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Tian Hu
Environmental Futures Research Institute, School of Environment and Science, Griffith University, Nathan, QLD, 4111, Australia

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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: 06 August 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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A long time-series land surface temperature (LST) product is useful for ecological and environmental studies. However, current LST products cannot provide a global coverage at a fine spatial resolution (~ 100 m) over a long period (> 30 years). Landsat series satellites that have been launched since 1972 provide a unique opportunity to fill the gap. Here, we proposed a single-channel framework for producing global long time-series Landsat LST retrievals on Google Earth Engine (GEE) cloud computing platform. This framework unifies the LST, LSE and AWV estimation algorithms, as well as the emissivity and atmospheric input data for the Landsat LST retrievals from the entire Landsat thermal infrared image archive. In-situ measurements from the NOAA surface radiation budget (SURFRAD) network and the MODIS LST products were employed to evaluate Landsat LST retrievals using the proposed framework over land and water surfaces, respectively. In total, 1260 clear-sky LST samples were collected from the Landsat 5—8 series after spatiotemporal registration with six SURFRAD sites, and the average bias and root-mean-square error (RMSE) were 0.14 K and 1.93 K, respectively. Inter-comparison between Landsat and MODIS LST retrievals based on 100 clear-sky scenes over 12 inland lakes showed an average bias of 0.17 K and RMSE of 1.11 K. We conclude that the proposed single-channel framework can produce Landsat LST with high accuracy following a simple yet robust way. Implementation of the single-channel method on GEE shows promise in providing the community with freely accessible and global long time-series (>30 years) LST data.

ACS Style

Mengmeng Wang; Zhengjia Zhang; Tian Hu; Guizhou Wang; Guojin He; Zhaoming Zhang; Hua Li; Zhijie Wu; Xiuguo Liu. An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 4689 -4701.

AMA Style

Mengmeng Wang, Zhengjia Zhang, Tian Hu, Guizhou Wang, Guojin He, Zhaoming Zhang, Hua Li, Zhijie Wu, Xiuguo Liu. An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):4689-4701.

Chicago/Turabian Style

Mengmeng Wang; Zhengjia Zhang; Tian Hu; Guizhou Wang; Guojin He; Zhaoming Zhang; Hua Li; Zhijie Wu; Xiuguo Liu. 2020. "An Efficient Framework for Producing Landsat-Based Land Surface Temperature Data Using Google Earth Engine." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 4689-4701.

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: 24 May 2020 in International Journal of Applied Earth Observation and Geoinformation
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Monitoring agricultural drought effectively and timely is important to support drought management and food security. Effective drought monitoring requires a suite of drought indices to capture the evolution process of drought. Thermal infrared signals respond rapidly to vegetation water stress, thus being regarded useful for drought monitoring at the early stage. Several temperature-based drought indices have been developed considering the role of land surface temperature (LST) in surface energy and water balance. Here, we compared the recently proposed Temperature Rise Index (TRI) with several agricultural drought indices that also use thermal infrared observations, including Temperature Condition Index (TCI), Vegetation Health Index (VHI) and satellite-derived evapotranspiration ratio anomaly (ΔfRET) for a better understanding of these thermal infrared drought indices. To do so, we developed a new method for calculating TRI directly from the top-of-atmosphere brightness temperatures in the two split-window channels (centered around ∼11 and 12 μm) rather than from LST. TRI calculated using the Himawari-8 brightness temperatures (TRI_BT) and LST retrievals (TRI_LST), along with the other LST-based indices, were calculated for the growing season (July–October) of 2015−2019 over the Australian wheatbelt. An evaluation was conducted by spatiotemporally comparing the indices with the drought indices used by the Australian Bureau of Meteorology in the official drought reports: the Precipitation Condition Index (PCI) and the Soil Moisture Condition Index (SMCI). All the LST-based drought indices captured the wet conditions in 2016 and dry conditions in 2019 clearly. Ranking of Pearson correlations of the LST-based indices with regards to PCI and SMCI produced very similar results. TRI_BT and TRI_LST showed the best agreement with PCI and SMCI (r > 0.4). TCI and VHI presented lower consistency with PCI and SMCI compared with TRI_BT and TRI_LST. ΔfRET had weaker correlations than the other LST-based indices in this case study, possibly because of outliers affecting the scaling procedure. The capability of drought early warning for TRI was demonstrated by comparing with the monthly time series of the greenness index Vegetation Condition Index (VCI) in a case study of 2018 considering the relatively slow response of the greenness index to drought. TRI_BT and TRI_LST had a lead of one month in showing the changing dryness conditions compared with VCI. In addition, the LST-based indices were correlated with annual wheat yield. Compared to wheat yields, all LST-based indices had a peak correlation in September. TRI_BT and TRI_LST had strong peak and average correlations with wheat yield (r ≥ 0.8). We conclude that TRI has promise for agricultural drought early warning, and TRI_BT appears to be a good candidate for efficient operational drought early warning given the readily accessible inputs and simple calculation approach.

