泛第三极大数据系统

1、泛第三极大数据系统Big Data System for Pan-Third Pole标题：祁连山地区日值0.050.05地表土壤水分数据（SMHiRes, V2）Title：Daily 0.050.05 Land Surface Soil Moisture Dataset of Qilian Mountain Area (SMHiRes, V2)摘要：本数据集包括祁连山地区2019年日值0.050.05地表土壤水分产品。采用耦合小波分析的随机森林优化降尺度模型（RF-OWCM），通过对“祁连山地区基于AMSR-E和AMSR2亮温数据的SMAP时间扩展日0.250.25地表土壤水分数据（SMs

2、mapTE, V1）”进行降尺度，得到0.050.05地表土壤水分产品。参与降尺度模型的数据包括GLASS Albedo/LAI/FVC，周纪-中国西部1km全天候地表温度数据（TRIMS LST-TP），以及经/纬度等信息。Abstract：This dataset contains daily 0.050.05 land surface soil moisture products in Qilian Mountain Area in 2019. The dataset was produced by utilizing the optimized wavelet-coupled-RF d

3、ownscaling model (RF-OWCM) to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.250.25 Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the downscaling model include GLASS Albedo/LAI/FVC, Thermal and Reanalysis Inte

4、grating Medium-resolution Spatial-seamless LST Tibetan Plateau (TRIMS LST-TP) by Ji Zhou and Lat/Lon information.缩略图 祁连山地区2019年8月1日0.05度地表土壤水分数据0.05-degree Land surface soil moisture in August 1st, 2019 of Qilian Mountain Area数据文件命名方式和使用方法：文件命名约定：YYYYMMDD.tiff（YYYY：年，MM：月，DD：日）数据版本号：V1投影：+proj=longl

5、at +datum=WGS84 +no_defs数据格式：GeoTIFF, 220行360列土壤水分单位：cm3/cm3土壤水分有效值范围：0.020.5填充值：NodataFile Naming Convention and data description：File Naming Convention: YYYYMMDD.tiff (YYYY: year, MM: month, DD: day)Data Version：V1Projection：+proj=longlat +datum=WGS84 +no_defsData Format: GeoTIFF, 220 rows 360 col

6、umesSoil Moisture Unit: cm3/cm3Soil Moisture Valid Range：0.02-0.5Filled Value：Nodata文件大小：数据集209M，单个文件588Kb时间范围: 2019资助项目：泛第三极环境变化与绿色丝绸之路建设专项（XDA20100101）数据来源：该数据集采用耦合小波分析的随机森林降尺度模型（RF-OWCM），通过对“祁连山地区基于AMSR-E和AMSR2亮温数据的SMAP时间扩展日0.250.25地表土壤水分数据（SMsmapTE, V1）”进行降尺度，得到0.050.05地表土壤水分产品。参与降尺度模型的数据包括GLASS

7、 Albedo/LAI/FVC，周纪-中国西部1km全天候地表温度数据LST（TRIMS LST-TP）等。算法中用到的数据分别来自：SMsmapTE：中科院地球大数据共享服务系统（ HYPERLINK / /）GLASS Albedo/LAI/FVC: 全球陆表特征参量产品（ HYPERLINK ）LST: 由电子科技大学周纪提供The dataset was produced by utilizing the the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the “AMSR-E and A

8、MSR2 TB-based SMAP Time-Expanded Daily 0.250.25 Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The datasets participating in the downscaling model were respectively obtained from:SMsmapTE：CAsearch（ HYPERLINK / /）GLASS Albedo/LAI/FVC: Global Land Surface Satellite product

9、s ( HYPERLINK )LST: Provided by Ji Zhou, University of Electronic Science and Technology空间范围（东、西、南、北经纬度）：89E - 107E，34N - 45N时间分辨率：日空间分辨率：0.050.05数据引用文献/Citation：Qu, Y., Zhu, Z., Montzka, C., Chai, L., Liu, S., Ge, Y., Liu, J., Lu, Z., He, X., & Zheng, J. (2021). Inter-comparison of several soil moi

10、sture downscaling methods over the Qinghai-Tibet Plateau, China. JOURNAL OF HYDROLOGY, 592, 125616. (/10.1016/j.jhydrol.2020.125616)参考文献/Reference：Zhao W, Snchez N, Lu H, Li A. A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. Journal o

11、f Hydrology, 2018, 563:1009-1024.Piles M, Petropoulos G P, Snchez N, et al. Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations. Remote Sensing of Environment, 2016,180:403-417.Merlin O, Escorihuela M J, Mayoral M A, et

12、 al. Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain. Remote Sensing of Environment, 2013,130(4):25-38.Im J, Park S, Rhee J, et al. Downscaling of AMSR-E soil moisture with MODIS products using machine learn

13、ing approaches. Environmental Earth Sciences, 2016,75(15):1120.Srivastava P K, Han D, Ramirez M R, et al. Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application. Water Resources Management, 2013,27(8):3127-3144.Xiao

14、Z, Liang S, Wang J, et al. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 209 - 223.Qu Y, Liu Q, Liang S, Wang L, Liu N, Liu S. Direct-estimation a

15、lgorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 907 - 919.Jia K, Liang S, Liu S, Li Y, et al. Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surf

16、ace reflectance. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(9): 4787 - 4796.Qu, Y., Zhu, Z., Chai, L., Liu, S., Montzka, C., Liu, J., Yang, X., Lu, Z., Jin, R., & Li, X. (2019). Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temper

17、ature and SMAP over the QinghaiTibet Plateau, China. Remote Sensing, 11, 683. Zhang, X., Zhou, J., Gttsche, F., Zhan, W., Liu, S., & Cao, R. (2019). A method based on temporal component decomposition for estimating 1-km all-weather land surface temperature by merging satellite thermal infrared and passive microwave observations. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 57, 4670-4691. 数据使用声明：为尊重知识产权、保障数据作者