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Hua Li
Aerospace Information Research Institute, Chinese Academy of Sciences, China

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Journal article
Published: 24 July 2021 in Remote Sensing of Environment
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Remotely sensed and accurate daily mean land surface temperature (Tdm) is valuable for various applications such as air temperature estimation and climate change monitoring. However, most traditional methods employed by the remote sensing community estimate Tdm by averaging the – usually few – observed cloud-free land surface temperatures (LSTs). Such estimates can have large sampling bias, especially for tandem polar orbiters, due to their sparse sampling of diurnal LST dynamics and the unavailability of under-cloud LSTs. To estimate accurate Tdm based on thermal observations from tandem polar orbiters, here we propose a simple yet robust framework that combines the annual temperature cycle (ATC) and the diurnal temperature cycle (DTC) models (termed the ADTC-based framework). The ATC model is used to reconstruct daily instantaneous under-cloud LSTs, based on which the DTC model is employed to establish diurnally continuous LST dynamics for estimating Tdm. The proposed framework is validated with geostationary LST observations and in-situ thermal measurements under both cloud-free and overcast conditions. The validations show that, under cloud-free conditions, the ADTC-based framework is able to reduce the positive sampling bias obtained with simple averaging (> 2.0 K) and yields a mean absolute error (MAE) of approximately 0.5 K. Under overcast conditions, the ADTC-based framework yields MAEs of 1.0 K and 0.5 K at the daily and monthly scales, respectively. Furthermore, a contribution analysis indicates that the ATC model reduces the MAE from around 4.2 K to 2.0 K while the DTC model reduces the MAE from around 2.0 K to 1.0 K. Based on our validation results and tests performed with MODIS data, the presented simple yet robust ADTC-based framework is able to accurately estimate large-scale spatiotemporally continuous Tdm from thermal observations of tandem polar orbiters. Therefore, the ADTC-based framework is a potentially valuable tool for many related applications.

ACS Style

Falu Hong; Wenfeng Zhan; Frank-M. Göttsche; Jiameng Lai; Zihan Liu; Leiqiu Hu; Peng Fu; Fan Huang; Jiufeng Li; Hua Li; Hua Wu. A simple yet robust framework to estimate accurate daily mean land surface temperature from thermal observations of tandem polar orbiters. Remote Sensing of Environment 2021, 264, 112612 .

AMA Style

Falu Hong, Wenfeng Zhan, Frank-M. Göttsche, Jiameng Lai, Zihan Liu, Leiqiu Hu, Peng Fu, Fan Huang, Jiufeng Li, Hua Li, Hua Wu. A simple yet robust framework to estimate accurate daily mean land surface temperature from thermal observations of tandem polar orbiters. Remote Sensing of Environment. 2021; 264 ():112612.

Chicago/Turabian Style

Falu Hong; Wenfeng Zhan; Frank-M. Göttsche; Jiameng Lai; Zihan Liu; Leiqiu Hu; Peng Fu; Fan Huang; Jiufeng Li; Hua Li; Hua Wu. 2021. "A simple yet robust framework to estimate accurate daily mean land surface temperature from thermal observations of tandem polar orbiters." Remote Sensing of Environment 264, no. : 112612.

Journal article
Published: 01 July 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Land surface temperature (LST) is a key parameter in numerous environmental studies. However, currently, there is no satellite sensor that can completely provide LST data with both high spatial and high temporal resolutions simultaneously. LST downscaling is regarded as an effective remedy for improving the temporal and spatial resolutions of LST data. In this study, a geographically and temporally weighted autoregressive (GTWAR) model of LST downscaling is that comprehensively considers the spatial heterogeneity, spatial autoregression and temporality of LST is newly proposed. The normalized difference water index (NDWI), the normalized difference built-up index (NDBI), and the normalized difference vegetation index (NDVI) were selected as explanatory variables to downscale the moderate resolution imaging spectroradiometer (MODIS) LST from 1000 m to 100 m, while the Landsat 8 LST was selected as the reference data. Compared with the thermal data sharpening (TsHARP), the geographically weighted regression (GWR), the geographically weighted autoregressive (GWAR) and the geographically and temporally weighted regression (GTWR) downscaling methods, the proposed method was superior based on quantitative indices, with the lowest root mean square error (RMSE) (Zhangye: 1.57, Beijing: 1.22) and mean absolute error (MAE) (Zhangye: 1.06, Beijing: 0.85). The downscaling model of GTWAR will facilitate improvements in the accuracy of downscaling for temporal series of LST data.