ACS Style

Tian Hu; Albert I.J.M. van Dijk; Luigi J. Renzullo; Zhihong Xu; Jie He; Siyuan Tian; Jun Zhou; Hua Li. On agricultural drought monitoring in Australia using Himawari-8 geostationary thermal infrared observations. International Journal of Applied Earth Observation and Geoinformation 2020, 91, 102153 .

AMA Style

Tian Hu, Albert I.J.M. van Dijk, Luigi J. Renzullo, Zhihong Xu, Jie He, Siyuan Tian, Jun Zhou, Hua Li. On agricultural drought monitoring in Australia using Himawari-8 geostationary thermal infrared observations. International Journal of Applied Earth Observation and Geoinformation. 2020; 91 ():102153.

Chicago/Turabian Style

Tian Hu; Albert I.J.M. van Dijk; Luigi J. Renzullo; Zhihong Xu; Jie He; Siyuan Tian; Jun Zhou; Hua Li. 2020. "On agricultural drought monitoring in Australia using Himawari-8 geostationary thermal infrared observations." International Journal of Applied Earth Observation and Geoinformation 91, no. : 102153.

Erratum
Published: 21 January 2020 in Remote Sensing of Environment
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ACS Style

Tian Hu; Luigi J. Renzullo; Albert I.J.M. Van Dijk; Jie He; Siyuan Tian; Zhihong Xu; Jun Zhou; Tengjiao Liu; Qinhuo Liu. Corrigendum to “Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals” [Remote Sens. Environ. 236 (January 2020) 111419]. Remote Sensing of Environment 2020, 239, 111661 .

AMA Style

Tian Hu, Luigi J. Renzullo, Albert I.J.M. Van Dijk, Jie He, Siyuan Tian, Zhihong Xu, Jun Zhou, Tengjiao Liu, Qinhuo Liu. Corrigendum to “Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals” [Remote Sens. Environ. 236 (January 2020) 111419]. Remote Sensing of Environment. 2020; 239 ():111661.

Chicago/Turabian Style

Tian Hu; Luigi J. Renzullo; Albert I.J.M. Van Dijk; Jie He; Siyuan Tian; Zhihong Xu; Jun Zhou; Tengjiao Liu; Qinhuo Liu. 2020. "Corrigendum to “Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals” [Remote Sens. Environ. 236 (January 2020) 111419]." Remote Sensing of Environment 239, no. : 111661.

Journal article
Published: 19 November 2019 in Remote Sensing of Environment
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Drought indices based on thermal remote sensing have been developed and have merit for effective early warning of agricultural droughts, but approaches so far are relatively complex or sensitive to land surface temperature (LST) estimation uncertainties. Here, we propose the temperature rise index (TRI), a drought index that is comparatively robust and easy to calculate, as the anomaly of the intrinsic morning rise of LST. The underlying principle is that the rate of LST rise between 1.5 and 3.5 h after the sunrise is approximately linear and occurs more rapidly under dry conditions than under wet conditions over vegetated surfaces as a consequence of stomatal control. TRI during the growing seasons of 2010–2014 was calculated over the Australian wheatbelt from LST retrievals from the geostationary Multifunction Transport Satellite-2 (MTSAT-2) instrument. The calculated TRI was compared with indices based on precipitation integrated over 1-, 3- and 6-month time scales, on Soil Moisture and Ocean Salinity (SMOS) soil moisture derived from passive microwave remote sensing, and on vegetation condition (normalized difference vegetation index, NDVI) derived from optical remote sensing. The various indices were also compared to annual wheat yield over large areas. The correlation coefficient between TRI and precipitation anomaly that serves as an operational drought index in Australia was above 0.6 in general with 3-month integrative time scale for precipitation. TRI produced spatiotemporal dryness patterns that were very similar to those in soil moisture, but with more detail due to its finer resolution. A time lag of >1 month was found between TRI and observed vegetation condition, supporting the use of TRI in early warning. Among the compared drought indices, TRI explained the largest fraction (35%) of wheat yield variations. TRI correlations with wheat yields peaked higher and earlier by almost one month in comparison to other indices. We conclude that the thermal drought index proposed here shows considerable potential for use in drought early warning as an effective complement.

