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Remote estimation of K-d (PAR) using MODIS and Landsat imagery for turbid inland waters in Northeast China
K. S. Song, J. H. Ma, Z. D. Wen, C. Fang, Y. X. Shang, Y. Zhao, M. Wang and J. Du; Song KS(宋开山)
2017
Source PublicationIsprs Journal of Photogrammetry and Remote Sensing
Volume123Pages:159-172
AbstractLight availability for photosynthetically active radiation (PAR) is one of the major factors governing photosynthesis in aquatic ecosystems. Conventional measurements of light attenuation in the PAR domain (K-d(PAR)) is representative for only small areas of water body. Remotely sensed optical imagery can be utilized to monitor K-d(PAR) in large areas of water bodies, based on the relationship between water leaving radiance and K-d(PAR). In this study, six field surveys were conducted over 20 lakes (or reservoirs) across Northeast China from April to September 2015. In order to derive the K-d(PAR) at regional scale, the Landsat/TM/ETM+/OLI and the MODIS daily surface reflectance data (MOD09GA similar to 500 m, Bands 17) were used to establish empirical inversion models. Through multiple stepwise regression analysis, the band difference (Red-Blue) and band ratio (NIR/Red) were used in Landsat imagery modeling, and the band difference (Red-Blue) and ratio (Red/Blue) were used in MODIS imagery modeling. The accuracy of the two models was evaluated by 10-fold cross-validation in ten times. The results indicate that the models performed well for both Landsat (R-2 = 0.83, RMSE = 0.95, and MRE = 0.33), and MODIS (R-2 = 0.86, RMSE = 0.91, and MRE = 0.19) imagery. However, the K-d(PAR) derived by MODIS is slightly higher than that estimated by Landsat (slope = 1.203 and R-2 = 0.972). Consistency of model performance between the MODIS daily (MYDO9G A) and the 8-Day composite reflectance (MYDO9A1) data was verified by K-d(PAR) estimations and regression analysis (slope = 1.044 and R-2 = 0.966). Finally, the spatial and temporal distribution of K-d(PAR) in Northeast China indicated that specific geographical characteristics as well as meteorological alterations can influence K-d(PAR) calibrations. Specifically, we have revealed that the wind speed and algal bloom are the major determinants of K-d(PAR) in Lake Hulun (2050 km(2)) and Xingkai (4412 km(2)). (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
DOI10.1016/j.isprsjprs.2016.11.010
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iga.ac.cn/handle/131322/7538
Collection湿地与全球变化学科组
Corresponding AuthorSong KS(宋开山)
Recommended Citation
GB/T 7714
K. S. Song, J. H. Ma, Z. D. Wen, C. Fang, Y. X. Shang, Y. Zhao, M. Wang and J. Du,Song KS. Remote estimation of K-d (PAR) using MODIS and Landsat imagery for turbid inland waters in Northeast China[J]. Isprs Journal of Photogrammetry and Remote Sensing,2017,123:159-172.
APA K. S. Song, J. H. Ma, Z. D. Wen, C. Fang, Y. X. Shang, Y. Zhao, M. Wang and J. Du,&宋开山.(2017).Remote estimation of K-d (PAR) using MODIS and Landsat imagery for turbid inland waters in Northeast China.Isprs Journal of Photogrammetry and Remote Sensing,123,159-172.
MLA K. S. Song, J. H. Ma, Z. D. Wen, C. Fang, Y. X. Shang, Y. Zhao, M. Wang and J. Du,et al."Remote estimation of K-d (PAR) using MODIS and Landsat imagery for turbid inland waters in Northeast China".Isprs Journal of Photogrammetry and Remote Sensing 123(2017):159-172.
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