This page has only limited features, please log in for full access.
Land surface component temperatures (LSCTs) are important parameters in many applications. However, the multi-angle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multi-pixel observations together. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard ESA's Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multi-pixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multi-angle algorithm. The introduction of multi-pixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multi-angle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors (RMSEs) of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products.
Zunjian Bian; Hua Li; Frank M. Gottsche; Ruibo Li; Yongming Du; Huazhong Ren; Biao Cao; Qing Xiao; Qinhuo Liu. Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 5536 -5549.
AMA StyleZunjian Bian, Hua Li, Frank M. Gottsche, Ruibo Li, Yongming Du, Huazhong Ren, Biao Cao, Qing Xiao, Qinhuo Liu. Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):5536-5549.
Chicago/Turabian StyleZunjian Bian; Hua Li; Frank M. Gottsche; Ruibo Li; Yongming Du; Huazhong Ren; Biao Cao; Qing Xiao; Qinhuo Liu. 2020. "Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 5536-5549.
In order to improve the simulation accuracy of directional brightness temperature (DBT) and the retrieval accuracy of component temperature, a model considering intra-row heterogeneity to simulate the DBT angular distribution over crop canopy is proposed. At individual scale, the probability of leaf appearance is inversely proportional to the distance from central stem. On the basis of this assumption, we formulated leaf area volume density (LAVD) spatial distribution at three hierarchical scales: individual scale, row scale, and scene scale. The equations for directional gap probability and bi-directional gap probability were modified to adapt the heterogeneity of row structure. Afterwards, a straightforward radiative transfer model was built based on the gap probabilities. A set of simulated data was generated by the thermal radiosity-graphics combined model (TRGM) as the benchmark to evaluate both forward simulation and inversion ability of the new model; we compared the new DBT model against an existing model assuming row as homogeneous box. With the growth of crops, the canopy structure of row crops will gradually change from row structure to continuous canopy. The new DBT model agreed with the TRGM model much better than the homogeneous row model at the middle stage of the crop growth season. The new model and the homogeneous row model achieve similar accuracy at early stage and end stage. At the middle growth stage, the new model can improve the accuracy of soil temperature retrieval. We recommend the new DBT model as an option to improve the DBT simulation and component temperature retrieval for row-planted crop canopy. In particular, the more accurate component temperatures during the middle growth stage are fundamentally important in characterizing crop water status, evapotranspiration, and soil moisture, which are subsequently critical for predicting crop productivity.
Yongming Du; Biao Cao; Hua Li; Zunjian Bian; Boxiong Qin; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. Modeling Directional Brightness Temperature (DBT) over Crop Canopy with Effects of Intra-Row Heterogeneity. Remote Sensing 2020, 12, 2667 .
AMA StyleYongming Du, Biao Cao, Hua Li, Zunjian Bian, Boxiong Qin, Qing Xiao, Qinhuo Liu, Yijian Zeng, Zhongbo Su. Modeling Directional Brightness Temperature (DBT) over Crop Canopy with Effects of Intra-Row Heterogeneity. Remote Sensing. 2020; 12 (17):2667.
Chicago/Turabian StyleYongming Du; Biao Cao; Hua Li; Zunjian Bian; Boxiong Qin; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. 2020. "Modeling Directional Brightness Temperature (DBT) over Crop Canopy with Effects of Intra-Row Heterogeneity." Remote Sensing 12, no. 17: 2667.
An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.
Ruibo Li; Hua Li; Lin Sun; Yikun Yang; Tian Hu; Zunjian Bian; Biao Cao; Yongming Du; Qinhuo Liu. An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data. Remote Sensing 2020, 12, 2613 .
AMA StyleRuibo Li, Hua Li, Lin Sun, Yikun Yang, Tian Hu, Zunjian Bian, Biao Cao, Yongming Du, Qinhuo Liu. An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data. Remote Sensing. 2020; 12 (16):2613.
Chicago/Turabian StyleRuibo Li; Hua Li; Lin Sun; Yikun Yang; Tian Hu; Zunjian Bian; Biao Cao; Yongming Du; Qinhuo Liu. 2020. "An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data." Remote Sensing 12, no. 16: 2613.
