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Qing Xiao
State key lab of remote sensing science, Institute of remote sensing an digital earth, beijing, China, 100101

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Journal article
Published: 18 August 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Validation of satellite products is necessary to enable end-users to know the fitness and exploit their full potential in supporting the decision-making process. Representative in situ observations need to be obtained to make an independent, unified, standard, and traceable validation due to the spatial scale mismatch between satellite and in situ-based observations. Under the demand of validating various pixel scales of satellite products ranging from a few meters to kilometers, this paper proposed a multiscale nested sampling method (MNSM), which is able to provide representative ground observations at various pixel scales under a unified accuracy requirement. The basic idea is to extract the spatiotemporal variation information of surface variables using long time sequences of high-resolution images. And the sample locations were optimized with a random combination method using an index of spatiotemporal representativeness. The optimal sampling was carried out from a coarse pixel scale to a high pixel scale with the top-down nested approach. The final point in situ observations can represent surface variables at different pixel scales with a unified and predetermined accuracy. It was found that the representativeness error of a single in situ measurement and the minimum number of required filed plots under a unified sampling accuracy requirement are partly determined by spatial heterogeneity and partly determined by the spatial scale mismatch between the plot size and the pixel size to be matched. The sampling method is applicable to many other surface variables (e.g., soil moisture, biomass, albedo, GPP, and LAI) that exhibiting spatial heterogeneity.

ACS Style

Xiaodan Wu; Jianguang Wen; Qing Xiao; Jingping Wang; Dujuan Ma; Xinwen Lin. A Multiscale Nested Sampling Method for representative albedo observations at various pixel scales. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.

AMA Style

Xiaodan Wu, Jianguang Wen, Qing Xiao, Jingping Wang, Dujuan Ma, Xinwen Lin. A Multiscale Nested Sampling Method for representative albedo observations at various pixel scales. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.

Chicago/Turabian Style

Xiaodan Wu; Jianguang Wen; Qing Xiao; Jingping Wang; Dujuan Ma; Xinwen Lin. 2021. "A Multiscale Nested Sampling Method for representative albedo observations at various pixel scales." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.

Journal article
Published: 27 July 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Validation of satellite albedo products is an essential step because their quantitative application lie in their ability to record the real state of the earth surface. Upscaling in situ measurements to the corresponding pixel scale is necessary due to the spatial scale mismatch between in situ and satellite measurements. Machine learning-based models have been increasingly used for upscaling because they can yield more reliable results than traditional methods. Nevertheless, the main controlling factors on upscaled results have rarely been discussed. This article explores the control factors that bring uncertainties to the upscaled results based on machine learning models. Three machine learning models, including random forest (RF), k-nearest neighbor (KNN), and Cubist models, were selected to upscale single site in situ-based albedo to the coarse pixel scale. The upscaled results were carefully assessed through comparison with pixel scale albedo reference. The results indicate that the accuracy of upscaled results depends on the machine learning models, the inclusion of key variables related to albedo, the dataset selection of these variables, the amount of training data, and the sensitivity of machine learning models to these factors. Despite the dependence on control factors, the machine learning-based upscaling methods generally have excellent applicability across different spatial scales and over other untrained areas. Therefore, they open the door to generating a time series of globally, spatially continuous distributed reference datasets with sufficient length, consistency, and continuity to adequately fulfill the requirement of a comprehensive validation.

ACS Style

Jingping Wang; Xiaodan Wu; Jianguang Wen; Qing Xiao; Baochang Gong; Dujuan Ma; Yurong Cui; Xingwen Lin; Yunfei Bao. Upscaling in Situ Site-Based Albedo Using Machine Learning Models: Main Controlling Factors on Results. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Jingping Wang, Xiaodan Wu, Jianguang Wen, Qing Xiao, Baochang Gong, Dujuan Ma, Yurong Cui, Xingwen Lin, Yunfei Bao. Upscaling in Situ Site-Based Albedo Using Machine Learning Models: Main Controlling Factors on Results. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Jingping Wang; Xiaodan Wu; Jianguang Wen; Qing Xiao; Baochang Gong; Dujuan Ma; Yurong Cui; Xingwen Lin; Yunfei Bao. 2021. "Upscaling in Situ Site-Based Albedo Using Machine Learning Models: Main Controlling Factors on Results." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

