Effect of Remotely Sensed Data Errors on the Retrieving Accuracy of Territorial Parameters ——a Case Study on Chlorophyll a Concentration Inversion of Taihu Lake
1. The Key Laboratory of Marine Hydrocarbon Resource and Environment Geology, Qingdao 266071, China 2. Qingdao Marine Geosciences Institute, Qingdao 266071, China 3. School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China
Abstract：The errors of the territorial parameters retrieved from remote sensing are decided by the data error and the model error. The data error is not simply added to the total errors of retrieval results. It would be reformed by the quantitative inversion model, and then, combined with the model errors and melts into the totals errors. Accordingly, during the quantitative process, taking advantage of the highest correlation coefficient or the least root mean square error as assessment standard for describing the chlorophyll a concentration vs remote sensing parameters is not reasonable. Focusing on the above problem, the study pointed out that the reason why the result of the optimized cost function is contrary with the practical is that different model has different influence on data errors. Combined with the in situ measurements of Taihu Lake, in October, 2003, it is known that due to the error magnification phenomena (TM2/TM3 algorithm is 2.28 times more than TM2/TM1 algorithm), although the regression coefficient of TM2/TM3 algorithm is higher than TM2/TM1 algorithm, the quantitative errors of TM2/TM3 algorithm are 7.938 5 μg·L-1 more than TM2/TM1 algorithm. Moreover, the retrieval results show that distribution pattern of the results of TM2/TM3 algorithm is completely opposite to the TM2/TM1 algorithm. According to the former research achievements, the results of TM2/TM1 algorithm would be more reasonable. In summary, only when that the factor of data error is added to the optimized cost function is taken as a constrain condition in search for the optimal solution of the quantitative models, would the retrieval results be more reliable.
Key words：Data error;Quantitative remote sensing;Cost function;Taihu lake
陈 军1, 2，周冠华3*，温珍河1, 2，付 军1, 2 . 遥感数据误差对地表参数定量反演可靠性的影响——以太湖叶绿素a反演为例[J]. 光谱学与光谱分析, 2010, 30(05): 1347-1351.
CHEN Jun1, 2，ZHOU Guan-hua3*，WEN Zhen-he1, 2，FU Jun1，2 . Effect of Remotely Sensed Data Errors on the Retrieving Accuracy of Territorial Parameters ——a Case Study on Chlorophyll a Concentration Inversion of Taihu Lake . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(05): 1347-1351.
[1] Gordon H R, Brown O B, Jacors M M. Applied Optics, 1975, 14: 417. [2] Deng M, Li Y. International Journal of Remote Sensing, 2003, 24(3): 519. [3] LI Xia(黎 夏). Renote Sensing of Environment China(环境遥感), 1992, 7(2): 106. [4] Gordon H R, Morel A Y. Remote Assessment of Ocean Color for Interpretation of Satellite Visable Image, 2nd ed. New York: Springer-Verlag, 1983. [5] Rousseeuw P J, Leroy A M. Robust Regression and Outlier Detection[M]. John Wiley & Sons., 1987. [6] Ding K Y, Gordon H R. Applied Optics, 1994, 33(30): 7096. [7] Gordon H R, Diego J C. Applied Optics, 1987, 26(11): 2111. [8] Deschamps P Y, Herman M, Tanre D. Applied Optic, 1983, 22(23): 3751. [9] Mueller J L, Austin R W. Ocean Optics Protocols for SeaWiFS Validation: NASA Techmical Memorandum 104566. Greenbelt, MD, NASA Goddard Space Flight Center, 1992. [10] TANG Jun-wu, TIAN Guo-liang, WANG Xiao-yong (唐军武，田国良，汪小勇). Journal of Remote Sensing(遥感学报), 2004, 8(1): 37. [11] IOCGG. Remote Sensing of Ocean Color in Coastal, and other Optically-Complex, Waters. Reports of the International Ocean-Colour Coordinating Group, No.3. Sathyendranath S. Dartmouth, Canada, IOCGG, 2000. [12] Ian J, Stephen B G. Journal of Experimental Marine Biology and Ecology, 2000, 250: 233. [13] Gomez R A, Boxall S R, Wekks A R. International Journal Remote Sensing, 2001, 22(15): 2867. [14] Carder K L, Steward R G. Limnol Oceanogr, 1985, 30(2): 286. [15] WEN Jian-guang, XIAO Qing, LIU Qin-huo, et al(闻建光, 肖 青, 柳钦火, 等). Science Geographica Sinica China(地理科学), 2007, 27(1): 92.