As a surface component, the tree trunk affects the top-of-canopy (TOC) emissivity and thermal infrared (TIR) radiance over a forest with fewer leaves, which is important for the inversion of land surface temperatures (LSTs) and further applications such as predicting forest fires and monitoring drought conditions. Therefore, the tree trunk effect was analyzed in this article using a thermal radiation directionality model, in which the forest structure was considered by the geometric optical (GO) theory and the spectral invariance theory was introduced into the GO framework for the single-scattering effect between components. The model used was evaluated using unmanned aerial vehicle (UAV)-based measurements with root-mean-square errors (RMSEs) lower than 0.25 °C for directional anisotropies (DAs) of brightness temperatures (BTs). Comparison with a 3-D radiative transfer model, discrete anisotropic radiative transfer (DART), also indicated an acceptable tool of the proposed model for the trunk effect with RMSEs lower than 0.003 °C and 1.2 °C for DAs of emissivity and BTs, respectively. In this study, the root-mean-squared difference (RMSD) levels between the vegetation-soil and vegetation-trunk-soil canopies, which were viewed as an equivalent indicator of the trunk effect, were provided for the TOC emissivity and BTs as well as their DAs, by combination with the changes in the leaf area index (LAI), stand density, trunk shape, and component temperatures, which can help identify the cases in which the trunk effect should be considered. According to a comprehensive analysis, for cases with sparse stand density (α <0.04), the tree trunk should be considered for a BT RMSD level lower than 0.5 °C when the LAI value was lower than 0.6. The corresponding LAI value was 0.8 for an RMSD level of BT DA lower than 0.3 °C. Moreover, for the cases with low soil emissivity, the difference in the TOC emissivity with and without trunk can reach up to 0.035, and the RMSD was still larger than 0.01 when the stand density and LAI were 0.05 and 0.6, respectively.
Zunjian Bian; Biao Cao; Hua Li; Yongming Du; Wenjie Fan; Qing Xiao; Qinhuo Liu. The Effects of Tree Trunks on the Directional Emissivity and Brightness Temperatures of a Leaf-Off Forest Using a Geometric Optical Model. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 5370 -5386.
AMA StyleZunjian Bian, Biao Cao, Hua Li, Yongming Du, Wenjie Fan, Qing Xiao, Qinhuo Liu. The Effects of Tree Trunks on the Directional Emissivity and Brightness Temperatures of a Leaf-Off Forest Using a Geometric Optical Model. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (6):5370-5386.
Chicago/Turabian StyleZunjian Bian; Biao Cao; Hua Li; Yongming Du; Wenjie Fan; Qing Xiao; Qinhuo Liu. 2020. "The Effects of Tree Trunks on the Directional Emissivity and Brightness Temperatures of a Leaf-Off Forest Using a Geometric Optical Model." IEEE Transactions on Geoscience and Remote Sensing 59, no. 6: 5370-5386.
When ground-leaving radiance from low-temperature surface is similar to downwelling radiance from the sky, the singular values arise in the retrieved land surface emissivity (LSE) at some specific spectral bands. In addition, too many singular values eventually cause temperature/emissivity separation algorithms to fail. To reduce the occurrence of these singular points, we formulate two indices, including the land-atmosphere radiance contrast index (LACI) and neighbor band contrast index (NBCI). LACI characterizes the contrast between surface radiance and sky downwelling radiance. NBCI characterizes the contrast between the radiance at neighboring bands. These two indices are used as filters to select bands which participate in the iterative spectrally smooth calculation. Thus, we modify the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm for low-temperature surfaces. Two methods have been used to evaluate the modified algorithm. First, numerical experiments are conducted to evaluate if the modified algorithm can accurately retrieve the ``true'' LSE from the simulated data. Second, an artificial low-temperature surface cooled by liquid nitrogen is measured to validate the modified algorithm. The results show that the modified algorithm can effectively avoid singular values, and behaves much better than the original algorithm with errors of less than 0.01 in retrieved emissivity when applied to low-temperature regions, while the modified algorithm brings limited improvement in retrieved temperature.
Yongming Du; Hua Li; Biao Cao; Zunjian Bian; Jianming Zhao; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. A Modified Interactive Spectral Smooth Temperature Emissivity Separation Algorithm for Low-Temperature Surface. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 7643 -7653.
AMA StyleYongming Du, Hua Li, Biao Cao, Zunjian Bian, Jianming Zhao, Qing Xiao, Qinhuo Liu, Yijian Zeng, Zhongbo Su. A Modified Interactive Spectral Smooth Temperature Emissivity Separation Algorithm for Low-Temperature Surface. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (11):7643-7653.
Chicago/Turabian StyleYongming Du; Hua Li; Biao Cao; Zunjian Bian; Jianming Zhao; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. 2020. "A Modified Interactive Spectral Smooth Temperature Emissivity Separation Algorithm for Low-Temperature Surface." IEEE Transactions on Geoscience and Remote Sensing 58, no. 11: 7643-7653.
This letter presents an experiment to explore the nonisothermal effects on temperature and emissivity separation (TES). The innovation of this experiment lies in its design, which highlights the contrast between isothermal and nonisothermal conditions in emissivity measurements. We artificially created a sharply contrasting nonisothermal soil surface using liquid nitrogen cooling and solar heating. The iterative spectrally smooth TES (ISSTES) algorithm was used to process the experimental data. The analyzed results of the experimental data show that the nonisothermal conditions have a significant effect on TES. The bias of retrieved emissivity increases with the component temperature difference as well as with wavelength. The bias around the split window band can reach up to 1% when the difference of the component temperature is 40K. Considering that 1% error in emissivity can cause approximately 1K error of retrieved land surface temperature (LST), the nonisothermal effects on emissivity cannot be ignored. We hope that this experiment will arouse attention of the nonisothermal effects on TES and call for more efforts to be devoted to this issue in the future.
