HiWATER: 1km/5day compositing Leaf Area Index (LAI) product of Heihe River Basin, 2015

HiWATER: 1km/5day compositing Leaf Area Index (LAI) product of Heihe River Basin, 2015


The 5-day Lai synthesis results in 2015 are provided by the 1 km / 5-day Lai data set of Heihe River Basin. The data set is constructed by using the data of Terra / MODIS, Aqua / MODIS, as well as the domestic satellites fy3a / MERSI and fy3b / MERSI to construct the multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. Multi-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which can be divided into first level data, second level data and third level data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide the basis for the selection of the optimal data set and the design of inversion algorithm flow. Leaf area index product inversion algorithm is designed to distinguish mountain land and vegetation type, using different neural network inversion model. Based on global DEM map and surface classification map, PROSAIL model is used for continuous vegetation such as grassland and crops, and gost model is used for forest and mountain vegetation. Using the reference map generated by the measured ground data of the forests in the upper reaches of Heihe River and the oasis in the middle reaches, and scaling up the corresponding high-resolution reference map to 1km resolution, compared with the Lai product, the product has a good correlation between the farmland and the forest area and the reference value, and the overall accuracy basically meets the accuracy threshold of 0.5%, 20% specified by GCOS. By cross comparing this product with Lais products such as MODIS, geov1 and glass, the accuracy of this Lai product is better than that of similar products compared with reference value. In a word, the synthetic Lai data set of 1km / 5 days in Heihe River Basin comprehensively uses multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products, so as to better serve the application of remote sensing data products.


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Li, J., Yin, G., Yin, G., Zhong, B., Yang, A., 吴俊君., Wu, S. (2016). HiWATER: 1km/5day compositing Leaf Area Index (LAI) product of Heihe River Basin, 2015. National Tibetan Plateau Data Center, DOI: 10.3972/hiwater.338.2016.db. CSTR: 18406.11.hiwater.338.2016.db. (Download the reference: RIS | Bibtex )

Related Literatures:

1. Li, X., Liu, S.M., Xiao, Q., Ma, M.G., Jin, R., Che, T., Wang, W.Z., Hu, X.L., Xu, Z.W., Wen, J.G., Wang, L.X. (2017). A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system. Scientific Data, 4, 170083. doi:10.1038/sdata.2017.83.( View Details | Download | Bibtex)

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


References literature

1.Li, X., Cheng, G.D., Liu, S.M., Xiao, Q., Ma, M.G., Jin, R., Che, T., Liu, Q.H., Wang, W.Z., Qi, Y., Wen, J.G., Li, H.Y., Zhu, G.F., Guo, J.W., Ran, Y.H., Wang, S.G., Zhu, Z.L., Zhou, J., Hu, X.L., & Xu, Z.W. (2013). Heihe watershed allied telemetry experimental research (hiwater): scientific objectives and experimental design. Bulletin of the American Meteorological Society, 94(8), 1145-1160. doi:10.1175/BAMS-D-12-00154.1. (View Details )

2.Li, X., Liu, S.M., Ma, M.G., Xiao, Q., Liu, Q.H., & Jin, R., et al. (2012). (2012). Hiwater:an integrated remote sensing experiment on hydrological and ecological processes in the heihe river basin. Advances in Earth Science, 27(5), 481-498. (View Details | Download )


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Keywords
Geographic coverage
East: 101.96 West: 97.11
South: 37.74 North: 42.69
Details
  • File size: 67 MB
  • Views: 8,442
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  • Access: Open Access
  • Temporal coverage: 2015-01-14 To 2016-01-13
  • Updated time: 2021-04-18
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