The Daily kernel-driven BRDF model coefficients retrieved from 5-days-composited multi-sensory data coupling topograpic effects over the Tibet Plateau (2016)
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
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). National Tibetan Plateau Data Center, DOI: 10.11888/Meteoro.tpdc.271196. CSTR: 18406.11.Meteoro.tpdc.271196. (Download the reference： RIS | Bibtex )Related Literatures:
1. Wu, S.B., Wen*, J.G., Gastellu-Etchegorry, J.P., Liu, Q.H., You, D.Q., Xiao, Q., Hao, D.L., Lin, X.W., Yin, T.G. (2019). The definition of remotely sensed reflectance quantities suitable for rugged terrain. Remote Sensing of Environment, 225, 403–415.( View Details | Bibtex)
2. Wen, J.G., Liu, Q., Xiao, Q., Liu, Q.H., You, D.Q., Hao, D.L., Wu, S.B., Lin, X.W. (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. Wen, J.G., Dou, B.C., You, D.Q., Tang, Y., Xiao, Q., Liu, Q.H. (2017). Forward a Small-Time Scale BRDF/albedo by Multi-sensors Combined BRDF inversion (MCBI) model. IEEE Transaction of GeoScience and Remote Sensing, 22(5), 683-697.( View Details | Bibtex)
Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.
2nd survey of Tibet plateau (No:2019QZKK0206)
Development of Tibetan BRDF/Albedo retriveal algorithm coupling topographic effects and combining multi-sensory data in snow or snow-free cases repectively (No:41971316)
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