Land cover information quality enhancement by integrating optical and radar satellite data in support of hydrological modeling
In order to follow-up the evolution of land-cover in the Cau river watershed (northern Vietnam), SPOT-4 HRVIR 2 images were acquired at the end of November 2007 and at the beginning of January 2008. Initially, the hierarchical classification with class-related features approach has been developed and applied in order to extract seven land-cover classes relevant to hydrological modeling, without distinguishing between rice fields and other types of crop. This approach has proven to be effective, with overall classification accuracy of 91%. A significant decrease in confusion between urban areas, farmland and plantations was noted compared to a prior classification of a LANDSAT-7 image acquired in 2003 which had an overall accuracy of 78%. Subsequently, in order to distinguish paddy fields from other crop types, two series of RADARSAT-2 images which cover the centre region of the watershed were acquired during the second half of 2009 and the first half of 2010. One series of images were acquired as dual-polarization (like-polarization HH and cross-polarization HV) in standard mode (S5) and the others as quad-polarization in fine mode (FQ21). These images were acquired over a period which covers two rice growing periods, including the traditional crop season and the spring crop season. For dual polarization images (S5), a suitable thresholding method was applied to the ratio between images acquired at the beginning of the season and at the mid-season. This method detected 74% of rice fields in the traditional rice season and 63% of rice fields in the spring rice season, compared with statistical data from the province of Thai Nguyen. The overall classification accuracy for all land-cover classes is 71% for the traditional rice season and 67% for the spring rice season. As to the polarimetric images (FQ21), the coherence matrix was chosen to identify the rice fields of a heterogeneous zone. The classification was performed using the SVM (Support Vector Machine) algorithm. The latter information was then integrated into the thematic map derived from SPOT-4 images to obtain the final map. This map is directly used for hydrological modeling. Calculations of water requirements for rice cultivation have shown that rice fields take a large proportion of the total flow in the two sub-watersheds used as examples. These calculations show the importance of detecting rice fields when the realization of the realistic hydrological simulations on the Cau river watershed and to confirm the hypothesis that the identification of rice fields on the land-cover map improves assessments and predictions related to the hydrological cycle.