Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019
The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/20/4970 |
| _version_ | 1797572432988471296 |
|---|---|
| author | Fan Zhang Lijuan Zhang Yanjiao Zheng Shiwen Wang Yutao Huang |
| author_facet | Fan Zhang Lijuan Zhang Yanjiao Zheng Shiwen Wang Yutao Huang |
| author_sort | Fan Zhang |
| collection | DOAJ |
| description | The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 μm, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 μm in northeast China, 2001–2019. The mean value was 441.78 μm, with an annual increase of 0.26 μm/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 μm, followed by January and February with 450.77 μm and 417.78 μm, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 μm to 497.141 μm. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow–ice albedo and their radiative forcing effect. |
| first_indexed | 2024-03-10T20:56:12Z |
| format | Article |
| id | doaj.art-f71ce2f740b944cc8ffae726bfb7ab3c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:56:12Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f71ce2f740b944cc8ffae726bfb7ab3c2023-11-19T17:59:00ZengMDPI AGRemote Sensing2072-42922023-10-011520497010.3390/rs15204970Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019Fan Zhang0Lijuan Zhang1Yanjiao Zheng2Shiwen Wang3Yutao Huang4Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaHeilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, ChinaJiangsu Key Laboratory of Agricultural Meteorology, School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaHeilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, ChinaHeilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, ChinaThe size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 μm, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 μm in northeast China, 2001–2019. The mean value was 441.78 μm, with an annual increase of 0.26 μm/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 μm, followed by January and February with 450.77 μm and 417.78 μm, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 μm to 497.141 μm. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow–ice albedo and their radiative forcing effect.https://www.mdpi.com/2072-4292/15/20/4970snow grain sizeART modelMODISnortheast China |
| spellingShingle | Fan Zhang Lijuan Zhang Yanjiao Zheng Shiwen Wang Yutao Huang Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019 Remote Sensing snow grain size ART model MODIS northeast China |
| title | Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019 |
| title_full | Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019 |
| title_fullStr | Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019 |
| title_full_unstemmed | Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019 |
| title_short | Remote Sensing-Based Simulation of Snow Grain Size and Spatial–Temporal Variation Characteristics of Northeast China from 2001 to 2019 |
| title_sort | remote sensing based simulation of snow grain size and spatial temporal variation characteristics of northeast china from 2001 to 2019 |
| topic | snow grain size ART model MODIS northeast China |
| url | https://www.mdpi.com/2072-4292/15/20/4970 |
| work_keys_str_mv | AT fanzhang remotesensingbasedsimulationofsnowgrainsizeandspatialtemporalvariationcharacteristicsofnortheastchinafrom2001to2019 AT lijuanzhang remotesensingbasedsimulationofsnowgrainsizeandspatialtemporalvariationcharacteristicsofnortheastchinafrom2001to2019 AT yanjiaozheng remotesensingbasedsimulationofsnowgrainsizeandspatialtemporalvariationcharacteristicsofnortheastchinafrom2001to2019 AT shiwenwang remotesensingbasedsimulationofsnowgrainsizeandspatialtemporalvariationcharacteristicsofnortheastchinafrom2001to2019 AT yutaohuang remotesensingbasedsimulationofsnowgrainsizeandspatialtemporalvariationcharacteristicsofnortheastchinafrom2001to2019 |