RESUMO
The land surface temperature (LST) is a key parameter for energy balance, evapotranspiration and climate change. In this study, two new methods of LST retrieval from passive microwave data are developed: one is deriving LST only using single-channel dual-polarized data based on the relationship between the emissivity and microwave polarization difference index (MPDI) (denoted as Method 1); the other one is deriving LST using the traditional multi-channel method with prior knowledge of the normalized difference vegetation index (NDVI) (denoted as Method 3). Taking Moderate Resolution Imaging Spectroradiometer (MODIS) LST products as the actual LSTs, the coefficients for these algorithms are determined. From the results for the year 2008, it is demonstrated that the root mean square errors (RMSEs) for the LST retrieval using Method 3 are the smallest and range from 2.92 K to 3.44 K, the RMSEs for the LST retrieval using traditional multi-channel method (denoted as Method 2) range from 3.07 K to 4.05 K, and the worst results come from Method 1, whose RMSEs range from 3.11 K to 4.13 K at a frequency of 89 GHz. This could be caused by the fact that the NDVI provides substantial emissivity knowledge in Method 3, and much richer vegetation could result in a more accurate emissivity estimation.
RESUMO
OBJECTIVE: To estimate snail distribution by using high spatial resolution QuickBird image on the basis of retrieving the eco-environment factors relevant to snail distribution. METHODS: Combined with the well-positioned ground data of Oncomelania snails, the meter-level high spatial resolution QuickBird image was used to retrieve the eco-environment factors related to snail distribution in Jiangxin village of Dangtu county, Anhui Province. The factors included vegetation (vegetation index and vegetation cover ratio) and soil (soil texture, soil cover type and humidity). A qualitative analysis was made by using principle component analysis, K-T transformation and supervision classification methods to retrieve the eco-environment factors. The vegetation index NDVI (Normalized Difference Vegetation Index) and MSAVI (Modified Soil Adjustment Vegetation Index) were calculated, and LAI (Leaf area index) and F (vegetation cover ratio) were retrieved. Information from QuickBird data and corresponding ground data were then used to analyze the relationship between snail distribution and environmental factors by using ArcGIS and statistical software. RESULTS: Snail data were received from 153 ground distribution spots and a GIS database on spacial distribution of snails was established. This database covered snail density, NDVI, MSAVI, LAI(NDVI), LAI(MSAVI), F(NDVI), F(MSAVI), PCA-1, PCA-2, PCA-3, KT-1, KT-2 and KT-3. Statistical analysis showed that the snail density could be estimated by LAINDVI and FMSAVI quantitatively based on the following regression model: Y = -3.919 + 1.22 LAI(MSVI) + 16.076 F(MSAVI). Decision index of the regression model was 0.2. CONCLUSIONS: A quantitative regression model between Oncomelania snail distribution and environmental variables retrieved from QuickBird images has been established, which may have a wide application prospect. KT-1, KT-2 and KT-3. Statistical analysis showed that the snail density could be estimated by LAINDVI and FMSAVI quantitatively based on the following regression model: Y = -3.919 + 1.22 LAI(MSAVI) + 16.076 F(MSAVI). Decision index of the regression model was 0.2. CONCLUSIONS: A quantitative regression model between Oncomelania snail distribution and environmental variables retrieved from QuickBird images has been established, which may have a wide application prospect.