ACS Style

Tian Hu; Luigi J. Renzullo; Albert I.J.M. van Dijk; Jie He; Siyuan Tian; Zhihong Xu; Jun Zhou; Tengjiao Liu; Qinhuo Liu. Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals. Remote Sensing of Environment 2019, 236, 111419 .

AMA Style

Tian Hu, Luigi J. Renzullo, Albert I.J.M. van Dijk, Jie He, Siyuan Tian, Zhihong Xu, Jun Zhou, Tengjiao Liu, Qinhuo Liu. Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals. Remote Sensing of Environment. 2019; 236 ():111419.

Chicago/Turabian Style

Tian Hu; Luigi J. Renzullo; Albert I.J.M. van Dijk; Jie He; Siyuan Tian; Zhihong Xu; Jun Zhou; Tengjiao Liu; Qinhuo Liu. 2019. "Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals." Remote Sensing of Environment 236, no. : 111419.

Review
Published: 01 October 2019 in Remote Sensing of Environment
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Biao Cao; Qinhuo Liu; Yongming Du; Jean-Louis Roujean; Jean-Philippe Gastellu-Etchegorry; Isabel F. Trigo; Wenfeng Zhan; Yunyue Yu; Jie Cheng; Frederic Jacob; Jean-Pierre Lagouarde; Zunjian Bian; Hua Li; Tian Hu; Qing Xiao. A review of earth surface thermal radiation directionality observing and modeling: Historical development, current status and perspectives. Remote Sensing of Environment 2019, 232, 1 .

AMA Style

Biao Cao, Qinhuo Liu, Yongming Du, Jean-Louis Roujean, Jean-Philippe Gastellu-Etchegorry, Isabel F. Trigo, Wenfeng Zhan, Yunyue Yu, Jie Cheng, Frederic Jacob, Jean-Pierre Lagouarde, Zunjian Bian, Hua Li, Tian Hu, Qing Xiao. A review of earth surface thermal radiation directionality observing and modeling: Historical development, current status and perspectives. Remote Sensing of Environment. 2019; 232 ():1.

Chicago/Turabian Style

Biao Cao; Qinhuo Liu; Yongming Du; Jean-Louis Roujean; Jean-Philippe Gastellu-Etchegorry; Isabel F. Trigo; Wenfeng Zhan; Yunyue Yu; Jie Cheng; Frederic Jacob; Jean-Pierre Lagouarde; Zunjian Bian; Hua Li; Tian Hu; Qing Xiao. 2019. "A review of earth surface thermal radiation directionality observing and modeling: Historical development, current status and perspectives." Remote Sensing of Environment 232, no. : 1.

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: 24 April 2019 in Remote Sensing of Environment
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The land surface emissivity (LSE) in the MYD21 product contains the effects of viewing zenith angle. The influence of the angular variation of LSE on the surface upwelling longwave radiation (SULR) estimation is still unexplored at the satellite scale. We performed statistical analyses of MYD21 emissivity retrievals over different land surface types for three longwave bands centred around 8.55 μm (Band 29), 11 μm (Band 31) and 12 μm (Band 32), respectively. A look-up table was generated to describe the angular variations for both single-band and broadband emissivities. The results showed that the angular variation of directional emissivity in Band 29 could reach up to 0.03, but was <0.01 for Bands 31 and 32. The angular variation in broadband emissivity was intermediate to that for individual bands. In all cases, the directional emissivity was greatest and symmetric around nadir. By integrating the directional broadband emissivity, the influence of angular variation of the LSE on estimated SULR was quantified using simulation and measurements at seven stations from the US surface radiation budget network (SURFRAD). The difference between the directional and integrated hemispheric broadband emissivity was within 0.01. As a result, the influence of angular variation of LSE on the SULR estimation was modest. For the SURFRAD stations, the differences of root-mean-square error (RMSE) before and after considering the angular variation of LSE were generally <1 W m−2. We conclude that the angular variation of broadband emissivity is not pronounced because of the small linear weight for Band 29 in the calculation of broadband emissivity. Ignoring the anisotropy of emissivity does not introduce large errors in SULR estimation generally.

