泛第三極大數(shù)據(jù)系統(tǒng)

1、泛第三極大數(shù)據(jù)系統(tǒng)Big Data System for Pan-Third Pole標題：祁連山地區(qū)日值0.050.05地表土壤水分數(shù)據(jù)（SMHiRes, V2）Title：Daily 0.050.05 Land Surface Soil Moisture Dataset of Qilian Mountain Area (SMHiRes, V2)摘要：本數(shù)據(jù)集包括祁連山地區(qū)2019年日值0.050.05地表土壤水分產(chǎn)品。采用耦合小波分析的隨機森林優(yōu)化降尺度模型（RF-OWCM），通過對“祁連山地區(qū)基于AMSR-E和AMSR2亮溫數(shù)據(jù)的SMAP時間擴展日0.250.25地表土壤水分數(shù)據(jù)（SMs

2、mapTE, V1）”進行降尺度，得到0.050.05地表土壤水分產(chǎn)品。參與降尺度模型的數(shù)據(jù)包括GLASS Albedo/LAI/FVC，周紀-中國西部1km全天候地表溫度數(shù)據(jù)（TRIMS LST-TP），以及經(jīng)/緯度等信息。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.縮略圖 祁連山地區(qū)2019年8月1日0.05度地表土壤水分數(shù)據(jù)0.05-degree Land surface soil moisture in August 1st, 2019 of Qilian Mountain Area數(shù)據(jù)文件命名方式和使用方法：文件命名約定：YYYYMMDD.tiff（YYYY：年，MM：月，DD：日）數(shù)據(jù)版本號：V1投影：+proj=longl

5、at +datum=WGS84 +no_defs數(shù)據(jù)格式：GeoTIFF, 220行360列土壤水分單位：cm3/cm3土壤水分有效值范圍：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?。簲?shù)據(jù)集209M，單個文件588Kb時間范圍: 2019資助項目：泛第三極環(huán)境變化與綠色絲綢之路建設專項（XDA20100101）數(shù)據(jù)來源：該數(shù)據(jù)集采用耦合小波分析的隨機森林降尺度模型（RF-OWCM），通過對“祁連山地區(qū)基于AMSR-E和AMSR2亮溫數(shù)據(jù)的SMAP時間擴展日0.250.25地表土壤水分數(shù)據(jù)（SMsmapTE, V1）”進行降尺度，得到0.050.05地表土壤水分產(chǎn)品。參與降尺度模型的數(shù)據(jù)包括GLASS

7、 Albedo/LAI/FVC，周紀-中國西部1km全天候地表溫度數(shù)據(jù)LST（TRIMS LST-TP）等。算法中用到的數(shù)據(jù)分別來自：SMsmapTE：中科院地球大數(shù)據(jù)共享服務系統(tǒng)（ HYPERLINK / /）GLASS Albedo/LAI/FVC: 全球陸表特征參量產(chǎn)品（ HYPERLINK ）LST: 由電子科技大學周紀提供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空間范圍（東、西、南、北經(jīng)緯度）：89E - 107E，34N - 45N時間分辨率：日空間分辨率：0.050.05數(shù)據(jù)引用文獻/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)參考文獻/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. 數(shù)據(jù)使用聲明：為尊重知識產(chǎn)權、保障數(shù)據(jù)作者