ACS Style

Xiaobo Luo; Yuan Chen; Zhi Wang; Hua Li; Yidong Peng. Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.

AMA Style

Xiaobo Luo, Yuan Chen, Zhi Wang, Hua Li, Yidong Peng. Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.

Chicago/Turabian Style

Xiaobo Luo; Yuan Chen; Zhi Wang; Hua Li; Yidong Peng. 2021. "Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.

Journal article
Published: 21 May 2021 in IEEE Geoscience and Remote Sensing Letters
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There are five widely used kernel-driven models in the thermal infrared domain designed for the angular correction of land surface temperature (LST), including three-parameter Roujean Lagouarde (RL), Vinnikov, RossThick-LiSparseR (Ross-Li), LiStrahlerFriedl-LiDenseR (LSF-Li), and four-parameter Vinnikov-RoujeanLagouarde (Vinnikov-RL). Their fitting accuracies with hundreds of observation angles (i.e., sufficient angle) were studied; however, the fitting ability of these five models with limited observation angles is unknown, which makes it difficult to choose the appropriate one in applications. To solve this problem, 30 600 groups of multiangle directional brightness temperature (DBT) datasets were simulated by the unified optical-thermal 4-stream model considering scattering by arbitrary inclined leaves (4SAIL) model considering ten different leaf area index values, three leaf inclination distribution functions, two hotspot factors, 17 different component temperatures, five solar zenith angles, and six solar azimuth angles. Each group contains DBT values in 21 960 viewing directions [i.e., 61 viewing zenith angle (VZA) x 360 viewing azimuth angle (VAA)]. We assume that all limited observations are in the plane with VAA = 180°/0° and VZA changing from -60° to 60° with a step of 10°. There are 13 candidate angles to be selected. Five, seven, nine, and 11 angle sampling schemes include 225, 400, 225, and 36 limited multiangle combinations, respectively. Each combination was used to drive these five kernel-driven models to fit 21 960 DBTs for 30 600 groups of 4SAIL simulations. The root-mean-square error (RMSE) of each combination and mean RMSE of all 886 combinations were used to assess the overall fitting ability of five kernel-driven models. In addition, 1 k errors were added to the driven DBTs to evaluate the models' robustness. Four groups of airborne measured DBTs were adopted to validate the assessment conclusions. Results show that the recommended order of these five models driven by 5-11 multiangle DBTs is Vinnikov-RL, LSF-Li, Vinnikov, Ross-Li, and RL when the driven DBTs do not contain errors; Vinnikov-RL, Vinnikov, LSF-Li, Ross-Li, and RL when the driven DBTs contain 1k errors; and Vinnikov-RL, LSF-Li, Ross-Li, RL, and Vinnikov for four groups of airborne measured datasets.

ACS Style

Xueting Ran; Biao Cao; Boxiong Qin; Zunjian Bian; Yongming Du; Hua Li; Qing Xiao; Qinhuo Liu. Assessment of Five Thermal Infrared Kernel-Driven Models Using Limited Multiangle Observations. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.

AMA Style

Xueting Ran, Biao Cao, Boxiong Qin, Zunjian Bian, Yongming Du, Hua Li, Qing Xiao, Qinhuo Liu. Assessment of Five Thermal Infrared Kernel-Driven Models Using Limited Multiangle Observations. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.

Chicago/Turabian Style

Xueting Ran; Biao Cao; Boxiong Qin; Zunjian Bian; Yongming Du; Hua Li; Qing Xiao; Qinhuo Liu. 2021. "Assessment of Five Thermal Infrared Kernel-Driven Models Using Limited Multiangle Observations." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Journal article
Published: 15 September 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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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.

ACS Style

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 Style

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 (99):5536-5549.

Chicago/Turabian Style

Zunjian 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.

