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Spatiotemporally continuous long-term leaf area index (LAI) products are urgently needed to monitor environmental changes. The current filter- or curve-fitting-based time series reconstructive algorithms fail to reconstruct the LAI time series with many continuous missing values or missing values in key phenological periods, which are common issues in high-spatial-resolution LAI time series. In this article, a meteorological data-driven backpropagation neural network (MBPNN) was proposed to reconstruct discontinuous LAI profiles with a two-step process using vegetation phenological information. As the basis of the strong dependence of vegetation growth on meteorological conditions, a reasonable growth trajectory of reconstructed LAI can be guaranteed by the algorithm even though if many observed values are missing. Validations for reconstructed LAI were conducted both spatially and temporally based on reference maps and field-measured long-term observations. The results showed that the LAI predicted by the MBPNN had a similar accuracy (RMSE = 0.4076) as the Landsat LAI inversions (RMSE = 0.4083) and a similar reconstructed trajectory as the field-measured LAI series even though over 100 days of continuous data were missing (RMSE = 0.1620). A comparison with the Harmonic ANalysis of Time Series (HANTS) algorithm showed that the accuracy of MBPNN was more stable regardless of the size/position of the missing data, and the proposed method performed much better when the data were continuously missing for 50 days or more.
Xinran Zhu; Jing Li; Qinhuo Liu; Wentao Yu; Songze Li; Jing Zhao; Yadong Dong; Zhaoxing Zhang; Hu Zhang; Shangrong Lin. Use of a BP Neural Network and Meteorological Data for Generating Spatiotemporally Continuous LAI Time Series. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.
AMA StyleXinran Zhu, Jing Li, Qinhuo Liu, Wentao Yu, Songze Li, Jing Zhao, Yadong Dong, Zhaoxing Zhang, Hu Zhang, Shangrong Lin. Use of a BP Neural Network and Meteorological Data for Generating Spatiotemporally Continuous LAI Time Series. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.
Chicago/Turabian StyleXinran Zhu; Jing Li; Qinhuo Liu; Wentao Yu; Songze Li; Jing Zhao; Yadong Dong; Zhaoxing Zhang; Hu Zhang; Shangrong Lin. 2021. "Use of a BP Neural Network and Meteorological Data for Generating Spatiotemporally Continuous LAI Time Series." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.
Cloud detection is a crucial procedure in remote sensing preprocessing. However, cloud detection is challenging in cloud-snow coexisting areas because cloud and snow have a similar spectral characteristic in visible spectrum. To overcome this challenge, we presented an automatic cloud detection neural network (ACD net) integrated remote sensing imagery with geospatial data and aimed to improve the accuracy of cloud detection from high-resolution imagery under cloud-snow coexistence. The proposed ACD net consisted of two parts: 1) feature extraction networks and 2) cloud boundary refinement module. The feature extraction networks module was designed to extract the spectral-spatial and geographic semantic information of cloud from remote sensing imagery and geospatial data. The cloud boundary refinement module is used to further improve the accuracy of cloud detection. The results showed that the proposed ACD net can provide a reliably cloud detection result in cloud-snow coexistence scene. Compared with the state-of-the-art deep learning algorithms, the proposed ACD net yielded substantially higher overall accuracy of 97.92%. This letter provides a new approach to how remote sensing imagery and geospatial big data can be integrated to obtain high accuracy of cloud detection in the circumstance of cloud-snow coexistence.
Yang Chen; Qihao Weng; Luliang Tang; Qinhuo Liu; Rongshuang Fan. An Automatic Cloud Detection Neural Network for High-Resolution Remote Sensing Imagery With Cloud-Snow Coexistence. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleYang Chen, Qihao Weng, Luliang Tang, Qinhuo Liu, Rongshuang Fan. An Automatic Cloud Detection Neural Network for High-Resolution Remote Sensing Imagery With Cloud-Snow Coexistence. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleYang Chen; Qihao Weng; Luliang Tang; Qinhuo Liu; Rongshuang Fan. 2021. "An Automatic Cloud Detection Neural Network for High-Resolution Remote Sensing Imagery With Cloud-Snow Coexistence." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
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.
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 StyleXueting 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 StyleXueting 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.