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: 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: 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: 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: 07 April 2020 in Remote Sensing
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As an essential climate variable (ECV), land surface albedo plays an important role in the Earth surface radiation budget and regional or global climate change. The Tibetan Plateau (TP) is a sensitive environment to climate change, and understanding its albedo seasonal and inter-annual variations is thus important to help capture the climate change rules. In this paper, we analyzed the large-scale spatial patterns, temporal trends, and seasonal variability of land surface albedo overall the TP, based on the moderate resolution imaging spectroradiometer (MODIS) MCD43 albedo products from 2001 to 2019. Specifically, we assessed the correlations between the albedo anomaly and the anomalies of normalized difference vegetation index (NDVI), the fraction of snow cover (snow cover), and land surface temperature (LST). The results show that there are larger albedo variations distributed in the mountainous terrain of the TP. Approximately 10.06% of the land surface is identified to have been influenced by the significant albedo variation from the year 2001 to 2019. The yearly averaged albedo was decreased significantly at a rate of 0.0007 (Sen’s slope) over the TP. Additionally, the yearly average snow cover was decreased at a rate of 0.0756. However, the yearly average NDVI and LST were increased with slopes of 0.0004 and 0.0253 over the TP, respectively. The relative radiative forcing (RRF) caused by the land cover change (LCC) is larger than that caused by gradual albedo variation in steady land cover types. Overall, the RRF due to gradual albedo variation varied from 0.0005 to 0.0170 W/m2, and the RRF due to LCC variation varied from 0.0037 to 0.0243 W/m2 during the years 2001 to 2019. The positive RRF caused by gradual albedo variation or the LCC can strengthen the warming effects in the TP. The impact of the gradual albedo variations occurring in the steady land cover types was very low between 2001 and 2019 because the time series was short, and it therefore cannot be neglected when examining radiative forcing for a long time series regarding climate change.

ACS Style

Xingwen Lin; Jianguang Wen; Qinhuo Liu; DongQin You; Shengbiao Wu; Dalei Hao; Qing Xiao; Zhaoyang Zhang; Zhenzhen Zhang. Spatiotemporal Variability of Land Surface Albedo over the Tibet Plateau from 2001 to 2019. Remote Sensing 2020, 12, 1188 .

AMA Style

Xingwen Lin, Jianguang Wen, Qinhuo Liu, DongQin You, Shengbiao Wu, Dalei Hao, Qing Xiao, Zhaoyang Zhang, Zhenzhen Zhang. Spatiotemporal Variability of Land Surface Albedo over the Tibet Plateau from 2001 to 2019. Remote Sensing. 2020; 12 (7):1188.

Chicago/Turabian Style

Xingwen Lin; Jianguang Wen; Qinhuo Liu; DongQin You; Shengbiao Wu; Dalei Hao; Qing Xiao; Zhaoyang Zhang; Zhenzhen Zhang. 2020. "Spatiotemporal Variability of Land Surface Albedo over the Tibet Plateau from 2001 to 2019." Remote Sensing 12, no. 7: 1188.

Journal article
Published: 18 December 2019 in IEEE Transactions on Geoscience and Remote Sensing
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The in situ measurements from globally distributed sparse networks provide a valuable data source for the validation of satellite products. However, the representativeness errors resulting from the spatial scale mismatch between in situ-satellite measurements and surface heterogeneity have generally limited past validation to only very few spatially representative sites, which cannot meet the requirement of a comprehensive validation. In response to this challenge, this article offers a strategy for upscaling sparse in situ measurements and removing the impact of representativeness errors on the evaluation of coarse-pixel albedo products. The main idea of the upscaling method is to establish the correspondence relationship between each subpixel albedo time series within a coarse pixel and in situ albedo time series by using high-resolution albedo maps as the prior knowledge. Furthermore, the performance of the upscaling method is carefully evaluated over the sites featured by different degrees of spatial representativeness. The results indicate that the upscaling method improves the representativeness of single-site measurements with respect to a coarse pixel, and the improvement is most significant over the sites with relatively low representativeness. Therefore, the upscaling method is particularly useful for the validation at heterogeneous sites in strengthening the reliability of validation results. It is expected to open the door to maximizing the use of existing sparse networks and generating a time series of globally distributed reference data sets with sufficient length, consistency, and continuity.

ACS Style

Xiaodan Wu; Jianguang Wen; Qing Xiao; DongQin You. Upscaling of Single-Site-Based Measurements for Validation of Long-Term Coarse-Pixel Albedo Products. IEEE Transactions on Geoscience and Remote Sensing 2019, 58, 3411 -3425.

