The Daily kernel-driven BRDF model coefficients retrieved from 5-days-composited multi-sensory data coupling topograpic effects over the Tibet Plateau (2016)

The Daily kernel-driven BRDF model coefficients retrieved from 5-days-composited multi-sensory data coupling topograpic effects over the Tibet Plateau (2016)

This daily land surface kernel-driven BRDF model's coeciffients proudct is with a spatl resolution of 0.02 ° x 0.02 ° over the Tibet Plateau in 2016. Multi-sensory data is used to retrieve the the kernel-driven BRDF model and coupled with topographic effects, and prior knowledge is introduced for quality control inversion. The high-precision BRDF of good spatial-temporal continiuty is retrieved by combining MODIS reflectance data (a polar orbiting satellite) and himawari-8 AHI land surface reflectance (a geostationary satellite ). MODIS lans surface reflectance data and AHI TOA reflectance data are downloaded from the official websites. After registration, atmospheric correction and other processing, the daily resolution BRDF is synthesized with a period of 5 days. Compared with similar products, it has more advantages in capturing rapidly changing surface features, and has better temporal and spatial continuity with the shortest composition period. It can effectively support angular effects correction and the BRDF-releated parameters' retrieval.

File naming and required software

File naming convention: Kernelyddd005.dat (yyyy: year, DDD: Day (Julian day)), such as kernel2016001005.dat, which represents the albedo of the Qinghai Tibet Plateau on the first day of 2016
Data reading mode: stored in bare data format, can be opened by envi, ArcGIS and other software, when the header file is Kernelheadfile.hdr , band 1-21 is the coefficient; when read the band of quality identification, using the header file is QAheadfile.hdr which is band 43.
Data version number: v1
Projection + proj = longlat + datum = WGS84
Data format: raw, 1250 rows × 750 columns
Fill value: 32767
Scale: nuclear coefficient band / 10000 (divided by 10000, it is the nuclear coefficient);
Band Description: band 1-21 is the kernel coefficients of MODIS seven reflectance bands, and every three bands correspond to the coefficients of MODIS one observation band, such as 1-3 bands are the isotropic-kernel coefficient, volume scattering-kernel coefficient and geometric-optical kernel coefficient of MODIS band 1. See Kernelheadfile.hdr .
Quality identification Description: 16 bits, 00-01 -- overall quality identification, 0 = good, 1 = acceptable, 2 = poor, 3 = unavailable (fill value); 02-03 -- surface type, 00 = vegetation, 01 = non vegetation, non ice and snow, 10 = ice and snow, 11 = unable to judge; 04 -- surface state change, 0 = rapid change, 1 = non rapid change; 05-07 -- reflectivity observation times for inversion, 0 = 0, 1 = 1-2, 2 = 3-7 times, 3 = 8-14 times, 4 = 15-21 times, 5 = 22-28 times, 6 = 29-35 times, 7 = > 36 times; 08 -- satellite data source, 0 = single sensor, 1 = multi-sensor; / / 09-10 -- terrestrial water body identification, 0 = land, 1 = mixed land and water body, or inland water body, or coastal zone, 2 = offshore and deep sea (NO production), 3 = not within the projection range (NO production). Using QAheadfile.hdr

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Cite as:

Wen, J., Tang, Y., You, D. (2021). The Daily kernel-driven BRDF model coefficients retrieved from 5-days-composited multi-sensory data coupling topograpic effects over the Tibet Plateau (2016). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Meteoro.tpdc.271196. CSTR: 18406.11.Meteoro.tpdc.271196. (Download the reference: RIS | Bibtex )

Related Literatures:

1. Wen, J. G., B. C. Dou, D. Q. You, Y. Tang, Q. Xiao, Q. Liu and L. Qinhuo (2017). "Forward a Small-Timescale BRDF/Albedo by Multisensor Combined BRDF Inversion Model." Ieee Transactions on Geoscience and Remote Sensing 55(2): 683-697.( View Details | Bibtex)

2. Wen, Jianguang; Liu, Qiang; Xiao, Qing; Liu, Qinhuo; You, Dongqin; Hao, Dalei; Wu, Shengbiao; Lin, Xingwen. 2018. "Characterizing Land Surface Anisotropic Reflectance over Rugged Terrain: A Review of Concepts and Recent Developments." Remote Sens.10, no. 3: DOI: 10.3390/rs10030370.( View Details | Bibtex)

3. Shengbiao Wu , Jianguang Wen*, Jean-Philippe Gastellu-Etchegorry , Qinhuo Liu, Dongqin You , Qing Xiao , DaLei Hao , Xingwen Lin, Tiangang Yin, 2019. The definition of remotely sensed reflectance quantities suitable for rugged terrain,Remote Sensing of Environment, 225,40( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

2nd survey of Tibet plateau (No:2019QZKK0206)

Algorithm development of Tibetan BRDF/Albedo from multi-sensory data coupling topographic effects in cases of snow or snow-free repectitively (No:41971316)

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License: This work is licensed under an Attribution 4.0 International (CC BY 4.0)

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Geographic coverage
East: 105.00 West: 80.00
South: 25.00 North: 40.00
  • Temporal resolution: Daily
  • Spatial resolution: 0.01º - 0.05º
  • File size: 78 MB
  • Views: 2617
  • Downloads: 442
  • Access: Open Access
  • Temporal coverage: 2016-01-01 To 2016-12-31
  • Updated time: 2022-04-18
: WEN Jianguang    TANG Yong   YOU Dongqin  

Distributor: A Big Earth Data Platform for Three Poles


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