The estimation of satellite-based albedo highly depends on the surface reflectance (SR). In mountainous areas, three types of SRs [i.e., the virtual SR (VSR) that is retrieved from the atmospheric correction model, the topographically corrected SR (TCSR) that is retrieved from the atmospheric and topographic correction model, and the sloping SR (SSR) that is retrieved from the physically bidirectional reflectance distribution function (BRDF)-based mountain-radiative-transfer (MRT) model] are commonly used to retrieve land surface albedo (SA). However, which type of SR is the best option for SA retrieval has not yet been quantitatively addressed. This letter assessed the performance of these three types of SRs on driving SA by comparison with in situ albedo measurements over field sites in the Heihe River Basin, China. Our results show that these three types of albedos have consistent accuracy over flat sites with a root mean squared error (RMSE) smaller than 0.0320. Moreover, the sloping SA (SSA) retrieved from SSR shows the best agreement with in situ albedo measurements over rugged sites with a bias of 0.0008, RMSE of 0.0338, relative RMSE (RMSER) of 12.92%, and correlation coefficient (r) of 0.89, followed by the topographically corrected SA (TCSA) from TCSR with a lager bias of 0.0208, RMSE of 0.0470, RMSER of 20.24%, and r of 0.69. The virtual SA (VSA) retrieved from VSR shows the largest uncertainty than the other two types of albedos, with an RMSE of 0.0516. These results illustrate that SSR is the best option of reflectance for satellite-based albedo retrieval over mountainous areas.
Xingwen Lin; Shengbiao Wu; Dalei Hao; Jianguang Wen; Qing Xiao; Qinhuo Liu. Sloping Surface Reflectance: The Best Option for Satellite-Based Albedo Retrieval Over Mountainous Areas. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleXingwen Lin, Shengbiao Wu, Dalei Hao, Jianguang Wen, Qing Xiao, Qinhuo Liu. Sloping Surface Reflectance: The Best Option for Satellite-Based Albedo Retrieval Over Mountainous Areas. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleXingwen Lin; Shengbiao Wu; Dalei Hao; Jianguang Wen; Qing Xiao; Qinhuo Liu. 2021. "Sloping Surface Reflectance: The Best Option for Satellite-Based Albedo Retrieval Over Mountainous Areas." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
Surface upward longwave radiation (SULR) is one of the four components of the surface radiation budget, which is defined as the total surface upward radiative flux in the spectral domain of 4-100 μm. The SULR is an indicator of surface thermal conditions and greatly impacts weather, climate, and phenology. Big Earth data derived from satellite remote sensing have been an important tool for studying earth science. The Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES-16) has greatly improved temporal and spectral resolution compared to the imager sensor of the previous GOES series and is a good data source for the generation of high spatiotemporal resolution SULR. In this study, based on the hybrid SULR estimation method and an upper hemisphere correction method for the SULR dataset, we developed a regional clear-sky land SULR dataset for GOES-16 with a half-hourly resolution for the period from 1st January 2018 to 30th June 2020. The dataset was validated against surface measurements collected at 65 Ameriflux radiation network sites. Compared with the SULR dataset of the Global LAnd Surface Satellite (GLASS) longwave radiation product that is generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the polar-orbiting Terra and Aqua satellites, the ABI/GOES-16 SULR dataset has commensurate accuracy (an RMSE of 15.9 W/m2 vs 19.02 W/m2 and an MBE of −4.4 W/m2 vs −2.57 W/m2), coarser spatial resolution (2 km at nadir vs 1 km resolution), less spatial coverage (most of the Americas vs global), fewer weather conditions (clear-sky vs all-weather conditions) and a greatly improved temporal resolution (48 vs 4 observations a day). The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00062.
Boxiong Qin; Biao Cao; Zunjian Bian; Ruibo Li; Hua Li; Xueting Ran; Yongming Du; Qing Xiao; Qinhuo Liu. Clear-sky land surface upward longwave radiation dataset derived from the ABI onboard the GOES–16 satellite. Big Earth Data 2021, 5, 161 -181.
AMA StyleBoxiong Qin, Biao Cao, Zunjian Bian, Ruibo Li, Hua Li, Xueting Ran, Yongming Du, Qing Xiao, Qinhuo Liu. Clear-sky land surface upward longwave radiation dataset derived from the ABI onboard the GOES–16 satellite. Big Earth Data. 2021; 5 (2):161-181.
Chicago/Turabian StyleBoxiong Qin; Biao Cao; Zunjian Bian; Ruibo Li; Hua Li; Xueting Ran; Yongming Du; Qing Xiao; Qinhuo Liu. 2021. "Clear-sky land surface upward longwave radiation dataset derived from the ABI onboard the GOES–16 satellite." Big Earth Data 5, no. 2: 161-181.
