A new way called compositive inversion is presented in the paper to derive albedo over cloudy areas with multi-angular
satellite remote sensing data. It combines complementary angular clear observations of pixels having same BRDF shape
and directly retrieves BRDF parameters and albedo with RossThick-LiSparse Reciprocal model when the minimum
multi-angular observations meet its requirement. Contrastive retrieval experiments with five continuous 16-day Terra
MODIS data over the Tibetan Plateau (30 cases in total) showed that its retrieval capability is much higher than that of
the magnitude inversion, a backup algorithm adopted by the U.S. BRDF/albedo products, and accuracy of its retrievals is
rather equivalent to those with magnitude inversion. Since the unique ancillary data used in which is the land-cover type
classification data, which is superior over a priori BRDF database, so the compositive inversion can give more accurate
albedo information, and is an applicable and practical way to derive albedo of cloudy areas. The idea of compositive
inversion provides a new way to derive albedo in shorter temporal cycle and a particular view for use of multi-angular
remote sensing data to derive land surface information.
Soil moisture is an inegligible physical variable in agrometeorology, climatology, hydrology, ecology and crop cultivation and predicted normally by use of the Penman formula for meteorological records from a single or a few stations and weather forecasts. This method, however, allows to make the prediction only for a limited number of stations rather than regional gridded predictions. For this reason, we developed a scheme of satellite sensings retrieval, the regional climate model (RegCM2) and a soil water predicting model in combination for moisture in fields of staple crops over the Huang-Huai Plains, by which to establish a drought warning system, of which 1) the soil water predicting model makes use of the soil moisture balance equation applicable to fields of winter wheat and summer corn in the Plains, whose central component is the Penman formula revised by FAO; 2) the needed NWP products are offered by NCAR RegCM2 and 3) the initial field of soil moisture comes from the retrieval of polar-orbiting meteorological satellite data that are corrected through vegetation cover correction and a variational technique. Results show that the proposed scheme is able to improve the precision of the prediction and to better monitor and predict changes in the moisture and the distribution of drought-hit crop areas over the study plains.
Vegetation cover is the primary index of the earth's ecological system and the change in large-scale vegetation cover represents the effects of natural and human activities upon ecological environments and change in vegetation bears an intimate relation with that of climate, thus being one of the heated issues in the research of global change. In the context of 1981-2001 NOAA/AVHRR NDVI satellite sensings, classified vegetation types and climate data for the Huanghe-Huaihe-Haihe (HHH) zone and based on a range of vegetation types selected, including forested land, grassland, meadow and farmland, study is conducted on the dynamic variations of NDVI on a seasonal and an interannual basis, together with their relations to climate change investigated, thus achieving some preliminary findings regarding the seasonal and interannual variation features of the HHH vegetation. Results show that, viewing the situation as a whole, the yearly variation displays intense seasonality under the control of monsoon climate while looking at the interannual variation, the 1981-2001 mean NDVI exhibits insignificant rise trend, bigger in spring compared to other seasons; types of scattered vegetation and forests are more steady on an interannual basis but the agricultural types change dramatically, indicating relatively greater effect of human activities; the vegetation degradation has occurred in the HHH zone in recent years; farmland vegetation is dominant and natural vegetation is about half the agricultural area; for different vegetation types, multi-yearly mean NDVI follows almost the same course on an interannual basis, except for some difference in range between them; yearly rainfall and temperature have positive effect on dynamic NDVI variation while evaporation is in higher negative correlation with the NDVI; Water is a sensitive factor to the growth of conifers, grassland and crops in spring and summer. Water and heat are important to biannual crops, broadleaf trees and grassland vegetation in autumn. In winter all types of vegetation are insignificantly correlated to climate factors.
In order to reduce the human labor in snow cover monitoring, recent study has been done on modification of the multi-spectral thresholds method which was developed in NSMC in 1996. Based on the analysis of the spectral characteristics of snow, cloud and other types of earth surface with multi-spectral data, an automated processing system with the new thresholds method to distinguish snow and cloud have been set up in NSMC. The devised technique is applied to multi-spectral data from FY-1C and NOAA-16 for mapping snow cover over China during winter season. To assess performance of the modification, the automatically produced snow data sets have been compared with the NOAA operational snow products and validated against in situ land surface observations in China. There is a good consistency between our results, NOAA snow data and ground measurements. The correlation coefficient between the snow cover produced by NSMC and NOAA is about 80%. The results of the comparison show us that the 1.6µm band data is very useful for snow and cloud distinguishing. The new method can reduce the human labor in snow cover monitoring and produce accurate snow cover images in China using FY-1C and NOAA-16 satellite data.
Snow cover is an important resource of the Earth. It is a potential factor related to climate and global changes. On account of its high reflectance and low heat conductivity, the existence of snow cover can affect surface and air temperature, surface albedo, radiation balance, soil moisture and so on. It may have influence on the earth- atmosphere system. In order to study and understand the impact of snow cover on climate and hydrologic budgets, it is necessary to have variation and distribution of snow cove over a long period. Usually the snow cover data can be obtained regularly by observation of weather station, but these data are limited to point surface measurement and poorly represented in mountainous and sparsely inhabited areas. Remote sensing is a powerful tool for snow cover observations.