Journal article
Published: 11 September 2020 in ISPRS Journal of Photogrammetry and Remote Sensing
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Statistical downscaling of land surface temperature (SDLST) algorithms with diverse scaling factors and regression models have been used to produce high spatial resolution LSTs based on Landsat-8 LST. However, the optimal choice of scaling factors and regression models and their associated combinations over various land surfaces, especially from a global perspective, remain unclear and even controversial. To cope with this issue, we compare 35 SDLST algorithms derived from a combination of seven scaling factors and five frequently used regression models over 32 geographical regions worldwide. The seven scaling factors, at varying degrees, make use of the LST-related information embedded within the visible and near-infrared and short-wave infrared bands of Landsat-8 data. The five regression models involved are multiple linear regression, partial least squares regression, artificial neural networks, support vector regression, and random forest (RF). Our main findings are: (1) The performance of the scaling factors and regression models are highly dependent on each other. Nevertheless, for most scaling factors, especially for high-dimension scaling factors with numerous LST-related variables, the downscaling algorithms that use RF as the regression model achieve the highest accuracy. (2) RFT21, a newly proposed SDLST algorithm based on the comparison results and further optimization, has high global operability and sufficiently high accuracy. RFT21 requires only Landsat-8 data as the inputs, and achieves the highest accuracy in comparison with the thermal sharpening (TsHARP) and high-resolution urban thermal sharpener (HUTS) algorithms, with the mean root-mean-square error (RMSE) reduced by more than 15%. These findings will facilitate the generation of high spatial resolution LSTs worldwide and associated applications.

ACS Style

Pan Dong; Lun Gao; Wenfeng Zhan; Zihan Liu; Jiufeng Li; Jiameng Lai; Hua Li; Fan Huang; Sagar K. Tamang; Limin Zhao. Global comparison of diverse scaling factors and regression models for downscaling Landsat-8 thermal data. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 169, 44 -56.

AMA Style

Pan Dong, Lun Gao, Wenfeng Zhan, Zihan Liu, Jiufeng Li, Jiameng Lai, Hua Li, Fan Huang, Sagar K. Tamang, Limin Zhao. Global comparison of diverse scaling factors and regression models for downscaling Landsat-8 thermal data. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 169 ():44-56.

Chicago/Turabian Style

Pan Dong; Lun Gao; Wenfeng Zhan; Zihan Liu; Jiufeng Li; Jiameng Lai; Hua Li; Fan Huang; Sagar K. Tamang; Limin Zhao. 2020. "Global comparison of diverse scaling factors and regression models for downscaling Landsat-8 thermal data." ISPRS Journal of Photogrammetry and Remote Sensing 169, no. : 44-56.

Journal article
Published: 19 August 2020 in Remote Sensing
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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.

ACS Style

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 Style

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 (17):2667.

Chicago/Turabian Style

Yongming 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.

Journal article
Published: 13 August 2020 in Remote Sensing
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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.

ACS Style

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 Style

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 (16):2613.

Chicago/Turabian Style

Ruibo 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.

Journal article
Published: 04 August 2020 in IEEE Transactions on Geoscience and Remote Sensing
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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.

ACS Style

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 Style

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 (6):5370-5386.

Chicago/Turabian Style

Zunjian 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.

Journal article
Published: 12 June 2020 in IEEE Transactions on Geoscience and Remote Sensing
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In this study, two collection 6 (C6) Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 land surface temperature (LST) products (MYD11_L2 and MYD21_L2) from the Aqua satellite were evaluated using temperature-based (T-based) and radiance-based (R-based) validation methods over barren surfaces in Northwestern China. The ground measurements collected at four barren surface sites from June 2012 to September 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment were used to perform the T-based evaluation. Ten sand dune sites were selected in six large deserts in Northwestern China to carry out an R-based validation from 2012 to 2018. The T-based validation results indicate that the C6 MYD21 LST product has a better accuracy than the C6 MYD11 product during both daytime and nighttime. The LST is underestimated by the C6 MYD11 products at the four T-based sites during the daytime, with a mean bias of -2.82 K and a mean RMSE of 3.82 K, whereas the MYD21 LST product has a mean bias and RMSE of -0.51 and 2.53 K, respectively. The LST is also underestimated at night by the C6 MYD11 products at the four T-based sites, with a mean bias of -1.40 K and a mean RMSE of 1.72 K, whereas the MYD21 LST product has a mean bias and RMSE of 0.23 and 1.01 K, respectively. For the R-based validation, the MYD11 results are associated with large negative biases during both daytime and nighttime at three sand dune sites and biases within 1 K at the other seven sites, whereas the MYD21 results are more consistent at all ten sand dune sites, with a mean bias of 0.45 and 0.70 K for daytime and nighttime, respectively. The emissivities for these two products in MODIS bands 31 and 32 were compared with each other and then compared with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity and laboratory emissivity. The results indicate that the emissivities in MODIS bands 31 and 32 of MYD11 at the four T-based and three of the R-based validation sites are overestimated and result in LST underestimation, whereas the emissivities of MYD21 are more consistent with the laboratory emissivity. Besides, an experiment was carried out to demonstrate that the physically retrieved dynamic emissivity of the MYD21 product can be utilized to improve the accuracy of the split-window (SW) algorithm for barren surfaces, making it a valuable data source for retrieving LST from different remote sensing data.

