Recently, Tibetan plateau (TP) has become a hot area of climate change research. And Land Surface Temperature (LST) is one of key factors in the research. In order to get a long time-series, high spatial resolution and high accuracy LST dataset, we carried out analysis of influence essential factor of LST retrieval from AVHRR oriented Tibetan plateau area. First, choose MODTRAN5.2 to simulate the impact of land surface, atmospheric, geometric factors on bright temperatures of channel 4 and channel 5 for special features of TP using stand atmospheric models. Result showed that emissivity, boundary temperature, water vapor amount and view zenith angle were the principal elements of bright temperature. Second an improved algorithm from Wanz-Dozier split window model was established considering these factors. At last, differences between LST retrieval result considering different factors were given.
Due to the lack of observation data which match the pixels size of satellite remote sensing data, the inversion accuracy of satellite inversion products in Tibet plateau is lack of an effective verification. Hence, the in situ observations are required to support their calibration and validation. For this purpose, a multi-level and multi-scale soil moisture and temperature regular automatic monitoring network (MS-SMTRMN) was established on Qiangtang grassland of northern Tibet area to support multiple satellite remote sensing application, climate modeling or assimilation, and land surface process studies. In this paper, MS-SMTRMN aim at multi-satellite remote sensing application was detailed and the observation data with quality control were used to the verification for multiple satellite retrieval products (FY3, AMSR2 and SMOS). This study will contribute to the understanding of the quality of products and lays the foundation for the satellite data assimilation results in the TP area.
KEYWORDS: MODIS, Temperature metrology, Satellites, Reflection, Radiometry, Spatial resolution, Data acquisition, Optical filters, Time series analysis, Algorithm development
Land surface emissivity is a key parameter in estimating the land surface radiation budget. The validation of the moderate-resolution imaging spectroradiometer (MODIS) land surface emissivity with field measurements is rarely performed. In this study, a field measurement was performed over the central part of the Taklimakan Desert for the validation of the MODIS land surface emissivity products (MOD11B1) Version 4 (V4.1) and Version 5 (V5). The homogeneity of two validation sites was verified using the advanced spaceborne thermal emission and reflection radiometer (ASTER) land surface temperature and emissivity acquired closely before and after the overpass of MODIS. MOD11B1 V4.1 and V5 emissivity data for bands 29, 31, and 32 were compared to the emissivity calculated from the field measured emissivity spectra convolved with the filter function of the MODIS bands 29 (8.52 μm), 31 (11.03 μm), and 32 (12.04 μm). The comparison results indicate that the V4.1 emissivity data agree well with the field measurements, with mean absolute differences of 0.017 and 0.007 for site 1 and site 2, respectively, and the mean absolute differences of the V5 emissivity data were 0.034 and 0.033 for site 1 and site 2, respectively. The data version used must be considered when MOD11B1 is used in real applications, especially for time series analysis.
Monitoring residential areas at a regional scale, and even at a global scale, has become an increasingly important topic.
However, extraction of residential information was still a difficulty and challenging task, such as multiple usable data
selection and automatic or semi-automatic techniques. In metropolitan area, such as Beijing, urban sprawl has brought
enormous pressure on rural and natural environments. Given a case study, a new strategy of extracting of residential
information integrating the upscaling methods and object multi-features was introduced in high resolution SPOT fused
image. Multi-resolution dataset were built using upscaling methods, and optimal resolution image was selected by
semi-variance analysis approach. Relevant optimal spatial resolution images were adopted for different type of
residential area (city, town and rural residence). Secondly, object multi-features, including spectral information, generic
shape features, class related features, and new computed features, were introduced. An efficient decision tree and Class
Semantic Representation were set up based on object multi-features. And different classes of residential area were
extracted from multi-resolution image. Afterwards, further discussion and comparison about improving the efficiency
and accuracy of classification with the proposed approach were presented. The results showed that the optimal resolution
image selected by upscaling and semi-variance method successfully decreased the heterogeneous, smoothed the noise
influence, decreased computational, storage burdens and improved classification efficiency in high spatial resolution
image. The Class Semantic Representation and decision tree based on object multi-features improved the overall
accuracy and diminished the 'salt and pepper effect'. The new image analysis approach offered a satisfactory solution for
extracting residential information quickly and efficiently.
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