Application of remote sensing technology on the drought monitoring of the Yangtze river basin

Kurzfassung

Drought is one of the most pervasive and complex natural hazards. The global warming significantly contributed to the ever severe drought all over the world, including the Yangtze River basin. The Yangtze River is one of the Chinese economy life lines. There is a need to understand its drought trend for the planning and scheduling of water resources, as well as the sustainability of the social development. Remote sensing technology is widely used for the drought monitoring in recent years because of its efficiency and relatively low cost. Various types of drought indexes derived from remote sensing data have been proposed. The combination of these drought indexes from different domains helps to draw a comprehensive picture of a drought. In particular, this thesis aims at combining the data of the optical sensor MODIS with the data of the passive microwave sensor SMOS to evaluate and monitor the soil moisture and the drought of the Yangtze River basin. The SMOS and MODIS data employed in this thesis are from June to September, 2015. Three remote sensing drought indexes, Vegetation Supply Water Index (VSWI), Perpendicular Drought Index (PDI), and Soil Moisture and Ocean Salinity (SMOS), can be fitted to geographically weighted regression (GWR) model based on soil moisture. In this way, we can combine the SOMS and MODIS data and assess the drought based on the inversion of the soil moisture model. The main research contents and conclusions are as follows: (1) This thesis applied MODIS data and carried out the calculation of two common remote sensing drought indexes – PDI and VSWI. A complete software package from preprocessing to evaluation was implemented. The experiment shows that the sensitivities of the PDI and VSWI are different. PDI is more sensitive to different regional climate condition, soil type, and planting structure, because it evaluates the drought based on the soil moisture; while VSWI employs vegetation the water stress which is less dependent on regional factors, ecological factors and the soil background influence, but it is less responsive to temporal changes. (2) Because the low spatial resolution of SMOS data as well as its limited penetration depth (top three centimeters of the soil profile), there is a deviation between the soil moisture values of SMOS data and those measured from the ground stations. Two regression models (least squares regression model and GWR) have been compared. GWR model can better eliminate the effects of the spatial autocorrelation, and achieve better results shown by the value of R-Squared(R2) and Akaike's Information Criterion (AICc), which is more suitable for the drought monitoring of large areas. (3) By applying the regression parameters derived from the GWR model, the drought monitoring of the Yangtze River basin can be achieved by calculating the soil moisture, and the drought level can be graded according to the common meteorological standard. In general, there is a prominent contrast of the regression parameters between the east and the west. The inversion results fit well to the measured data overall. The results of regression analysis show that due to the limited number and the uneven distribution of the ground stations, a few ground station points have different levels compared to the measured drought grade, . In addition, GWR model is more suitable for area with relatively low soil moisture. The more humid of the area, the larger the error will be.