Vegetation index (VI) derived from remotely sensed images is a proxy of terrestrial vegetation information and widely used in land monitoring and global change studies. Recently, the prediction of vegetation properties has been an interest in related communities. With the accumulation of satellite records over the past few decades, the spatial-temporal prediction of VI becomes feasible. In this letter, we developed deep recurrent neural networks (RNNs) with long short-term memory (LSTM) and gated recurrent units (GRUs) to predict the short-term VI based on historical observations. The pixel-based fully connected networks GRU and LSTM (FCGRU and FCLSTM) and patch-based convolutional networks (ConvGRU and ConvLSTM) are established and compared with the traditional multilayer perceptron (MLP) model. Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 normalized difference VI (NDVI) data sets were used in the experiments. The prediction performance is evaluated globally in different regions, different vegetation types, and different growing seasons. Results demonstrate that the RNN models can predict VI with high accuracy (average root mean square error (RMSE) around 0.03), which is superior to the MLP model. In general, the pixel-based RNN models performed better than the patch-based models especially in regions with a larger proportion of outliers. And the prediction accuracy is stable over different vegetation types and growing seasons.
Wentao Yu; Jing Li; Qinhuo Liu; Jing Zhao; Yadong Dong; Cong Wang; Shangrong Lin; Xinran Zhu; Hu Zhang. Spatial-Temporal Prediction of Vegetation Index With Deep Recurrent Neural Networks. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleWentao Yu, Jing Li, Qinhuo Liu, Jing Zhao, Yadong Dong, Cong Wang, Shangrong Lin, Xinran Zhu, Hu Zhang. Spatial-Temporal Prediction of Vegetation Index With Deep Recurrent Neural Networks. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleWentao Yu; Jing Li; Qinhuo Liu; Jing Zhao; Yadong Dong; Cong Wang; Shangrong Lin; Xinran Zhu; Hu Zhang. 2021. "Spatial-Temporal Prediction of Vegetation Index With Deep Recurrent Neural Networks." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
Land surface component temperatures (LSCTs) are important parameters in many applications. However, the multi-angle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multi-pixel observations together. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard ESA's Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multi-pixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multi-angle algorithm. The introduction of multi-pixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multi-angle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors (RMSEs) of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products.
Zunjian Bian; Hua Li; Frank M. Gottsche; Ruibo Li; Yongming Du; Huazhong Ren; Biao Cao; Qing Xiao; Qinhuo Liu. Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 5536 -5549.
AMA StyleZunjian Bian, Hua Li, Frank M. Gottsche, Ruibo Li, Yongming Du, Huazhong Ren, Biao Cao, Qing Xiao, Qinhuo Liu. Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):5536-5549.
Chicago/Turabian StyleZunjian Bian; Hua Li; Frank M. Gottsche; Ruibo Li; Yongming Du; Huazhong Ren; Biao Cao; Qing Xiao; Qinhuo Liu. 2020. "Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 5536-5549.
In order to improve the simulation accuracy of directional brightness temperature (DBT) and the retrieval accuracy of component temperature, a model considering intra-row heterogeneity to simulate the DBT angular distribution over crop canopy is proposed. At individual scale, the probability of leaf appearance is inversely proportional to the distance from central stem. On the basis of this assumption, we formulated leaf area volume density (LAVD) spatial distribution at three hierarchical scales: individual scale, row scale, and scene scale. The equations for directional gap probability and bi-directional gap probability were modified to adapt the heterogeneity of row structure. Afterwards, a straightforward radiative transfer model was built based on the gap probabilities. A set of simulated data was generated by the thermal radiosity-graphics combined model (TRGM) as the benchmark to evaluate both forward simulation and inversion ability of the new model; we compared the new DBT model against an existing model assuming row as homogeneous box. With the growth of crops, the canopy structure of row crops will gradually change from row structure to continuous canopy. The new DBT model agreed with the TRGM model much better than the homogeneous row model at the middle stage of the crop growth season. The new model and the homogeneous row model achieve similar accuracy at early stage and end stage. At the middle growth stage, the new model can improve the accuracy of soil temperature retrieval. We recommend the new DBT model as an option to improve the DBT simulation and component temperature retrieval for row-planted crop canopy. In particular, the more accurate component temperatures during the middle growth stage are fundamentally important in characterizing crop water status, evapotranspiration, and soil moisture, which are subsequently critical for predicting crop productivity.