AMA Style

Xiaodan Wu, Jianguang Wen, Qing Xiao, DongQin You. Upscaling of Single-Site-Based Measurements for Validation of Long-Term Coarse-Pixel Albedo Products. IEEE Transactions on Geoscience and Remote Sensing. 2019; 58 (5):3411-3425.

Chicago/Turabian Style

Xiaodan Wu; Jianguang Wen; Qing Xiao; DongQin You. 2019. "Upscaling of Single-Site-Based Measurements for Validation of Long-Term Coarse-Pixel Albedo Products." IEEE Transactions on Geoscience and Remote Sensing 58, no. 5: 3411-3425.

Journal article
Published: 12 December 2019 in IEEE Transactions on Geoscience and Remote Sensing
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The semiempirical kernel-driven model is commonly used for global surface reflectance characterization because of its simplicity and underlying physical meaning. However, the current kernel-driven reflectance models assume that the terrain is flat and homogeneous, and can induce significant errors in the surface reflectance estimation and subsequent parameter retrievals over rugged terrain. In this study, an improved topography-coupled kernel-driven (TCKD) reflectance model with the correction of diffuse skylight effects was proposed based on the diffused-equivalent slope model (dESM) and RossThick-LiTransit (RTLT) kernel-driven model. The TCKD model's accuracy and effectiveness were evaluated using surface reflectance simulated by the radiosity approach and the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Against simulated data, the results show that the TCKD model can accurately capture the distortion of the reflectance shape and hemispherical distribution caused by the topographic effects. Compared to MODIS data, the TCKD model has an overall better performance than the RTLT model across different spatial scales and land cover types. When the mean slope is larger than 35° at the 500-m resolution, the TCKD model's near-infrared (NIR) root-mean-square error (RMSE) and the regression slope of the fitting line are 0.037 and 0.752, respectively, whereas those of the RTLT model are 0.049 and 0.645. Neglecting the diffuse skylight in the TCKD model can also lead to great bias in the reflectance retrievals. When the mean slope is 31°, as the ratio of diffuse skylight varies from 0 to 1, the NIR RMSE of the TCKD model decreases from 0.012 to 0.005, whereas that increases from 0.012 to around 0.02 if the diffuse skylight effects are neglected. These preliminary results demonstrate that the TCKD model is capable of improving the fitting ability of the kernel-driven model over rugged terrain and provides potentials for better retrieving and interpreting land surface parameters such as land surface albedo in mountainous areas.

ACS Style

Dalei Hao; Jianguang Wen; Qing Xiao; DongQin You; Yong Tang. An Improved Topography-Coupled Kernel-Driven Model for Land Surface Anisotropic Reflectance. IEEE Transactions on Geoscience and Remote Sensing 2019, 58, 2833 -2847.

AMA Style

Dalei Hao, Jianguang Wen, Qing Xiao, DongQin You, Yong Tang. An Improved Topography-Coupled Kernel-Driven Model for Land Surface Anisotropic Reflectance. IEEE Transactions on Geoscience and Remote Sensing. 2019; 58 (4):2833-2847.

Chicago/Turabian Style

Dalei Hao; Jianguang Wen; Qing Xiao; DongQin You; Yong Tang. 2019. "An Improved Topography-Coupled Kernel-Driven Model for Land Surface Anisotropic Reflectance." IEEE Transactions on Geoscience and Remote Sensing 58, no. 4: 2833-2847.

Journal article
Published: 22 May 2019 in Journal of Geophysical Research: Atmospheres
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A rigorous and reliable validation is the prerequisite and basis to properly understand the accuracy of satellite surface albedo products. However, the representativeness error of point‐scale in situ measurements, which mainly results from the spatial mismatch between in situ and satellite measurements as well as the surface heterogeneities, generally hinders an effective validation of satellite surface albedo products based on the conventional direct comparison method. Triple collocation (TC) technique has been recognized as a powerful tool to estimate the errors of satellite products without requiring the truth. Here, we evaluated the effectiveness of both the direct comparison and extended triple collocation (ETC) methods in tackling with the representativeness error of in situ measurements and characterizing the performances of satellite surface albedo products (MCD43A3 (V006), GLASS and NPP VIIRS albedos) given whether the in situ‐based pixel‐scale albedo was available or not. The ETC method is shown to be advantageous over the direct comparison based on single “point” in situ measurements, because it is less affected by representativeness errors. However, it shall not substitute for the direct comparison method based on in situ‐based pixel‐scale albedo measurements, because its effectiveness in solving representativeness errors and estimating product errors is very limited and dependent on the choice of the data in the triplets. Furthermore, the ETC technique cannot be used to estimate the true physical errors of satellite surface albedo products.