ACS Style

Hua Li; Ruibo Li; Yikun Yang; Biao Cao; Zunjian Bian; Tian Hu; Yongming Du; Lin Sun; Qinhuo Liu. Temperature-Based and Radiance-Based Validation of the Collection 6 MYD11 and MYD21 Land Surface Temperature Products Over Barren Surfaces in Northwestern China. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 1794 -1807.

AMA Style

Hua Li, Ruibo Li, Yikun Yang, Biao Cao, Zunjian Bian, Tian Hu, Yongming Du, Lin Sun, Qinhuo Liu. Temperature-Based and Radiance-Based Validation of the Collection 6 MYD11 and MYD21 Land Surface Temperature Products Over Barren Surfaces in Northwestern China. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (2):1794-1807.

Chicago/Turabian Style

Hua Li; Ruibo Li; Yikun Yang; Biao Cao; Zunjian Bian; Tian Hu; Yongming Du; Lin Sun; Qinhuo Liu. 2020. "Temperature-Based and Radiance-Based Validation of the Collection 6 MYD11 and MYD21 Land Surface Temperature Products Over Barren Surfaces in Northwestern China." IEEE Transactions on Geoscience and Remote Sensing 59, no. 2: 1794-1807.

Journal article
Published: 05 June 2020 in Remote Sensing
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Surface upward longwave radiation (SULR) is a critical component in the calculation of the Earth’s surface radiation budget. Multiple clear-sky SULR estimation methods have been developed for high-spatial resolution satellite observations. Here, we comprehensively evaluated six SULR estimation methods, including the temperature-emissivity physical methods with the input of the MYD11/MYD21 (TE-MYD11/TE-MYD21), the hybrid methods with top-of-atmosphere (TOA) linear/nonlinear/artificial neural network regressions (TOA-LIN/TOA-NLIN/TOA-ANN), and the hybrid method with bottom-of-atmosphere (BOA) linear regression (BOA-LIN). The recently released MYD21 product and the BOA-LIN—a newly developed method that considers the spatial heterogeneity of the atmosphere—is used initially to estimate SULR. In addition, the four hybrid methods were compared with simulated datasets. All the six methods were evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Surface Radiation Budget Network (SURFRAD) in situ measurements. Simulation analysis shows that the BOA-LIN is the best one among four hybrid methods with accurate atmospheric profiles as input. Comparison of all the six methods shows that the TE-MYD21 performed the best, with a root mean square error (RMSE) and mean bias error (MBE) of 14.0 and −0.2 W/m2, respectively. The RMSE of BOA-LIN, TOA-NLIN, TOA-LIN, TOA-ANN, and TE-MYD11 are equal to 15.2, 16.1, 17.2, 21.2, and 18.5 W/m2, respectively. TE-MYD21 has a much better accuracy than the TE-MYD11 over barren surfaces (NDVI < 0.3) and a similar accuracy over non-barren surfaces (NDVI ≥ 0.3). BOA-LIN is more stable over varying water vapor conditions, compared to other hybrid methods. We conclude that this study provides a valuable reference for choosing the suitable estimation method in the SULR product generation.

ACS Style

Boxiong Qin; Biao Cao; Hua Li; Zunjian Bian; Tian Hu; Yongming Du; Yingpin Yang; Qing Xiao; Qinhuo Liu. Evaluation of Six High-Spatial Resolution Clear-Sky Surface Upward Longwave Radiation Estimation Methods with MODIS. Remote Sensing 2020, 12, 1 .

AMA Style

Boxiong Qin, Biao Cao, Hua Li, Zunjian Bian, Tian Hu, Yongming Du, Yingpin Yang, Qing Xiao, Qinhuo Liu. Evaluation of Six High-Spatial Resolution Clear-Sky Surface Upward Longwave Radiation Estimation Methods with MODIS. Remote Sensing. 2020; 12 (11):1.