Yongming Du; Biao Cao; Hua Li; Zunjian Bian; Boxiong Qin; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. Modeling Directional Brightness Temperature (DBT) over Crop Canopy with Effects of Intra-Row Heterogeneity. Remote Sensing 2020, 12, 2667 .
AMA StyleYongming Du, Biao Cao, Hua Li, Zunjian Bian, Boxiong Qin, Qing Xiao, Qinhuo Liu, Yijian Zeng, Zhongbo Su. Modeling Directional Brightness Temperature (DBT) over Crop Canopy with Effects of Intra-Row Heterogeneity. Remote Sensing. 2020; 12 (17):2667.
Chicago/Turabian StyleYongming Du; Biao Cao; Hua Li; Zunjian Bian; Boxiong Qin; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. 2020. "Modeling Directional Brightness Temperature (DBT) over Crop Canopy with Effects of Intra-Row Heterogeneity." Remote Sensing 12, no. 17: 2667.
An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.
Ruibo Li; Hua Li; Lin Sun; Yikun Yang; Tian Hu; Zunjian Bian; Biao Cao; Yongming Du; Qinhuo Liu. An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data. Remote Sensing 2020, 12, 2613 .
AMA StyleRuibo Li, Hua Li, Lin Sun, Yikun Yang, Tian Hu, Zunjian Bian, Biao Cao, Yongming Du, Qinhuo Liu. An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data. Remote Sensing. 2020; 12 (16):2613.
Chicago/Turabian StyleRuibo Li; Hua Li; Lin Sun; Yikun Yang; Tian Hu; Zunjian Bian; Biao Cao; Yongming Du; Qinhuo Liu. 2020. "An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data." Remote Sensing 12, no. 16: 2613.
As a surface component, the tree trunk affects the top-of-canopy (TOC) emissivity and thermal infrared (TIR) radiance over a forest with fewer leaves, which is important for the inversion of land surface temperatures (LSTs) and further applications such as predicting forest fires and monitoring drought conditions. Therefore, the tree trunk effect was analyzed in this article using a thermal radiation directionality model, in which the forest structure was considered by the geometric optical (GO) theory and the spectral invariance theory was introduced into the GO framework for the single-scattering effect between components. The model used was evaluated using unmanned aerial vehicle (UAV)-based measurements with root-mean-square errors (RMSEs) lower than 0.25 °C for directional anisotropies (DAs) of brightness temperatures (BTs). Comparison with a 3-D radiative transfer model, discrete anisotropic radiative transfer (DART), also indicated an acceptable tool of the proposed model for the trunk effect with RMSEs lower than 0.003 °C and 1.2 °C for DAs of emissivity and BTs, respectively. In this study, the root-mean-squared difference (RMSD) levels between the vegetation-soil and vegetation-trunk-soil canopies, which were viewed as an equivalent indicator of the trunk effect, were provided for the TOC emissivity and BTs as well as their DAs, by combination with the changes in the leaf area index (LAI), stand density, trunk shape, and component temperatures, which can help identify the cases in which the trunk effect should be considered. According to a comprehensive analysis, for cases with sparse stand density (α <0.04), the tree trunk should be considered for a BT RMSD level lower than 0.5 °C when the LAI value was lower than 0.6. The corresponding LAI value was 0.8 for an RMSD level of BT DA lower than 0.3 °C. Moreover, for the cases with low soil emissivity, the difference in the TOC emissivity with and without trunk can reach up to 0.035, and the RMSD was still larger than 0.01 when the stand density and LAI were 0.05 and 0.6, respectively.
Zunjian Bian; Biao Cao; Hua Li; Yongming Du; Wenjie Fan; Qing Xiao; Qinhuo Liu. The Effects of Tree Trunks on the Directional Emissivity and Brightness Temperatures of a Leaf-Off Forest Using a Geometric Optical Model. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 5370 -5386.
AMA StyleZunjian Bian, Biao Cao, Hua Li, Yongming Du, Wenjie Fan, Qing Xiao, Qinhuo Liu. The Effects of Tree Trunks on the Directional Emissivity and Brightness Temperatures of a Leaf-Off Forest Using a Geometric Optical Model. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (6):5370-5386.
