A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM
Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental va...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/18/4601 |
| _version_ | 1797577174367076352 |
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| author | Bingru Tian Hua Chen Xin Yan Sheng Sheng Kangling Lin |
| author_facet | Bingru Tian Hua Chen Xin Yan Sheng Sheng Kangling Lin |
| author_sort | Bingru Tian |
| collection | DOAJ |
| description | Satellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental variables, satellite precipitation estimations, and rain gauge observations for the calibration of satellite precipitation data. First, the GPM data are downscaled from 0.1° to 0.01° based on the seasonal RF models to minimize the spatial differences between the satellite estimations and the rain gauge observations. Secondly, the fusion model combining ConvLSTM and CBAM explores the spatiotemporal correlation of downscaled satellite precipitation data with environmental co-variables and ground-based observations to correct GPM precipitation. The integrated scheme (CBAM-ConvLSTM) is applied to acquire monthly precipitation at a spatial resolution of 0.01° over Hanjiang River Basin from 2014 to 2018. Comparative analyses of model-based satellite products with in situ observations show that model-based precipitation products have a high-resolution spatial distribution along with high accuracy, which combines the advantages of in situ observations and satellite products. Compared to the original GPM product, the evaluation metric values of the merged precipitation products all improved: the <i>RMSE</i> decreased by 31% while the <i>CC</i> increased from 0.55 to 0.69, the bias decreased from about 25% to less than 1.8%, and the <i>MAE</i> decreased by 27.8% while the <i>KGE</i> increased from 0.28 to 0.52. This two-step scheme provides an effective way to derive a high-resolution and accurate monthly precipitation product for humid regions. |
| first_indexed | 2024-03-10T22:04:15Z |
| format | Article |
| id | doaj.art-5f94880cb2064aef90afe053bf431dae |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T22:04:15Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5f94880cb2064aef90afe053bf431dae2023-11-19T12:50:02ZengMDPI AGRemote Sensing2072-42922023-09-011518460110.3390/rs15184601A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTMBingru Tian0Hua Chen1Xin Yan2Sheng Sheng3Kangling Lin4State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, ChinaAnhui Survey and Design Institute of Water Resources and Hydropower Co., Ltd., Hefei 230088, ChinaState Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, ChinaState Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, ChinaSatellite products have mediocre performance in precipitation estimation, while rain gauges are incapable of describing continuous spatial precipitation distributions. To obtain spatially continuous and accurate precipitation data, this paper proposes a two-step scheme incorporating environmental variables, satellite precipitation estimations, and rain gauge observations for the calibration of satellite precipitation data. First, the GPM data are downscaled from 0.1° to 0.01° based on the seasonal RF models to minimize the spatial differences between the satellite estimations and the rain gauge observations. Secondly, the fusion model combining ConvLSTM and CBAM explores the spatiotemporal correlation of downscaled satellite precipitation data with environmental co-variables and ground-based observations to correct GPM precipitation. The integrated scheme (CBAM-ConvLSTM) is applied to acquire monthly precipitation at a spatial resolution of 0.01° over Hanjiang River Basin from 2014 to 2018. Comparative analyses of model-based satellite products with in situ observations show that model-based precipitation products have a high-resolution spatial distribution along with high accuracy, which combines the advantages of in situ observations and satellite products. Compared to the original GPM product, the evaluation metric values of the merged precipitation products all improved: the <i>RMSE</i> decreased by 31% while the <i>CC</i> increased from 0.55 to 0.69, the bias decreased from about 25% to less than 1.8%, and the <i>MAE</i> decreased by 27.8% while the <i>KGE</i> increased from 0.28 to 0.52. This two-step scheme provides an effective way to derive a high-resolution and accurate monthly precipitation product for humid regions.https://www.mdpi.com/2072-4292/15/18/4601precipitation bias correctionsatellite productGPMconvolutional block attention moduleconvolutional long short-term memory |
| spellingShingle | Bingru Tian Hua Chen Xin Yan Sheng Sheng Kangling Lin A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM Remote Sensing precipitation bias correction satellite product GPM convolutional block attention module convolutional long short-term memory |
| title | A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM |
| title_full | A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM |
| title_fullStr | A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM |
| title_full_unstemmed | A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM |
| title_short | A Downscaling–Merging Scheme for Monthly Precipitation Estimation with High Resolution Based on CBAM-ConvLSTM |
| title_sort | downscaling merging scheme for monthly precipitation estimation with high resolution based on cbam convlstm |
| topic | precipitation bias correction satellite product GPM convolutional block attention module convolutional long short-term memory |
| url | https://www.mdpi.com/2072-4292/15/18/4601 |
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