Chicago/Turabian Style

Boxiong Qin; Biao Cao; Hua Li; Zunjian Bian; Tian Hu; Yongming Du; Yingpin Yang; Qing Xiao; Qinhuo Liu. 2020. "Evaluation of Six High-Spatial Resolution Clear-Sky Surface Upward Longwave Radiation Estimation Methods with MODIS." Remote Sensing 12, no. 11: 1.

Journal article
Published: 13 April 2020 in IEEE Transactions on Geoscience and Remote Sensing
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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.

ACS Style

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 Style

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 (11):7643-7653.

Chicago/Turabian Style

Yongming 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.

Journal article
Published: 09 July 2019 in International Journal of Applied Earth Observation and Geoinformation
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The angular variation of land surface emissivity (LSE) is rarely considered in the split-window algorithm for retrieving land surface temperature (LST), and this can cause large uncertainties in LST retrievals. To analyze the influence of angular LSE variation on LST retrievals, we built a look-up table (LUT) of directional emissivities from the MYD21A1 LST/LSE product in the Moderate Resolution Imaging Spectroradiometer (MODIS) split-window channels. The extracted directional emissivities were then input into the MODIS generalized split-window (GSW) algorithm to substitute for the classification-based emissivities. A simulation analysis was first conducted based on the LUT. Furthermore, the LST retrievals estimated from MODIS observations using the directional emissivities were compared with those estimated using the classification-based emissivities. In-situ measurements from the US SURFRAD and China’s HiWATER networks were used to evaluate LST retrievals obtained using the two different emissivities. The results showed that angular LSE variations in the split-window channels for vegetated surfaces were generally minor during the daytime, but more pronounced during the night-time (approximately 0.005 between nadir and 60°). For barren surfaces, the angular LSE variation in the ˜12 μm channel was negligible but reached approximately 0.01 in the ˜11 μm channel. In the simulation, the influence of angular LSE variation was minor for view-zenith angles (VZA) 40° reaching approximately 1.0 and 0.7 K at VZA 65° for barren and vegetated surfaces, respectively. In the evaluation, the LST estimated using the directional emissivities showed a higher accuracy than those estimated using the classification-based emissivities, especially over barren surfaces where the improvement reached >1 K. We conclude that angular LSE variation cannot be ignored in LST estimation using the GSW algorithm when VZA is >40°, especially over barren surfaces. The accuracy of the GSW algorithm is improved pronouncedly by using the directional emissivities extracted from the MYD21 product.

ACS Style

Tian Hu; Hua Li; Biao Cao; Albert I.J.M. van Dijk; Luigi J. Renzullo; Zhihong Xu; Jun Zhou; Yongming Du; Qinhuo Liu. Influence of emissivity angular variation on land surface temperature retrieved using the generalized split-window algorithm. International Journal of Applied Earth Observation and Geoinformation 2019, 82, 101917 .

AMA Style

Tian Hu, Hua Li, Biao Cao, Albert I.J.M. van Dijk, Luigi J. Renzullo, Zhihong Xu, Jun Zhou, Yongming Du, Qinhuo Liu. Influence of emissivity angular variation on land surface temperature retrieved using the generalized split-window algorithm. International Journal of Applied Earth Observation and Geoinformation. 2019; 82 ():101917.

Chicago/Turabian Style

Tian Hu; Hua Li; Biao Cao; Albert I.J.M. van Dijk; Luigi J. Renzullo; Zhihong Xu; Jun Zhou; Yongming Du; Qinhuo Liu. 2019. "Influence of emissivity angular variation on land surface temperature retrieved using the generalized split-window algorithm." International Journal of Applied Earth Observation and Geoinformation 82, no. : 101917.

Journal article
Published: 12 June 2019 in IEEE Transactions on Geoscience and Remote Sensing
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In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.78 and -2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of -0.26 and -1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.60 and -1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products.

ACS Style

Hua Li; Qinhuo Liu; Yikun Yang; Ruibo Li; Heshun Wang; Biao Cao; Zunjian Bian; Tian Hu; Yongming Du; Lin Sun. Comparison of the MuSyQ and MODIS Collection 6 Land Surface Temperature Products Over Barren Surfaces in the Heihe River Basin, China. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 8081 -8094.