Chicago/Turabian StyleZunjian Bian; Biao Cao; Hua Li; Yongming Du; Wenjie Fan; Qing Xiao; Qinhuo Liu. 2020. "The Effects of Tree Trunks on the Directional Emissivity and Brightness Temperatures of a Leaf-Off Forest Using a Geometric Optical Model." IEEE Transactions on Geoscience and Remote Sensing 59, no. 6: 5370-5386.
Soil texture has been shown to affect the dielectric behavior of soil over the entire frequency range. Three universally employed dielectric semiempirical models (SEMs), the Dobson model, the Wang–Schmugge model and the Mironov model, as well as a new improved SEM known as the soil semi-empirical mineralogy-related-to-water dielectric model (SSMDM), incorporate a significant soil texture effect in different ways. In this paper, soil moisture estimate uncertainties from the effect of soil texture on these four SEMs are systematically and widely investigated over all soil texture cases at different frequencies between 1.4 and 18 GHz for volumetric water content levels between 0.0 and 0.4 m3/m3 from the perspective of two aspects: soil dielectric model discordance and soil texture discordance. Firstly, the effect of soil texture on these four dielectric SEMs is analyzed. Then, soil moisture estimate uncertainties due to the effect of soil texture are carefully investigated. Finally, the applicability of these SEMs is discussed, which can supply references for their choice. The results show that soil moisture estimate uncertainties are small and satisfy the 4% volumetric water content retrieval requirement in some cases. However, in other cases, it may contribute relatively significant uncertainties to soil moisture estimates and correspond to a difference that exceeds the 4% volumetric water content requirement, with potential for the largest deviations to exceed 0.22 m3/m3.
Jing Liu; Qinhuo Liu. Soil Moisture Estimate Uncertainties from the Effect of Soil Texture on Dielectric Semiempirical Models. Remote Sensing 2020, 12, 2343 .
AMA StyleJing Liu, Qinhuo Liu. Soil Moisture Estimate Uncertainties from the Effect of Soil Texture on Dielectric Semiempirical Models. Remote Sensing. 2020; 12 (14):2343.
Chicago/Turabian StyleJing Liu; Qinhuo Liu. 2020. "Soil Moisture Estimate Uncertainties from the Effect of Soil Texture on Dielectric Semiempirical Models." Remote Sensing 12, no. 14: 2343.
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.
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 StyleHua 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 StyleHua 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.
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.
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 StyleBoxiong 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 StyleBoxiong 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.
The environment project in the greater Mekong sub-region was the largest multi-field environmental cooperation launched by six countries (China, Vietnam, Laos, Myanmar, Thailand and Cambodia) in 2006, since the cooperation mechanism was established by Asian Development Bank (ADB) in 1992. How to establish the indicators to assess the achievements of the biological corridor construction and the status of ecological environment quantitatively is one of the prerequisites for the future project ongoing phase. The popular Pressure-State-Response (PSR) framework was employed in this study to assess the natural and human pressure, the healthy state of regional natural environment, and the subsequent response of ecosystem dynamic change in the Greater Mekong Subregion. Instead of using surveying based data as driving parameters, large amount of driving factors were retrieved from multi-source remote sensing data from 2000 to 2017, which provides access to larger updated and real-time databases, more tangible data allowing more objective goal management, and better spatially covered. The driving factors for pressure analysis included digital elevation, land surface temperature, evapotranspiration, light index, road network map, land cover dynamic change and land use degree, which were derived directly and indirectly from remote sensing. The indicators for state evaluation were composed of vegetation index, leaf area index, and fractional vegetation cover from remote sensing directly. The comprehensive response index was mainly determined by the pressure and state indicators. Through the analysis based on an overlay technique, it showed that the ecological environment deteriorated firstly from 2000 to 2010 and then started to improve from 2010 to 2017. The proofs indicated that the natural forest and wetland ecosystems were improved and the farmland area was decreased between 2000 and 2017. This study explored effective indicators from remote sensing for the ecological and environmental assessment, which can provide a strong decision-making basis for promoting the sustainable development of the ecological environment in the greater Mekong subregion, as well as the technological support for the construction of the biodiversity corridor.
Junjun Wu; Xin Wang; Bo Zhong; Aixia Yang; Kunsheng Jue; Jinhua Wu; Lan Zhang; Weijin Xu; Shanlong Wu; Nan Zhang; Qinhuo Liu. Ecological environment assessment for Greater Mekong Subregion based on Pressure-State-Response framework by remote sensing. Ecological Indicators 2020, 117, 106521 .
