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建立了珠江口2个试验区遥感测深的多种统计相关模型,并选取相关性最好的模型进行水深反演和结果分析,探讨了不同悬沙浓度情况下遥感测深的可能性和实用性.结果表明,对于悬浮泥沙浓度大的试验区1,实测水深值和遥感各波段DN值的相关性<0.5,实际反演水深的能力较差,加入了泥沙因子的统计相关模型相关性有较大提高,但水深反演精度仍不高,达不到实际应用的精度;而悬浮泥沙浓度较小的试验区2,实测水深值和遥感各波段DN值的相关性基本大于0.7,但加入泥沙因子后水深值和遥感波段的相关系数并没有提高,以B2为反演因子的指数模型反演精度最高,5~10 m水深段的反演效果最好,平均相对误差为22.5%、平均绝对误差为1.56 m,模型总体平均相对误差为31.9%、总体平均绝对误差为1.92m,反演结果较好地反映了试验区2的水深情况.从所建模型来看,非线性模型的反演效果均好于相应的线性模型,多因子模型好于单因子模型.“,”We established a variety of statistical correlation models between water depth and remote sensing image in this research.A best model was selected for feasibility study in two experimental zones with different concentrations of suspended sediment in the Pearl River Estuary.In the testing area 1 with high suspension,the correlation between the observed water depth and the band DN values of remote sensing image was less than 0.5,and the capacity to define the actual water depth was poor.The correlation of the statistical model was much improved after taking the sediment factor into account,but the precision of remote sensing water depth remain low and beyond the required precision in application.In the testing area 2,where sediment concentration is low,the correlation between the observed water depth and the band DN values of remote sensing image was higher than 0.7,but there is no improvement after taking the sediment factor into consideration.The precision of exponential model was the highest using B2 as variable and the best results were gained in the area with water depth at 5-10m.The mean-relative-error was 22.5% and the mean-absolute-error was 1.56m,with an overall mean-relative-error of 31.9% and a mean-absolute-error of 1.92m.It means that the water depth derived from remote sensing are more satisfied in the testing area 2.As the conclusion,the nonlinear model was better than the linear model,and the multiple factor model better than single factor model.