ACS Style

Xiaodan Wu; Qing Xiao; Jianguang Wen; DongQin You. Direct Comparison and Triple Collocation: Which Is More Reliable in the Validation of Coarse‐Scale Satellite Surface Albedo Products. Journal of Geophysical Research: Atmospheres 2019, 124, 5198 -5213.

AMA Style

Xiaodan Wu, Qing Xiao, Jianguang Wen, DongQin You. Direct Comparison and Triple Collocation: Which Is More Reliable in the Validation of Coarse‐Scale Satellite Surface Albedo Products. Journal of Geophysical Research: Atmospheres. 2019; 124 (10):5198-5213.

Chicago/Turabian Style

Xiaodan Wu; Qing Xiao; Jianguang Wen; DongQin You. 2019. "Direct Comparison and Triple Collocation: Which Is More Reliable in the Validation of Coarse‐Scale Satellite Surface Albedo Products." Journal of Geophysical Research: Atmospheres 124, no. 10: 5198-5213.

Journal article
Published: 11 April 2019 in IEEE Geoscience and Remote Sensing Letters
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This letter presents an experiment to explore the nonisothermal effects on temperature and emissivity separation (TES). The innovation of this experiment lies in its design, which highlights the contrast between isothermal and nonisothermal conditions in emissivity measurements. We artificially created a sharply contrasting nonisothermal soil surface using liquid nitrogen cooling and solar heating. The iterative spectrally smooth TES (ISSTES) algorithm was used to process the experimental data. The analyzed results of the experimental data show that the nonisothermal conditions have a significant effect on TES. The bias of retrieved emissivity increases with the component temperature difference as well as with wavelength. The bias around the split window band can reach up to 1% when the difference of the component temperature is 40K. Considering that 1% error in emissivity can cause approximately 1K error of retrieved land surface temperature (LST), the nonisothermal effects on emissivity cannot be ignored. We hope that this experiment will arouse attention of the nonisothermal effects on TES and call for more efforts to be devoted to this issue in the future.

ACS Style

Yongming Du; Biao Cao; Hua Li; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. An Experimental Study on Separating Temperature and Emissivity of a Nonisothermal Surface. IEEE Geoscience and Remote Sensing Letters 2019, 16, 1610 -1614.

AMA Style

Yongming Du, Biao Cao, Hua Li, Qing Xiao, Qinhuo Liu, Yijian Zeng, Zhongbo Su. An Experimental Study on Separating Temperature and Emissivity of a Nonisothermal Surface. IEEE Geoscience and Remote Sensing Letters. 2019; 16 (10):1610-1614.

Chicago/Turabian Style

Yongming Du; Biao Cao; Hua Li; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. 2019. "An Experimental Study on Separating Temperature and Emissivity of a Nonisothermal Surface." IEEE Geoscience and Remote Sensing Letters 16, no. 10: 1610-1614.

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: 18 March 2019 in Remote Sensing
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Hydro-agricultural applications often require surface soil moisture (SM) information at high spatial resolutions. In this study, daily spatial patterns of SM at a spatial resolution of 1 km over the Babao River Basin in northwestern China were mapped using a Bayesian-based upscaling algorithm, which upscaled point-scale measurements to the grid-scale (1 km) by retrieving SM information using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and topography data (including aspect and elevation data) and in situ measurements from a wireless sensor network (WSN). First, the time series of pixel-scale (1 km) representative SM information was retrieved from in situ measurements of SM, topography data, and LST. Second, Bayesian linear regression was used to calibrate the relationship between the representative SM and the WSN measurements. Last, the calibrated relationship was used to upscale a network of in situ measured SM to map spatially continuous SM at a high resolution. The upscaled SM data were evaluated against ground-based SM measurements with satisfactory accuracy—the overall correlation coefficient (r), slope, and unbiased root mean square difference (ubRMSD) values were 0.82, 0.61, and 0.025 m3/m3, respectively. Moreover, when accounting for topography, the proposed upscaling algorithm outperformed the algorithm based only on SM derived from LST (r = 0.80, slope = 0.31, and ubRMSD = 0.033 m3/m3). Notably, the proposed upscaling algorithm was able to capture the dynamics of SM under extreme dry and wet conditions. In conclusion, the proposed upscaled method can provide accurate high-resolution SM estimates for hydro-agricultural applications.