AMA StyleJunjun Wu, Xin Wang, Bo Zhong, Aixia Yang, Kunsheng Jue, Jinhua Wu, Lan Zhang, Weijin Xu, Shanlong Wu, Nan Zhang, Qinhuo Liu. Ecological environment assessment for Greater Mekong Subregion based on Pressure-State-Response framework by remote sensing. Ecological Indicators. 2020; 117 ():106521.
Chicago/Turabian StyleJunjun Wu; Xin Wang; Bo Zhong; Aixia Yang; Kunsheng Jue; Jinhua Wu; Lan Zhang; Weijin Xu; Shanlong Wu; Nan Zhang; Qinhuo Liu. 2020. "Ecological environment assessment for Greater Mekong Subregion based on Pressure-State-Response framework by remote sensing." Ecological Indicators 117, no. : 106521.
The angular and spectral kernel-driven (ASK) model distinguishes soil and vegetation spectral features by the component spectra and is a promising model which combines multisensor data for inversion. However, its global application is limited by the component spectra. This article proposes parameterization of the ASK component spectra of soil and leaf from global spectra libraries as ANGERS, GOSPEL, LOPEX, and USGS. A statistical ratio (ɣ ) of various leaf to soil spectra is used to capture their spectral differences and variations, with mean (m) +u (0, ± 0.5,,,±1) standard deviations (σ ) [i.e., ɣ (m+uσ )]. Optimization inversion is applied to determine the ratio candidates ɣ (m+uσ ), allowing more tolerance for spectral uncertainty, which releases the semiempirical nature of the kernel-driven model. Simulation data analysis proves its feasibility and good capture of vegetation-soil spectral differences. The model's bidirectional reflectance factor (BRF) fitting error [root-mean-square error (RMSE)] of 0.0245 is slightly larger than the true component spectra of 0.0178, and albedo RMSE is 0.0116 in Black Sky Albedo and 0.0182 in White Sky Albedo. The result also shows its good robustness to the noises, where the level up to 20% noise conducts a 0.0277 error in BRF fitting and an ignorable influence in albedo. The synergistic-retrieved albedo from multisensor satellite data consists of in situ measurements with an RMSE of 0.0171, compared to 0.0131 from true component spectra retrievals. The new parameterization sacrifices some accuracy, but it is simple and operational for global retrieval with a satisfactory precision.
DongQin You; Jianguang Wen; Qiang Liu; Yingtong Zhang; Yong Tang; Qinhuo Liu; Hongjie Xie. The Component-Spectra-Parameterized Angular and Spectral Kernel-Driven Model: A Potential Solution for Global BRDF/Albedo Retrieval From Multisensor Satellite Data. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 8674 -8688.
AMA StyleDongQin You, Jianguang Wen, Qiang Liu, Yingtong Zhang, Yong Tang, Qinhuo Liu, Hongjie Xie. The Component-Spectra-Parameterized Angular and Spectral Kernel-Driven Model: A Potential Solution for Global BRDF/Albedo Retrieval From Multisensor Satellite Data. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (12):8674-8688.
Chicago/Turabian StyleDongQin You; Jianguang Wen; Qiang Liu; Yingtong Zhang; Yong Tang; Qinhuo Liu; Hongjie Xie. 2020. "The Component-Spectra-Parameterized Angular and Spectral Kernel-Driven Model: A Potential Solution for Global BRDF/Albedo Retrieval From Multisensor Satellite Data." IEEE Transactions on Geoscience and Remote Sensing 58, no. 12: 8674-8688.
The objective of this article is a systematic investigation of the sensitivity of C- and X-band emissions to leaf shape and orientation for various growth stages of corn. To simulate these effects, we used the model developed at Tor Vergata University (TOV model), which is based on a matrix doubling algorithm considering multiple scattering. Corn leaves have specific properties of shape, curvature, and orientation. We have compared different approaches, including segmented elliptical disk oriented following leaf curvature, unique elliptical disk per leaf, and segmented circular disk with size determined by the shorter leaf dimension and following the leaf curvature. Moreover, widespread leaf inclination angle distribution functions combined with in ,,situ measurements of leaf inclination angle are adopted. The scatterers' phase matrix calculations are based on the physical optics approximation. Simulations are conducted with the ground-measured soil and vegetation properties as inputs and evaluated against the corresponding ground-based, multifrequency radiometer observations carried out in four different years over Chinese sites. The investigations show that in most cases the segmented circular disk assumption shows the best correspondence to the measurements over intermediate growth stages when the vegetation heights lie between 50 and 200 cm, and the unique elliptical disk model achieves the best correspondence for the later growth stages when the vegetation heights are larger than 200 cm with prefer-erectophile distribution of leaf orientation. The use of in ,,situ leaf inclination angle measurements can improve the model accuracy by up to 25 K for tall vegetation heights compared with random distribution assumption.
