Development and validation of a new MODIS snow-cover-extent product over China
<p>Based on MOD09GA/MYD09GA surface reflectance data, a new MODIS snow-cover-extent (SCE) product from 2000 to 2020 over China has been produced by the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains two preliminar...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-04-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/26/1937/2022/hess-26-1937-2022.pdf |
| _version_ | 1818049208717934592 |
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| author | X. Hao X. Hao G. Huang G. Huang G. Huang Z. Zheng Z. Zheng X. Sun X. Sun W. Ji H. Zhao J. Wang J. Wang H. Li H. Li X. Wang |
| author_facet | X. Hao X. Hao G. Huang G. Huang G. Huang Z. Zheng Z. Zheng X. Sun X. Sun W. Ji H. Zhao J. Wang J. Wang H. Li H. Li X. Wang |
| author_sort | X. Hao |
| collection | DOAJ |
| description | <p>Based on MOD09GA/MYD09GA surface reflectance data, a new MODIS snow-cover-extent (SCE) product from 2000 to 2020 over China has been
produced by the Northwest Institute of Eco-Environment and Resources
(NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains
two preliminary clear-sky SCE datasets – Terra-MODIS and Aqua-MODIS SCE
datasets and a final daily cloud-gap-filled (CGF) SCE dataset. The first two datasets are generated mainly through optimizing snow-cover discriminating rules over land-cover types, and the latter dataset is produced after a series of gap-filling
processes such as aggregating the two preliminary datasets, reducing cloud
gaps with adjacent information in space and time, and eliminating all gaps
with auxiliary data. The validation against 362 China Meteorological
Administration (CMA) stations shows that during snow seasons the overall
accuracy (OA) values of the three datasets are larger than 93 %, all of the omission error (OE) values are constrained within 9 %, and all of the commission error (CE) values are constrained within 10 %. Bias values of 0.98, 1.02, and 1.03 demonstrate on a whole that there is no significant overestimation nor a significant underestimation. Based on the same ground reference data, we found that the new product accuracies are obviously higher than standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For example, compared with the CE of 23.78 % that the MYD10A1 product shows, the CE of the new Aqua-MODIS SCE dataset is 6.78 %; the OA of the new CGF SCE dataset is up to 93.15 % versus 89.54 % of MOD10A1F product and 84.36 % of MYD10A1F product. Besides, as expected, snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates that the OA has increased by 3–10 percentage points, the OE has dropped by 1–8 percentage points, and the CE has dropped by 4–21 percentage points. Therefore, our product has virtually provided more reliable snow knowledge over China; thereby, it can better serve for hydrological, climatic, environmental, and other related studies there.</p> |
| first_indexed | 2024-12-10T10:33:56Z |
| format | Article |
| id | doaj.art-b484712daabe40fda0eb5b5c91c1478f |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-10T10:33:56Z |
| publishDate | 2022-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-b484712daabe40fda0eb5b5c91c1478f2022-12-22T01:52:30ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382022-04-01261937195210.5194/hess-26-1937-2022Development and validation of a new MODIS snow-cover-extent product over ChinaX. Hao0X. Hao1G. Huang2G. Huang3G. Huang4Z. Zheng5Z. Zheng6X. Sun7X. Sun8W. Ji9H. Zhao10J. Wang11J. Wang12H. Li13H. Li14X. Wang15Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, ChinaNational Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, ChinaKey Laboratory of Radiometric Calibration and Validation for Environmental satellites, China Meteorological Administration, Beijing 100081, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaEngineering Laboratory for National Geographic State Monitoring, Lanzhou Jiaotong University, Lanzhou 730070, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaHeihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCollege of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China<p>Based on MOD09GA/MYD09GA surface reflectance data, a new MODIS snow-cover-extent (SCE) product from 2000 to 2020 over China has been produced by the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains two preliminary clear-sky SCE datasets – Terra-MODIS and Aqua-MODIS SCE datasets and a final daily cloud-gap-filled (CGF) SCE dataset. The first two datasets are generated mainly through optimizing snow-cover discriminating rules over land-cover types, and the latter dataset is produced after a series of gap-filling processes such as aggregating the two preliminary datasets, reducing cloud gaps with adjacent information in space and time, and eliminating all gaps with auxiliary data. The validation against 362 China Meteorological Administration (CMA) stations shows that during snow seasons the overall accuracy (OA) values of the three datasets are larger than 93 %, all of the omission error (OE) values are constrained within 9 %, and all of the commission error (CE) values are constrained within 10 %. Bias values of 0.98, 1.02, and 1.03 demonstrate on a whole that there is no significant overestimation nor a significant underestimation. Based on the same ground reference data, we found that the new product accuracies are obviously higher than standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For example, compared with the CE of 23.78 % that the MYD10A1 product shows, the CE of the new Aqua-MODIS SCE dataset is 6.78 %; the OA of the new CGF SCE dataset is up to 93.15 % versus 89.54 % of MOD10A1F product and 84.36 % of MYD10A1F product. Besides, as expected, snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates that the OA has increased by 3–10 percentage points, the OE has dropped by 1–8 percentage points, and the CE has dropped by 4–21 percentage points. Therefore, our product has virtually provided more reliable snow knowledge over China; thereby, it can better serve for hydrological, climatic, environmental, and other related studies there.</p>https://hess.copernicus.org/articles/26/1937/2022/hess-26-1937-2022.pdf |
| spellingShingle | X. Hao X. Hao G. Huang G. Huang G. Huang Z. Zheng Z. Zheng X. Sun X. Sun W. Ji H. Zhao J. Wang J. Wang H. Li H. Li X. Wang Development and validation of a new MODIS snow-cover-extent product over China Hydrology and Earth System Sciences |
| title | Development and validation of a new MODIS snow-cover-extent product over China |
| title_full | Development and validation of a new MODIS snow-cover-extent product over China |
| title_fullStr | Development and validation of a new MODIS snow-cover-extent product over China |
| title_full_unstemmed | Development and validation of a new MODIS snow-cover-extent product over China |
| title_short | Development and validation of a new MODIS snow-cover-extent product over China |
| title_sort | development and validation of a new modis snow cover extent product over china |
| url | https://hess.copernicus.org/articles/26/1937/2022/hess-26-1937-2022.pdf |
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