ACS Style

Lei Fan; A. Al-Yaari; Frédéric Frappart; Jennifer J. Swenson; Qing Xiao; Jianguang Wen; Rui Jin; Jian Kang; Xiaojun Li; R. Fernandez-Moran; J.-P. Wigneron. Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures. Remote Sensing 2019, 11, 656 .

AMA Style

Lei Fan, A. Al-Yaari, Frédéric Frappart, Jennifer J. Swenson, Qing Xiao, Jianguang Wen, Rui Jin, Jian Kang, Xiaojun Li, R. Fernandez-Moran, J.-P. Wigneron. Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures. Remote Sensing. 2019; 11 (6):656.

Chicago/Turabian Style

Lei Fan; A. Al-Yaari; Frédéric Frappart; Jennifer J. Swenson; Qing Xiao; Jianguang Wen; Rui Jin; Jian Kang; Xiaojun Li; R. Fernandez-Moran; J.-P. Wigneron. 2019. "Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures." Remote Sensing 11, no. 6: 656.

Journal article
Published: 16 December 2018 in Remote Sensing
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This study assessed accuracies of MCD43A3, Global Land-Surface Satellite (GLASS) and forthcoming Multi-source Data Synergized Quantitative Remote Sensing Production system (MuSyQ) albedos using ground observations and Huan Jing (HJ) data over the Heihe River Basin. MCD43A3 and MuSyQ albedos show similar high accuracies with identical root mean square errors (RMSE). Nevertheless, MuSyQ albedo is better correlated with ground measurements when sufficient valid observations are available or snow-free. The opposite happens when less than seven valid observations are available. GLASS albedo presents a larger RMSE than MCD43A3 and MuSyQ albedos in comparison with ground measurements. Over surfaces with smaller seasonal variations, MCD43A3 and MuSyQ albedos show smaller RMSEs than GLASS albedo in comparison with HJ albedo. However, for surfaces with larger temporal variations, both RMSEs and R2 of GLASS albedo are comparable with MCD43A3 and MuSyQ. Generally, MCD43A3 and MuSyQ albedos featured the same RMSEs of 0.034 and similar R2 (0.920 and 0.903, respectively), which are better than GLASS albedo (RMSE = 0.043, R2 = 0.787). However, when it comes to comparison with aggregated HJ albedo, MuSyQ and GLASS albedos are with lower RMSEs of 0.027 and 0.032 and higher R2 of 0.900 and 0.898 respectively than MCD43A3 (RMSE = 0.038, R2 = 0.836). Despite the limited geographic region of the study area, they still provide an important insight into the accuracies of three albedo products.

ACS Style

Xiaodan Wu; Jianguang Wen; Qing Xiao; DongQin You; Baocheng Dou; Xinwen Lin; Andreas Hueni. Accuracy Assessment on MODIS (V006), GLASS and MuSyQ Land-Surface Albedo Products: A Case Study in the Heihe River Basin, China. Remote Sensing 2018, 10, 2045 .

AMA Style

Xiaodan Wu, Jianguang Wen, Qing Xiao, DongQin You, Baocheng Dou, Xinwen Lin, Andreas Hueni. Accuracy Assessment on MODIS (V006), GLASS and MuSyQ Land-Surface Albedo Products: A Case Study in the Heihe River Basin, China. Remote Sensing. 2018; 10 (12):2045.

Chicago/Turabian Style

Xiaodan Wu; Jianguang Wen; Qing Xiao; DongQin You; Baocheng Dou; Xinwen Lin; Andreas Hueni. 2018. "Accuracy Assessment on MODIS (V006), GLASS and MuSyQ Land-Surface Albedo Products: A Case Study in the Heihe River Basin, China." Remote Sensing 10, no. 12: 2045.

