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This paper uses the refined Generalized Split-Window (GSW) algorithm to derive the land surface temperature (LST) from the data acquired by the Visible and Infrared Radiometer on FengYun 3B (FY-3B/VIRR). The coefficients in the GSW algorithm corresponding to a series of overlapping ranges for the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST are derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The GSW algorithm is applied to retrieve LST from FY-3B/VIRR data in an arid area in northwestern China. Three emissivity databases are used to evaluate the accuracy of different emissivity databases for LST retrieval, including the ASTER Global Emissivity Database (ASTER_GED) at a 1-km spatial resolution (AG1km), an average of twelve ASTER emissivity data in the 2012 summer and emissivity spectra extracted from spectral libraries. The LSTs retrieved from the three emissivity databases are evaluated with ground-measured LST at four barren surface sites from June 2012 to December 2013 collected during the HiWATER field campaign. The results indicate that using emissivity extracted from ASTER_GED can achieve the highest accuracy with an average bias of 1.26 and −0.04 K and an average root mean square error (RMSE) of 2.69 and 1.38 K for the four sites during daytime and nighttime, respectively. This result indicates that ASTER_GED is a useful emissivity database for generating global LST products from different thermal infrared data and that using FY-3B/VIRR data can produce reliable LST products for other research areas.
Jinxiong Jiang; Hua Li; Qinhuo Liu; Heshun Wang; Yongming Du; Biao Cao; Bo Zhong; Shanlong Wu. Evaluation of Land Surface Temperature Retrieval from FY-3B/VIRR Data in an Arid Area of Northwestern China. Remote Sensing 2015, 7, 7080 -7104.
AMA StyleJinxiong Jiang, Hua Li, Qinhuo Liu, Heshun Wang, Yongming Du, Biao Cao, Bo Zhong, Shanlong Wu. Evaluation of Land Surface Temperature Retrieval from FY-3B/VIRR Data in an Arid Area of Northwestern China. Remote Sensing. 2015; 7 (6):7080-7104.
Chicago/Turabian StyleJinxiong Jiang; Hua Li; Qinhuo Liu; Heshun Wang; Yongming Du; Biao Cao; Bo Zhong; Shanlong Wu. 2015. "Evaluation of Land Surface Temperature Retrieval from FY-3B/VIRR Data in an Arid Area of Northwestern China." Remote Sensing 7, no. 6: 7080-7104.
This study analyzed the scaling problem of land surface temperature (LST) data retrieved with the Temperature Emissivity Separation (TES) algorithm. We compiled a remotely sensed dataset that included Thermal Airborne Hyperspectral Imager (TASI) and satellite-based Advanced Spaceborne Thermal Emission Reflection (ASTER) data, which were acquired simultaneously. This dataset provided the range of spatial heterogeneities of land surface necessary for the study, which was quantified by the dispersion variance. The LST scaling problem was studied by comparing the remotely sensed LST products in two ways. First, the LST products calculated in the distributed method and the lumped method were compared. Second, the airborne and satellite-based LST products derived from the TES algorithm were compared. Four upscaling methods of LST were used in the process. A scaling correction methodology was developed based on the comparisons. The results showed that the scaling effect could be as large as 0.8 when the spatial resolution of the TASI LST data was coarse. The scaling effect increases quickly with the spatial resolution until it reaches the characteristic scale of the landscape and is positively correlated with the spatial heterogeneity. The first two upscaling methods denoted as Methods 1–2 can upscale the LST more effectively when compared with the other two scaling methods (Methods 3–4). The scaling effect for the ASTER data is not notable. The comparison between the TASI and ASTER data showed that they were highly consistent, with a root mean square error (RMSE) of approximately 0.88 K, when the pixels were relatively homogeneous. When the spatial heterogeneity was significant, the RMSE was as large as 2.68 K The scaling correction methodology provided resolution-invariant results with scaling effects of less than 0.5 K.