ACS Style

Tian Hu; Luigi J. Renzullo; Biao Cao; Albert Van Dijk; Yongming Du; Hua Li; Jie Cheng; Zhihong Xu; Jun Zhou; Qinhuo Liu. Directional variation in surface emissivity inferred from the MYD21 product and its influence on estimated surface upwelling longwave radiation. Remote Sensing of Environment 2019, 228, 45 -60.

AMA Style

Tian Hu, Luigi J. Renzullo, Biao Cao, Albert Van Dijk, Yongming Du, Hua Li, Jie Cheng, Zhihong Xu, Jun Zhou, Qinhuo Liu. Directional variation in surface emissivity inferred from the MYD21 product and its influence on estimated surface upwelling longwave radiation. Remote Sensing of Environment. 2019; 228 ():45-60.

Chicago/Turabian Style

Tian Hu; Luigi J. Renzullo; Biao Cao; Albert Van Dijk; Yongming Du; Hua Li; Jie Cheng; Zhihong Xu; Jun Zhou; Qinhuo Liu. 2019. "Directional variation in surface emissivity inferred from the MYD21 product and its influence on estimated surface upwelling longwave radiation." Remote Sensing of Environment 228, no. : 45-60.

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: 14 December 2018 in Journal of Geophysical Research: Atmospheres
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The single‐channel (SC) algorithm has been widely used to retrieve land surface temperature (LST) from Landsat series data for its simplicity and requirement of only one thermal infrared channel. The main error sources of the existing SC algorithms are the linearization of the Planck's function and atmospheric correction. This paper proposed a practical single‐channel (PSC) algorithm to retrieve LST from Landsat series data aiming at avoiding the aforementioned error sources. The sensitivity of the PSC algorithm to the input parameters was analyzed. The performance of the proposed PSC algorithm was compared with the most commonly used single‐channel algorithm (the generalized single‐channel, GSC) using a simulation dataset and satellite measurements. Results showed that the PSC algorithm was less sensitive to uncertainties in the input parameters than the GSC algorithm. When validated with the simulation data set, the root mean‐square error (RMSE) of the PSC algorithm was 1.23 K, with an improvement by 0.57 K compared with the GSC algorithm. For the validation with 71 clear‐sky Landsat 8 images, the RMSE of the PSC algorithm was 1.77 K when using the measurements from US surface radiation budget network as real values. Compared with the GSC algorithm, the RMSE improvement for the PSC algorithm was 0.47 K. We conclude that the PSC algorithm is more accurate than the GSC algorithm and the sensitivity to input parameters in the PSC algorithm is weaker than in the GSC algorithm.

ACS Style

M. Wang; Z. Zhang; T. Hu; X. Liu. A Practical Single‐Channel Algorithm for Land Surface Temperature Retrieval: Application to Landsat series data. Journal of Geophysical Research: Atmospheres 2018, 1 .

AMA Style

M. Wang, Z. Zhang, T. Hu, X. Liu. A Practical Single‐Channel Algorithm for Land Surface Temperature Retrieval: Application to Landsat series data. Journal of Geophysical Research: Atmospheres. 2018; ():1.

Chicago/Turabian Style

M. Wang; Z. Zhang; T. Hu; X. Liu. 2018. "A Practical Single‐Channel Algorithm for Land Surface Temperature Retrieval: Application to Landsat series data." Journal of Geophysical Research: Atmospheres , no. : 1.