Journal article
Published: 08 November 2018 in Remote Sensing
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The academician Xiaowen Li devoted much of his life to pursuing fundamental research in remote sensing. A pioneer in the geometric-optical modeling of vegetation canopies, his work is held in high regard by the international remote sensing community. He codeveloped the Li–Strahler geometric-optic model, and this paper was selected by a member of the International Society for Optical Engineering (SPIE) milestone series. As a chief scientist, Xiaowen Li led a scientific team that made outstanding advances in bidirectional reflectance distribution modeling, directional thermal emission modeling, comprehensive experiments, and the understanding of spatial and temporal scale effects in remote sensing information, and of quantitative inversions utilizing remote sensing data. In addition to his broad research activities, he was noted for his humility and his dedication in making science more accessible for the general public. Here, the life and academic contributions of Xiaowen Li to the field of quantitative remote sensing science are briefly reviewed.

ACS Style

Qinhuo Liu; Guangjian Yan; Ziti Jiao; Qing Xiao; Jianguang Wen; Shunlin Liang; Jindi Wang; Crystal Schaaf; Alan Strahler. From Geometric-Optical Remote Sensing Modeling to Quantitative Remote Sensing Science—In Memory of Academician Xiaowen Li. Remote Sensing 2018, 10, 1764 .

AMA Style

Qinhuo Liu, Guangjian Yan, Ziti Jiao, Qing Xiao, Jianguang Wen, Shunlin Liang, Jindi Wang, Crystal Schaaf, Alan Strahler. From Geometric-Optical Remote Sensing Modeling to Quantitative Remote Sensing Science—In Memory of Academician Xiaowen Li. Remote Sensing. 2018; 10 (11):1764.

Chicago/Turabian Style

Qinhuo Liu; Guangjian Yan; Ziti Jiao; Qing Xiao; Jianguang Wen; Shunlin Liang; Jindi Wang; Crystal Schaaf; Alan Strahler. 2018. "From Geometric-Optical Remote Sensing Modeling to Quantitative Remote Sensing Science—In Memory of Academician Xiaowen Li." Remote Sensing 10, no. 11: 1764.

Journal article
Published: 18 September 2018 in IEEE Geoscience and Remote Sensing Letters
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Accurately estimating the spatial-temporal distribution of downward surface shortwave radiation (DSSR) is essential for terrestrial ecological modeling and climate change research. The accurate georegistration of digital elevation model (DEM) has become one of the significant bottlenecks for improving the DSSR accuracy over rugged terrain. To clearly understand and quantitatively evaluate the impact of geolocation bias on the DSSR estimation under clear sky, this letter conducts a systematical simulation research in Dayekou Basin of China based on a developed remote sensing satellite-based DSSR estimation scheme over rugged terrain. The results demonstrate that the proposed approach can accurately capture the high temporal and spatial heterogeneities of DSSR, and the DSSR estimations are sensitive to geolocation bias. When the horizontal bias is lower than half a pixel, the deviations of the direct radiation could lead to above 600 W/m² due to the illumination angle effects and shadow effects. The consequence of the bias on the diffuse and reflected radiation from adjacent terrains is little because of their relatively small values and low-spatial heterogeneities under clear sky in general except for the deep valley areas. The trends of the total radiation errors with the geolocation bias are identical in different days (scenes), and the error is related to the solar zenith angle. In addition, the more rugged the terrain, the greater the influence of geolocation bias on the radiation accuracy.

ACS Style

Dalei Hao; Jianguang Wen; Qing Xiao; Shengbiao Wu; Xingwen Lin; DongQin You; Yong Tang. Impacts of DEM Geolocation Bias on Downward Surface Shortwave Radiation Estimation Over Clear-Sky Rugged Terrain: A Case Study in Dayekou Basin, China. IEEE Geoscience and Remote Sensing Letters 2018, 16, 10 -14.

AMA Style

Dalei Hao, Jianguang Wen, Qing Xiao, Shengbiao Wu, Xingwen Lin, DongQin You, Yong Tang. Impacts of DEM Geolocation Bias on Downward Surface Shortwave Radiation Estimation Over Clear-Sky Rugged Terrain: A Case Study in Dayekou Basin, China. IEEE Geoscience and Remote Sensing Letters. 2018; 16 (1):10-14.

Chicago/Turabian Style

Dalei Hao; Jianguang Wen; Qing Xiao; Shengbiao Wu; Xingwen Lin; DongQin You; Yong Tang. 2018. "Impacts of DEM Geolocation Bias on Downward Surface Shortwave Radiation Estimation Over Clear-Sky Rugged Terrain: A Case Study in Dayekou Basin, China." IEEE Geoscience and Remote Sensing Letters 16, no. 1: 10-14.

