Preprints
https://doi.org/10.5194/hess-2021-556
https://doi.org/10.5194/hess-2021-556

  22 Nov 2021

22 Nov 2021

Review status: this preprint is currently under review for the journal HESS.

Development and validation of a new MODIS snow-cover-extent product over China

Xiaohua Hao1,2, Guanghui Huang3,1,2, Zhaojun Zheng4,5, Xingliang Sun1,6, Wenzheng Ji1, Hongyu Zhao1, Jian Wang1,2, Hongyi Li1,2, and Xiaoyan Wang3 Xiaohua Hao et al.
  • 1Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
  • 2Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
  • 3College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  • 4National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
  • 5Key Laboratory of Radiometric Calibration and Validation for Environmental satellites, China Meteorological Administration, Beijing 100081, China
  • 6Engineering Laboratory for National Geographic State Monitoring, Lanzhou Jiaotong University, Lanzhou 730070, China

Abstract. Based on the MOD09GA/MYD09GA 500-m surface reflectance, a new MODIS snow-cover-extent (SCE) product 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 formers are generated mainly through optimizing snow-cover discriminating rules over different land-cover types, and the latter 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. Validation against 362 China Meteorological Administration (CMA) stations shows during snow seasons the overall accuracies (OA) of the three datasets are all larger than 93 %, the omission errors (OE) are all constrained within 9 %, and the commission errors (CE) are all constrained within 10 %. Biases ranging from the lowest 0.98 to the medium 1.02, to the largest 1.03 demonstrate on a whole the SCEs given by the new product are neither overestimated nor underestimated significantly. Based on the same ground reference data, we found the new product’s accuracies are clearly higher than those of standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For examples, compared with the CE of 23.78 % that the standard 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 the standard MOD10A1F product and 84.36 % of the standard MYD10A1F product. Besides, as expected snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates 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, and thereby can better serve for hydrological, climatic, environmental, and other related studies there.

Xiaohua Hao et al.

Status: open (until 17 Jan 2022)

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Xiaohua Hao et al.

Xiaohua Hao et al.

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Short summary
We develop and validate a new 20-year MODIS snow-cover-extent product over China, which is dedicated to addressing known problems of the standard snow products. As expected, the new product significantly outperforms the state-of-the-art MODIS C6.1 products; and improvements are particularly clear in forests and for the daily cloud-free product. Our product has provided more reliable snow knowledge over China, and can be accessible freely via https://dx.doi.org/10.11888/Snow.tpdc.271387.