AMA Style

Hua Li, Qinhuo Liu, Yikun Yang, Ruibo Li, Heshun Wang, Biao Cao, Zunjian Bian, Tian Hu, Yongming Du, Lin Sun. Comparison of the MuSyQ and MODIS Collection 6 Land Surface Temperature Products Over Barren Surfaces in the Heihe River Basin, China. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57 (10):8081-8094.

Chicago/Turabian Style

Hua Li; Qinhuo Liu; Yikun Yang; Ruibo Li; Heshun Wang; Biao Cao; Zunjian Bian; Tian Hu; Yongming Du; Lin Sun. 2019. "Comparison of the MuSyQ and MODIS Collection 6 Land Surface Temperature Products Over Barren Surfaces in the Heihe River Basin, China." IEEE Transactions on Geoscience and Remote Sensing 57, no. 10: 8081-8094.

Journal article
Published: 25 March 2019 in IEEE Transactions on Geoscience and Remote Sensing
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Many physical models have been proposed to simulate the directional anisotropy in the thermal infrared (TIR) region over vegetation canopies to produce angular corrected directional brightness temperature or land surface temperature. However, too many input parameters obstruct their operational use. Semiempirical kernel-driven models are designed to be a tradeoff between physical accuracy and operationality. Recently, four kernel-driven models have been proposed: the first two are direct extensions of kernel models in the visible- and near-infrared region and the last two were directly designed for the TIR region. In this paper, 153 continuous and 153 discrete canopies with varying structures and temperature distributions were considered in order to evaluate their accuracies against two physical models (4SAIL and DART). Their error distribution, scatterplots, and directional anisotropy patterns are compared. LSF-Li model, followed by Ross-Li, Vinnikov, and RL model, gave the best fitting results for all the scenes. The R² of all four kernel models can reach up to 0.82 for discrete scenes; however, the kernel-driven models underestimate the hotspot effect from continuous scenes; therefore, further improvements are necessary for operational use with future TIR satellite missions.

ACS Style

Biao Cao; Jean-Philippe Gastellu-Etchegorry; Yongming Du; Hua Li; Zunjian Bian; Tian Hu; Wenjie Fan; Qing Xiao; Qinhuo Liu. Evaluation of Four Kernel-Driven Models in the Thermal Infrared Band. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 5456 -5475.

AMA Style

Biao Cao, Jean-Philippe Gastellu-Etchegorry, Yongming Du, Hua Li, Zunjian Bian, Tian Hu, Wenjie Fan, Qing Xiao, Qinhuo Liu. Evaluation of Four Kernel-Driven Models in the Thermal Infrared Band. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57 (8):5456-5475.

Chicago/Turabian Style

Biao Cao; Jean-Philippe Gastellu-Etchegorry; Yongming Du; Hua Li; Zunjian Bian; Tian Hu; Wenjie Fan; Qing Xiao; Qinhuo Liu. 2019. "Evaluation of Four Kernel-Driven Models in the Thermal Infrared Band." IEEE Transactions on Geoscience and Remote Sensing 57, no. 8: 5456-5475.

Journal article
Published: 09 July 2018 in IEEE Transactions on Geoscience and Remote Sensing
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A new directional canopy emissivity model (CE-P) based on spectral invariants is proposed in this paper. First, we prove the existence of the spectral invariant properties in the thermal infrared (TIR) band using a Monte Carlo model. Based on it, the equation of the new model is derived from the perspective of absorption. In this expression, single-scattering and multiscattering effects are separated analytically in the TIR band. We find that the overall contribution of multiple scatterings is less than 0.005 when the component emissivities are over 0.90, and the overall contribution decreases with increasing leaf or soil emissivity. Furthermore, the new model can avoid the logical difficulty encountered when using the traditional cavity effect factor to simulate the emissivity of a sparse vegetation canopy. The results of 4SAIL and Discrete Anisotropic Radiative Transfer (DART) are selected to do cross validation. The CE-P can achieve a high accuracy compared with 4SAIL and DART, with an absolute bias less than 0.002 when the leaf (soil) emissivity is equal to 0.98 (0.94). Four widely used analytical models are selected for comparison. The resulting accuracies of these models are ordered from CE-P to REN15, FR97, FR02, and VALOR96 with the most serious error up to 0.002, 0.002, 0.007, 0.013, and 0.014, respectively. Three main conclusions are obtained through the sensitivity analysis: the multiscattering between vegetation and the background can be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), the second and higher order scattering within the vegetation can also be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), and the single-scattering effect within the canopy should be considered which can be calculated using three view factors.