Journal article
Published: 17 September 2018 in IEEE Geoscience and Remote Sensing Letters
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Presents corrections to the paper, “Algorithms for calculating topographic parameters and their uncertainties in downward surface solar radiation (DSSR) estimation,” (Wu, S., et al), IEEE Geosci. Remote Sens. Lett., vol. 15, no. 8, pp. 1149–1153, Aug. 2017.

ACS Style

Shengbiao Wu; Jianguang Wen; DongQin You; Hailong Zhang; Qing Xiao; Qinhuo Liu. Erratum to “algorithms for calculating topographic parameters and their uncertainties in downward surface solar radiation estimation” [aug 17 1149-1153]. IEEE Geoscience and Remote Sensing Letters 2018, 16, 160 -160.

AMA Style

Shengbiao Wu, Jianguang Wen, DongQin You, Hailong Zhang, Qing Xiao, Qinhuo Liu. Erratum to “algorithms for calculating topographic parameters and their uncertainties in downward surface solar radiation estimation” [aug 17 1149-1153]. IEEE Geoscience and Remote Sensing Letters. 2018; 16 (1):160-160.

Chicago/Turabian Style

Shengbiao Wu; Jianguang Wen; DongQin You; Hailong Zhang; Qing Xiao; Qinhuo Liu. 2018. "Erratum to “algorithms for calculating topographic parameters and their uncertainties in downward surface solar radiation estimation” [aug 17 1149-1153]." IEEE Geoscience and Remote Sensing Letters 16, no. 1: 160-160.

Journal article
Published: 27 August 2018 in Journal of Geophysical Research: Atmospheres
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In situ albedo measurement over sloped surfaces is pivotal to a wide range of remote sensing applications, such as the estimation and evaluation of surface energy budget at regional and global scales. However, existing albedo measurements over rugged terrain are limited and controversial and remain a major challenge. In this paper, two commonly measured broadband albedos, which depend on incoming/outgoing geometric conditions, were characterized over sloped surfaces and illustrated. These albedos are the horizontal/horizontal sloped surface albedo (HHSA) and inclined/inclined sloped surface albedo (IISA). 3‐D Discrete Anisotropic Radiative Transfer (DART) model simulations over varying slopes were utilized to quantify differences in the albedos. In particular, the effects of the slope, aspect, the solar zenith angle (SZA) and the proportion of diffuse skylight were investigated. The results show that absolute (relative) biases between HHSA and IISA are significant, reaching up to 0.026 (61.8%), 0.134 (62.4%), and 0.114 (62.3%) in the visible (VIS), near‐infrared (NIR), and shortwave (SW) broadbands, respectively. In addition, the diurnal cycle differences between HHSA and IISA were also compared using DART simulations and in situ observations over four typical slopes. Comparisons reveal that topographic parameters (e.g., slope and aspect) and atmospheric conditions (e.g., diffuse skylight and atmospheric visibility) are the primary factors, while the optical and structural parameters have a relatively smaller effect.

ACS Style

Shengbiao Wu; Jianguang Wen; DongQin You; Dalei Hao; Xingwen Lin; Qing Xiao; Qinhuo Liu; Jean-Philippe Gastellu-Etchegorry. Characterization of Remote Sensing Albedo Over Sloped Surfaces Based on DART Simulations and In Situ Observations. Journal of Geophysical Research: Atmospheres 2018, 123, 8599 -8622.

AMA Style

Shengbiao Wu, Jianguang Wen, DongQin You, Dalei Hao, Xingwen Lin, Qing Xiao, Qinhuo Liu, Jean-Philippe Gastellu-Etchegorry. Characterization of Remote Sensing Albedo Over Sloped Surfaces Based on DART Simulations and In Situ Observations. Journal of Geophysical Research: Atmospheres. 2018; 123 (16):8599-8622.

Chicago/Turabian Style

Shengbiao Wu; Jianguang Wen; DongQin You; Dalei Hao; Xingwen Lin; Qing Xiao; Qinhuo Liu; Jean-Philippe Gastellu-Etchegorry. 2018. "Characterization of Remote Sensing Albedo Over Sloped Surfaces Based on DART Simulations and In Situ Observations." Journal of Geophysical Research: Atmospheres 123, no. 16: 8599-8622.