Jing Liu; Paolo Ferrazzoli; Leila Guerriero; Junhua Bai; Qinhuo Liu; Zhongjun Zhang. Modeling Microwave Emission of Corn Crop Considering Leaf Shape and Orientation Under the Physical Optics Approximation. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 8316 -8331.
AMA StyleJing Liu, Paolo Ferrazzoli, Leila Guerriero, Junhua Bai, Qinhuo Liu, Zhongjun Zhang. Modeling Microwave Emission of Corn Crop Considering Leaf Shape and Orientation Under the Physical Optics Approximation. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (12):8316-8331.
Chicago/Turabian StyleJing Liu; Paolo Ferrazzoli; Leila Guerriero; Junhua Bai; Qinhuo Liu; Zhongjun Zhang. 2020. "Modeling Microwave Emission of Corn Crop Considering Leaf Shape and Orientation Under the Physical Optics Approximation." IEEE Transactions on Geoscience and Remote Sensing 58, no. 12: 8316-8331.
Semi-empirical, kernel-driven linear BRDF models are widely used to characterize vegetation reflectance anisotropy and provide land surface BRF products at the regional and global scales. However, these models usually imply an assumption of spherical leaf inclination. The effects of such an ideal assumption on simulating surface BRF remain few quantified. In this paper, we first evaluated the effects of leaf inclination on the most commonly-used kernel-driven RossThick-LiSparse-Reciprocal (RTLSR) model by using the reflectance benchmark simulated by the mature PROSAIL (PROSAIL+SAIL) radiative transfer model. Subsequently, we improved the RTLSR model into a four-parameter version (RTLSRV4p) with a new volumetric scattering kernel derived from the assumption of vertical leaf inclination. Finally, the proposed RTLSRV4p model was validated by PROSAIL canopy BRF simulations, in situ canopy BRF measurements and Wide-angle Infrared Dual-mode line/area Array Scanner (WIDAS) airborne observations. Validation results demonstrate that RTLSRV4p improves vegetation reflectance characterization for large leaf inclinations compared to the original RTLSR model, especially for the near-infrared (NIR) spectral domain. When validated against the simulated canopy BRFs, the mean root-mean-square error (RMSE), mean absolute percentage error (MAPE), bias, and coefficient of determination (R2) were improved from 0.0810, 31.63%, 0.0651, 0.6578 to 0.0453, 13.38%, 0.0326, 0.8734. Using the in situ BRF measurement, the fitted RMSE, MAPE, bias, and R2 were improved from 0.0917, 14.31%, 0.0728, and 0.5776 to 0.0226, 3.35%, 0.0166, and 0.9744. These validation metrics were improved from 0.0423, 10.85%, 0.0347, and 0.6598 to 0.0258, 5.85%, 0.0181, and 0.8732 when compared against the WIDAS observation.
Shengbiao Wu; Jianguang Wen; Qinhuo Liu; DongQin You; Gaofei Yin; Xinwen Lin. Improving kernel-driven BRDF model for capturing vegetation canopy reflectance with large leaf inclinations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 1 -1.
AMA StyleShengbiao Wu, Jianguang Wen, Qinhuo Liu, DongQin You, Gaofei Yin, Xinwen Lin. Improving kernel-driven BRDF model for capturing vegetation canopy reflectance with large leaf inclinations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 ():1-1.
Chicago/Turabian StyleShengbiao Wu; Jianguang Wen; Qinhuo Liu; DongQin You; Gaofei Yin; Xinwen Lin. 2020. "Improving kernel-driven BRDF model for capturing vegetation canopy reflectance with large leaf inclinations." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. : 1-1.
