the atmosphere, playing an important role in hydrology, agriculture, and meteorology. Traditional methods for monitoring soil moisture suffer from spatial discontinuity, time-consuming processes, and high costs. Remote sensing technology enables the non-destructive and efficient retrieval of land information, allowing rapid soil moisture monitoring to schedule crop irrigation and evaluate the irrigation efficiency. Satellite data with different resolutions provide different observation scales. Evaluating the accuracy of estimating soil moisture based on open and free satellite data, as well as exploring the comprehensiveness and adaptability of different satellites for soil moisture temporal and spatial observations, are important research contents of current soil moisture monitoring. The study utilized three types of satellite data, namely GF-1, Landsat-8, and GF-4, with respective temporal and spatial resolutions of 16 m (every 4 days), 30 m (every 16 days), and 50 m (daily). The gray relational analysis (GRA) was employed to identify vegetation indices that selected sensitivity to soil moisture at varying depths (3 cm, 10 cm, and 20 cm). Then, this study employed random forest (RF), Extra Tree (ETr), and linear regression (LR) algorithms to estimate soil moisture at different depths with optical satellite data sources. The results showed that the accuracy of soil moisture estimation was different at different growth stages. The model accuracy exhibited an upward trend during the middle and late growth stages, coinciding with higher vegetation coverage; however, it demonstrated a decline in accuracy during the early and late growth stages due to either the absence or limited presence of vegetation. Among the three satellite images, the vegetation indices derived from GF-1 exhibited were more sensitive to vegetation characteristics and demonstrated superior soil moisture estimation accuracy (with R<sup>2</sup> ranging 0.129–0.928, RMSE ranging 0.017–0.078), followed by Landsat-8 (with R<sup>2</sup> ranging 0.117–0.862, RMSE ranging 0.017–0.088). The soil moisture estimation accuracy of GF-4 was the worst (with R<sup>2</sup> ranging 0.070–0.921, RMSE ranging 0.020–0.140). Thus, GF-1 is suitable for vegetated areas. In addition, the ETr model outperformed the other models in both accuracy and stability (ETr model: R<sup>2</sup> ranging from 0.117 to 0.928, RMSE ranging from 0.021 to 0.091; RF model: R<sup>2</sup> ranging from 0.225 to 0.926, RMSE ranging from 0.019 to 0.085; LR model: R<sup>2</sup> ranging from 0.048 to 0.733, RMSE ranging from 0.030 to 0.144). Utilizing GF-1 is recommended to construct the ETr model for assessing soil moisture variations in the farming land of northern China. Therefore, in cases where there are limited ground sample data, it is advisable to utilize high-spatiotemporal-resolution remote sensing data, along with machine learning algorithms such as ETr and RF, which are suitable for small samples, for soil moisture estimation.https://www.mdpi.com/2072-4292/15/17/4214soil moisturemultisource remote sensingmachine learningfarming land |
| spellingShingle | Quanshan Liu Zongjun Wu Ningbo Cui Xiuliang Jin Shidan Zhu Shouzheng Jiang Lu Zhao Daozhi Gong Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China Remote Sensing soil moisture multisource remote sensing machine learning farming land |
| title | Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China |
| title_full | Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China |
| title_fullStr | Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China |
| title_full_unstemmed | Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China |
| title_short | Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China |
| title_sort | estimation of soil moisture using multi source remote sensing and machine learning algorithms in farming land of northern china |
| topic | soil moisture multisource remote sensing machine learning farming land |
| url | https://www.mdpi.com/2072-4292/15/17/4214 |
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