Yongming Du; Biao Cao; Hua Li; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. An Experimental Study on Separating Temperature and Emissivity of a Nonisothermal Surface. IEEE Geoscience and Remote Sensing Letters 2019, 16, 1610 -1614.
AMA StyleYongming Du, Biao Cao, Hua Li, Qing Xiao, Qinhuo Liu, Yijian Zeng, Zhongbo Su. An Experimental Study on Separating Temperature and Emissivity of a Nonisothermal Surface. IEEE Geoscience and Remote Sensing Letters. 2019; 16 (10):1610-1614.
Chicago/Turabian StyleYongming Du; Biao Cao; Hua Li; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. 2019. "An Experimental Study on Separating Temperature and Emissivity of a Nonisothermal Surface." IEEE Geoscience and Remote Sensing Letters 16, no. 10: 1610-1614.
Land surface temperatures (LSTs) obtained from remote sensing data are crucial in monitoring the conditions of crops and urban heat islands. However, since retrieved LSTs represent only the average temperature states of pixels, the distributions of temperatures within individual pixels remain unknown. Such data cannot satisfy the requirements of applications such as precision agriculture. Therefore, in this paper, we propose a model that combines a fast radiosity model, the Radiosity Applicable to Porous IndiviDual Objects (RAPID) model, and energy budget methods to dynamically simulate brightness temperatures (BTs) over complex surfaces. This model represents a model-based tool that can be used to estimate temperature distributions using fine-scale visible as well as near-infrared (VNIR) data and temporal variations in meteorological conditions. The proposed model is tested over a study area in an artificial oasis in Northwestern China. The simulated BTs agree well with those measured with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The results reflect root mean squared errors (RMSEs) less than 1.6 °C and coefficients of determination (R2) greater than 0.7. In addition, compared to the leaf area index (LAI), this model displays high sensitivity to wind speed during validation. Although simplifications may be adopted for use in specific simulations, this proposed model can be used to support in situ measurements and to provide reference data over heterogeneous vegetation surfaces.
Zunjian Bian; Biao Cao; Hua Li; Yongming Du; Huaguo Huang; Qing Xiao; Qinhuo Liu. Modeling the Distributions of Brightness Temperatures of a Cropland Study Area Using a Model that Combines Fast Radiosity and Energy Budget Methods. Remote Sensing 2018, 10, 736 .
AMA StyleZunjian Bian, Biao Cao, Hua Li, Yongming Du, Huaguo Huang, Qing Xiao, Qinhuo Liu. Modeling the Distributions of Brightness Temperatures of a Cropland Study Area Using a Model that Combines Fast Radiosity and Energy Budget Methods. Remote Sensing. 2018; 10 (5):736.
Chicago/Turabian StyleZunjian Bian; Biao Cao; Hua Li; Yongming Du; Huaguo Huang; Qing Xiao; Qinhuo Liu. 2018. "Modeling the Distributions of Brightness Temperatures of a Cropland Study Area Using a Model that Combines Fast Radiosity and Energy Budget Methods." Remote Sensing 10, no. 5: 736.
After publication of the research paper [1], it was found that funding information was missing from the Acknowledgment part
Zunjian Bian; Biao Cao; Hua Li; Yongming Du; Lisheng Song; Wenjie Fan; Qing Xiao; Qinhuo Liu. Addendum: Bian, Z. et al. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sens. 2017, 9, 780. Remote Sensing 2017, 9, 1039 .
AMA StyleZunjian Bian, Biao Cao, Hua Li, Yongming Du, Lisheng Song, Wenjie Fan, Qing Xiao, Qinhuo Liu. Addendum: Bian, Z. et al. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sens. 2017, 9, 780. Remote Sensing. 2017; 9 (10):1039.
Chicago/Turabian StyleZunjian Bian; Biao Cao; Hua Li; Yongming Du; Lisheng Song; Wenjie Fan; Qing Xiao; Qinhuo Liu. 2017. "Addendum: Bian, Z. et al. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sens. 2017, 9, 780." Remote Sensing 9, no. 10: 1039.
The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 °C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index.
Zunjian Bian; Biao Cao; Hua Li; Yongming Du; Lisheng Song; Wenjie Fan; Qing Xiao; Qinhuo Liu. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sensing 2017, 9, 780 .
AMA StyleZunjian Bian, Biao Cao, Hua Li, Yongming Du, Lisheng Song, Wenjie Fan, Qing Xiao, Qinhuo Liu. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sensing. 2017; 9 (8):780.
Chicago/Turabian StyleZunjian Bian; Biao Cao; Hua Li; Yongming Du; Lisheng Song; Wenjie Fan; Qing Xiao; Qinhuo Liu. 2017. "A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index." Remote Sensing 9, no. 8: 780.
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.