When ground-leaving radiance from low-temperature surface is similar to downwelling radiance from the sky, the singular values arise in the retrieved land surface emissivity (LSE) at some specific spectral bands. In addition, too many singular values eventually cause temperature/emissivity separation algorithms to fail. To reduce the occurrence of these singular points, we formulate two indices, including the land-atmosphere radiance contrast index (LACI) and neighbor band contrast index (NBCI). LACI characterizes the contrast between surface radiance and sky downwelling radiance. NBCI characterizes the contrast between the radiance at neighboring bands. These two indices are used as filters to select bands which participate in the iterative spectrally smooth calculation. Thus, we modify the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm for low-temperature surfaces. Two methods have been used to evaluate the modified algorithm. First, numerical experiments are conducted to evaluate if the modified algorithm can accurately retrieve the ``true'' LSE from the simulated data. Second, an artificial low-temperature surface cooled by liquid nitrogen is measured to validate the modified algorithm. The results show that the modified algorithm can effectively avoid singular values, and behaves much better than the original algorithm with errors of less than 0.01 in retrieved emissivity when applied to low-temperature regions, while the modified algorithm brings limited improvement in retrieved temperature.
Yongming Du; Hua Li; Biao Cao; Zunjian Bian; Jianming Zhao; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. A Modified Interactive Spectral Smooth Temperature Emissivity Separation Algorithm for Low-Temperature Surface. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 7643 -7653.
AMA StyleYongming Du, Hua Li, Biao Cao, Zunjian Bian, Jianming Zhao, Qing Xiao, Qinhuo Liu, Yijian Zeng, Zhongbo Su. A Modified Interactive Spectral Smooth Temperature Emissivity Separation Algorithm for Low-Temperature Surface. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (11):7643-7653.
Chicago/Turabian StyleYongming Du; Hua Li; Biao Cao; Zunjian Bian; Jianming Zhao; Qing Xiao; Qinhuo Liu; Yijian Zeng; Zhongbo Su. 2020. "A Modified Interactive Spectral Smooth Temperature Emissivity Separation Algorithm for Low-Temperature Surface." IEEE Transactions on Geoscience and Remote Sensing 58, no. 11: 7643-7653.
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.
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 StyleXingwen 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 StyleXingwen 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.
GaoFen6 (GF-6), successfully launched on June 2, 2018, is the sixth satellite of the High-Definition Earth observation system (HDEOS). Although GF-6 is the first high-resolution satellite in China to achieve precise agricultural observation, it will be widely used in many other domains, such as land survey, natural resources management, emergency management, ecological environment and so on. The GF-6 was not equipped with an onboard calibration instrument, so on-orbit radiometric calibration is essential. This paper aimed at the on-orbit radiometric calibration of the wide field of view camera (WFV) onboard GF-6 (GF-6/WFV) in multispectral bands. Firstly, the radiometric capability of GF-6/WFV is evaluated compared with the Operational Land Imager (OLI) onboard Landsat-8, Multi Spectral Instrument (MSI) onboard Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra, which shows that GF-6/WFV has an obvious attenuation. Consequently, instead of vicarious calibration once a year, more frequent calibration is required to guarantee its radiometric consistency. The cross-calibration method based on the Badain Jaran Desert site using the bi-directional reflectance distribution function (BRDF) model calculated by Landsat-8/OLI and ZY-3/Three-Line Camera (TLC) data is subsequently applied to GF-6/WFV and much higher frequencies of calibration are achieved. Finally, the cross-calibration results are validated using the synchronized ground measurements at Dunhuang test site and the uncertainty of the proposed method is analyzed. The validation shows that the relative difference of cross-calibration is less than 5% and it is satisfied with the requirements of cross-calibration.
Aixia Yang; Bo Zhong; Longfei Hu; Shanlong Wu; Zhaopeng Xu; Hongbo Wu; Junjun Wu; Xueshuang Gong; Haibo Wang; Qinhuo Liu. Radiometric Cross-Calibration of the Wide Field View Camera Onboard GaoFen-6 in Multispectral Bands. Remote Sensing 2020, 12, 1037 .
AMA StyleAixia Yang, Bo Zhong, Longfei Hu, Shanlong Wu, Zhaopeng Xu, Hongbo Wu, Junjun Wu, Xueshuang Gong, Haibo Wang, Qinhuo Liu. Radiometric Cross-Calibration of the Wide Field View Camera Onboard GaoFen-6 in Multispectral Bands. Remote Sensing. 2020; 12 (6):1037.
Chicago/Turabian StyleAixia Yang; Bo Zhong; Longfei Hu; Shanlong Wu; Zhaopeng Xu; Hongbo Wu; Junjun Wu; Xueshuang Gong; Haibo Wang; Qinhuo Liu. 2020. "Radiometric Cross-Calibration of the Wide Field View Camera Onboard GaoFen-6 in Multispectral Bands." Remote Sensing 12, no. 6: 1037.