ACS Style

Biao Cao; Mingzhu Guo; Wenjie Fan; Xiru Xu; Jingjing Peng; Huazhong Ren; Yongming Du; Hua Li; Zunjian Bian; Tian Hu; Qing Xiao; Qinhuo Liu. A New Directional Canopy Emissivity Model Based on Spectral Invariants. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 6911 -6926.

AMA Style

Biao Cao, Mingzhu Guo, Wenjie Fan, Xiru Xu, Jingjing Peng, Huazhong Ren, Yongming Du, Hua Li, Zunjian Bian, Tian Hu, Qing Xiao, Qinhuo Liu. A New Directional Canopy Emissivity Model Based on Spectral Invariants. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (12):6911-6926.

Chicago/Turabian Style

Biao Cao; Mingzhu Guo; Wenjie Fan; Xiru Xu; Jingjing Peng; Huazhong Ren; Yongming Du; Hua Li; Zunjian Bian; Tian Hu; Qing Xiao; Qinhuo Liu. 2018. "A New Directional Canopy Emissivity Model Based on Spectral Invariants." IEEE Transactions on Geoscience and Remote Sensing 56, no. 12: 6911-6926.

Journal article
Published: 26 June 2018 in IEEE Transactions on Geoscience and Remote Sensing
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Soil semiempirical dielectric models (SEMs) are powerful, and they are generally considered a useful hybrid of both empirical and physical models. In this paper, the Wang-Schmugge dielectric model is improved to more accurately estimate the relative complex dielectric constants (CDCs) of moist soils. Instead of the Debye relaxation spectrum of liquid water located outside of the soil (i.e., free out-of-soil water) adopted in the Wang-Schmugge model, the Debye relaxation formula related to the free-water component inside the soil [i.e., free soil water (FSW)], which is correlated with the soil texture, is employed in the improved SEM. In addition, the effective conductivity loss term related to both soil texture and soil moisture is introduced to explain the ionic conductivity losses of FSW. Since the soil moisture influence is reduced at high frequencies, the effective conductivity loss term related to only the soil texture is also analyzed for 14-18 GHz. As in the Wang-Schmugge model, the relative CDC of bound soil water varies with the soil volumetric moisture content when the soil moisture is lower than the maximum bound water fraction in the new model, which takes a different approach than the Mironov mineralogy-based SEM. The proposed model obtains better fitting results than the three most widely employed SEMs. The improved model exhibits a significantly improved accuracy with a higher correlation coefficient (R²), a closer 1:1 relationship, and a lower root-mean-square error, including in the L-band, and especially in the imaginary part of the L-band.

ACS Style

Jing Liu; Qinhuo Liu; Hua Li; Yongming Du; Biao Cao. An Improved Microwave Semiempirical Model for the Dielectric Behavior of Moist Soils. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 6630 -6644.

AMA Style

Jing Liu, Qinhuo Liu, Hua Li, Yongming Du, Biao Cao. An Improved Microwave Semiempirical Model for the Dielectric Behavior of Moist Soils. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (11):6630-6644.

Chicago/Turabian Style

Jing Liu; Qinhuo Liu; Hua Li; Yongming Du; Biao Cao. 2018. "An Improved Microwave Semiempirical Model for the Dielectric Behavior of Moist Soils." IEEE Transactions on Geoscience and Remote Sensing 56, no. 11: 6630-6644.

Journal article
Published: 10 May 2018 in Remote Sensing
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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.

ACS Style

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 Style

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 (5):736.

Chicago/Turabian Style

Zunjian 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.

Addendum
Published: 11 October 2017 in Remote Sensing
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After publication of the research paper [1], it was found that funding information was missing from the Acknowledgment part

ACS Style

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 Style

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 (10):1039.

Chicago/Turabian Style

Zunjian 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.

Journal article
Published: 30 July 2017 in Remote Sensing
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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.

ACS Style

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 Style

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 (8):780.

Chicago/Turabian Style

Zunjian 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.

Journal article
Published: 29 May 2015 in Remote Sensing
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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.

ACS Style

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 Style

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 (6):7080-7104.

Chicago/Turabian Style

Jinxiong 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.