PREDICTIVE MODELS OF TURBIDITY AND WATER DEPTH IN THE DO?ANA MARSHES USING LANDSAT TM AND ETM+ IMAGES

摘要

We have used Landsat-5 TM and Landsat-7 ETM+ images together with simultaneous ground-truth data at sample points in the Do?ana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38 % of variance in ground-truth data and included as predictors band 3 (630-690 nm), band 5 (1550-1750 nm) and the ratio between bands 1 (450-520 nm) and 4 (760-900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir river and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter a model simple using band 3 reflectance explains 75 % of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520-600 nm) and band 4, and bottom soil reflectance in band 4 in September when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Do?ana wetlands during the last 30 years using the Landsat satellite images time series.