Tian Hu; Qinhuo Liu; Yongming Du; Hua Li; Heshun Wang; Biao Cao. Analysis of the Land Surface Temperature Scaling Problem: A Case Study of Airborne and Satellite Data over the Heihe Basin. Remote Sensing 2015, 7, 6489 -6509.
AMA StyleTian Hu, Qinhuo Liu, Yongming Du, Hua Li, Heshun Wang, Biao Cao. Analysis of the Land Surface Temperature Scaling Problem: A Case Study of Airborne and Satellite Data over the Heihe Basin. Remote Sensing. 2015; 7 (5):6489-6509.
Chicago/Turabian StyleTian Hu; Qinhuo Liu; Yongming Du; Hua Li; Heshun Wang; Biao Cao. 2015. "Analysis of the Land Surface Temperature Scaling Problem: A Case Study of Airborne and Satellite Data over the Heihe Basin." Remote Sensing 7, no. 5: 6489-6509.
High spatial resolution airborne data with little sub-pixel heterogeneity were used to evaluate the suitability of the temperature/vegetation (Ts/VI) space method developed from satellite observations, and were explored to improve the performance of the Ts/VI space method for estimating soil moisture (SM). An evaluation of the airborne ΔTs/Fr space (incorporated with air temperature) revealed that normalized difference vegetation index (NDVI) saturation and disturbed pixels were hindering the appropriate construction of the space. The non-disturbed ΔTs/Fr space, which was modified by adjusting the NDVI saturation and eliminating the disturbed pixels, was clearly correlated with the measured SM. The SM estimations of the non-disturbed ΔTs/Fr space using the evaporative fraction (EF) and temperature vegetation dryness index (TVDI) were validated by using the SM measured at a depth of 4 cm, which was determined according to the land surface types. The validation results show that the EF approach provides superior estimates with a lower RMSE (0.023 m3·m−3) value and a higher correlation coefficient (0.68) than the TVDI. The application of the airborne ΔTs/Fr space shows that the two modifications proposed in this study strengthen the link between the ΔTs/Fr space and SM, which is important for improving the precision of the remote sensing Ts/VI space method for monitoring SM.
Lei Fan; Qing Xiao; Jianguang Wen; Qiang Liu; Yong Tang; DongQin You; Heshun Wang; Zhaoning Gong; Xiaowen Li. Evaluation of the Airborne CASI/TASI Ts-VI Space Method for Estimating Near-Surface Soil Moisture. Remote Sensing 2015, 7, 3114 -3137.
AMA StyleLei Fan, Qing Xiao, Jianguang Wen, Qiang Liu, Yong Tang, DongQin You, Heshun Wang, Zhaoning Gong, Xiaowen Li. Evaluation of the Airborne CASI/TASI Ts-VI Space Method for Estimating Near-Surface Soil Moisture. Remote Sensing. 2015; 7 (3):3114-3137.
Chicago/Turabian StyleLei Fan; Qing Xiao; Jianguang Wen; Qiang Liu; Yong Tang; DongQin You; Heshun Wang; Zhaoning Gong; Xiaowen Li. 2015. "Evaluation of the Airborne CASI/TASI Ts-VI Space Method for Estimating Near-Surface Soil Moisture." Remote Sensing 7, no. 3: 3114-3137.
