[Effect of remotely sensed data errors on the retrieving accuracy of territorial parameters--a case study on chlorophyll a concentration inversion of Taihu Lake].
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 microg x 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.
Chen
Jun
J
The Key Laboratory of Marine Hydrocarbon Resource and Environment Geology, Qingdao 266071, China. chenjun820711@163.com
Zhou
Guan-Hua
GH
Wen
Zhen-He
ZH
Fu
Jun
J
D000465:Algorithms; D002681:China; D002734:Chlorophyll / Q000032:analysis*; D000077194:Chlorophyll A; D004784:Environmental Monitoring; D060106:Lakes; D008962:Models, Theoretical; D058998:Remote Sensing Technology
D002734:Chlorophyll; D000077194:Chlorophyll A