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: 28 April 2017 in IEEE Transactions on Geoscience and Remote Sensing
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Surface upward longwave radiation (SULR) is a significant component of the surface radiation budget and is closely linked with evapotranspiration, soil moisture, and surface cooling on clear nights. Therefore, accurately estimating SULR is essential to better understand its spatiotemporal dynamics or to characterize the thermal environment of a given land surface. Currently, most methods for estimating SULR (including the physical and hybrid methods) fail to account for the thermal anisotropy, which can introduce significant errors into the calculation. We previously proposed the combined algorithm that considers the thermal anisotropy to more accurately estimate the SULR. However, this proposed method has several shortcomings. For example, it considers the directionality of the emissivity and the effective temperature separately under the support of a parametric directional emissivity model. However, the directional emissivity model is not maturely developed for different land surface types, especially on nonvegetated surfaces. And the separation of land surface temperature and emissivity may undermine the estimation accuracy. Furthermore, this proposed method requires a series of input parameters that is not always available, limiting its applicability. In this paper, we present a refined algorithm that uses a kernel-driven model and the technique of band conversion to calculate the SULR directly based on surface-leaving radiances. This direct physical algorithm is then applied to the Wide-angle infrared Dual-mode line/area Array Scanner data set and validated using longwave radiation data collected by automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results of these tests suggest that the direct algorithm works effectively. The root-mean-square error (RMSE) and mean bias error (MBE) of the direct algorithm on maize surfaces are 4.417 and 0.474 W· m⁻², respectively. When the thermal anisotropy is incorporated, the RMSE and absolute MBE decrease by a maximum of 4.734 and 7.414 W· m⁻², respectively. Different land types yield different results: for vegetable surfaces, the estimation biases of the direct model are approximately -2 W· m⁻², whereas orchard surfaces yield biases are between -2 and -3.5 W· m⁻², and village surfaces yield biases exceeding -10 W· m⁻². These differences can be attributed to the varying effects of the kernel-driven model across different types of land surfaces. The RMSE and absolute MBE obtained using the direct algorithm are slightly smaller (0.587 and 1.685 W· m⁻², respectively) than those obtained using the combined algorithm; they are also smaller than the results of the traditional temperature-emissivity algorithm (by 8.7 and 11.7 W· m⁻², respectively).Griffith Sciences, School of Natural SciencesNo Full Tex

ACS Style

Tian Hu; Biao Cao; Yongming Du; Hua Li; Cong Wang; Zunjian Bian; Donglian Sun; Qinhuo Liu. Estimation of Surface Upward Longwave Radiation Using a Direct Physical Algorithm. IEEE Transactions on Geoscience and Remote Sensing 2017, 55, 4412 -4426.

AMA Style

Tian Hu, Biao Cao, Yongming Du, Hua Li, Cong Wang, Zunjian Bian, Donglian Sun, Qinhuo Liu. Estimation of Surface Upward Longwave Radiation Using a Direct Physical Algorithm. IEEE Transactions on Geoscience and Remote Sensing. 2017; 55 (8):4412-4426.

Chicago/Turabian Style

Tian Hu; Biao Cao; Yongming Du; Hua Li; Cong Wang; Zunjian Bian; Donglian Sun; Qinhuo Liu. 2017. "Estimation of Surface Upward Longwave Radiation Using a Direct Physical Algorithm." IEEE Transactions on Geoscience and Remote Sensing 55, no. 8: 4412-4426.

Journal article
Published: 27 July 2016 in IEEE Transactions on Geoscience and Remote Sensing
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Tian Hu; Yongming Du; Biao Cao; Hua Li; Zunjian Bian; Donglian Sun; Qinhuo Liu. Estimation of Upward Longwave Radiation From Vegetated Surfaces Considering Thermal Directionality. IEEE Transactions on Geoscience and Remote Sensing 2016, 54, 6644 -6658.

AMA Style

Tian Hu, Yongming Du, Biao Cao, Hua Li, Zunjian Bian, Donglian Sun, Qinhuo Liu. Estimation of Upward Longwave Radiation From Vegetated Surfaces Considering Thermal Directionality. IEEE Transactions on Geoscience and Remote Sensing. 2016; 54 (11):6644-6658.

Chicago/Turabian Style

Tian Hu; Yongming Du; Biao Cao; Hua Li; Zunjian Bian; Donglian Sun; Qinhuo Liu. 2016. "Estimation of Upward Longwave Radiation From Vegetated Surfaces Considering Thermal Directionality." IEEE Transactions on Geoscience and Remote Sensing 54, no. 11: 6644-6658.

Journal article
Published: 22 May 2015 in Remote Sensing
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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.

ACS Style

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 Style

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 (5):6489-6509.

Chicago/Turabian Style

Tian 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.