In this study, the Visible Infrared Imager Radiometer Suite (VIIRS) land surface temperature (LST) environmental data record (EDR) and Moderate Resolution Imaging Spectroradiometer (MODIS) L2 swath LST products (collection 5) from both the Terra and Aqua satellites were evaluated against ground observations in an arid area of northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. Four barren surface sites were chosen for the evaluation, which took place from June 2012 to April 2013. The results show that the current VIIRS LST products demonstrate a reasonable accuracy, with an average bias of 0.36K and −0.58K and an average root mean square error (RMSE) of 2.74K and 1.48K for the four sites during daytime and nighttime, respectively. The accuracy of the nighttime LST is much better than that of daytime. Furthermore, it was also found that the VIIRS split-window (SW) algorithm provides better performance than the VIIRS dual split-window (DSW) algorithm during both daytime and nighttime. For MODIS LST products, the results show that both Terra and Aqua MODIS C5 LST products underestimate the LST for the four barren surface sites at daytime, and the biases and RMSEs are much larger for Aqua, with biases varies from −0.91K to −3.13K for Terra and from −1.31K to −3.76K for Aqua
Hua Li; Donglian Sun; Yunyue Yu; Hongyan Wang; Yuling Liu; Qinhuo Liu; Yongming Du; Heshun Wang; Biao Cao. Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China. Remote Sensing of Environment 2014, 142, 111 -121.
AMA StyleHua Li, Donglian Sun, Yunyue Yu, Hongyan Wang, Yuling Liu, Qinhuo Liu, Yongming Du, Heshun Wang, Biao Cao. Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China. Remote Sensing of Environment. 2014; 142 ():111-121.
Chicago/Turabian StyleHua Li; Donglian Sun; Yunyue Yu; Hongyan Wang; Yuling Liu; Qinhuo Liu; Yongming Du; Heshun Wang; Biao Cao. 2014. "Evaluation of the VIIRS and MODIS LST products in an arid area of Northwest China." Remote Sensing of Environment 142, no. : 111-121.
In this paper, two atmospheric profile sources were assessed for land surface temperature (LST) retrieval purposes for the HJ-1B IRS (Infrared Scanner) single-channel thermal infrared (TIR) data. One profile source is the National Center for Environmental Prediction (NCEP) operational global analysis data, and the other source is the Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric profiles product (MOD07). The atmospheric profiles were used as the input to the MODTRAN 4 radiative transfer model to calculate the atmospheric parameters involved in LST retrieval. The LST retrievals from the HJ-1B IRS data were compared with ground measured temperatures obtained from a series of field campaigns in Hebei province, China, from May to September of 2010. Ground measurements were performed over four land-cover types: bare soil, full-cover wheat, full-cover corn, and water surfaces. A total of 11 points of measurements was collected over a period of eight days. The results indicate that the LST derived from HJ-1B IRS data using either the NCEP or MOD07 profiles showed good agreement with the ground LSTs, with an root mean square error (RMSE) of 1.16 and 1.21 K for the NCEP and MOD07, respectively. In addition, we found that the MOD07 profiles may cause greater error for the atmospheric parameters estimation in the TIR domain for the regions of higher altitude due to a lack of data at the lower altitude levels. Thus, we proposed a method for combination of the MOD07 and NCEP profiles for LST retrieval. The results show that the combined profile is able to produce more reliable results than the use of only one type of profile because the combination offers both high spatial resolution and the necessary level of accuracy. This result implies that the combined profiles may be highly useful for accurate LST retrieval when local soundings are not available and particularly for sensors with only one thermal channel.
Hua Li; Qinhuo Liu; Yongming Du; Jinxiong Jiang; Heshun Wang. Evaluation of the NCEP and MODIS Atmospheric Products for Single Channel Land Surface Temperature Retrieval With Ground Measurements: A Case Study of HJ-1B IRS Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2013, 6, 1399 -1408.
AMA StyleHua Li, Qinhuo Liu, Yongming Du, Jinxiong Jiang, Heshun Wang. Evaluation of the NCEP and MODIS Atmospheric Products for Single Channel Land Surface Temperature Retrieval With Ground Measurements: A Case Study of HJ-1B IRS Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2013; 6 (3):1399-1408.
Chicago/Turabian StyleHua Li; Qinhuo Liu; Yongming Du; Jinxiong Jiang; Heshun Wang. 2013. "Evaluation of the NCEP and MODIS Atmospheric Products for Single Channel Land Surface Temperature Retrieval With Ground Measurements: A Case Study of HJ-1B IRS Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6, no. 3: 1399-1408.