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Xihan Mu
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, 100875 Beijing, China

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Journal article
Published: 02 June 2021 in Journal of Geophysical Research: Atmospheres
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Terrain reflected solar radiation in snow-covered mountains is nonnegligible in investigations of the energy budget. However, it has so far not been investigated thoroughly, especially with regard to the influence of snow cover. Several parameterization approaches have been raised but not yet evaluated in a more uniform and quantitative manner. Based on the three-dimensional (3-D) ray-tracing simulation, we explored the temporal and spatial characteristics of the terrain reflected radiation in 15 domains on the southeastern Tibetan Plateau, and comprehensively evaluated different parameterization approaches. The results indicate that the ratio of reflected radiation to total daily radiation ranges from 0.25% to 10.85% at the scale of 5 × 5 km2 in a winter clear day, and it is 57% higher at noon in spring and autumn due to the higher snow cover fraction. Snow cover not only enhances the magnitude of reflected radiation by increasing surface albedo but also changes the spatial distribution pattern of radiation in partial snow-covered mountains, causing more snow-reflected radiation to be received by the surrounding surfaces. Three forms of terrain configuration factors in common parameterization approaches were evaluated by the ray-tracing model. The complementary of sky view factor shows good consistency with ray-tracing model at domain-averaged scales, with a root mean square error (RMSE) of 2.55 (14%) W/m2, while the other two both underestimate the radiation. The parameterization approach involving the multi-reflection shows better performance with the normalized RMSE decreasing by 5%. However, the uncertainty of it increases with the surface spatial heterogeneity caused by the partial snow cover, especially at high resolution.

ACS Style

Qing Chu; Guangjian Yan; Jianbo Qi; Xihan Mu; Linyuan Li; Yiyi Tong; Yingji Zhou; Yanan Liu; Donghui Xie; Martin Wild. Quantitative Analysis of Terrain Reflected Solar Radiation in Snow‐Covered Mountains: A Case Study in Southeastern Tibetan Plateau. Journal of Geophysical Research: Atmospheres 2021, 126, 1 .

AMA Style

Qing Chu, Guangjian Yan, Jianbo Qi, Xihan Mu, Linyuan Li, Yiyi Tong, Yingji Zhou, Yanan Liu, Donghui Xie, Martin Wild. Quantitative Analysis of Terrain Reflected Solar Radiation in Snow‐Covered Mountains: A Case Study in Southeastern Tibetan Plateau. Journal of Geophysical Research: Atmospheres. 2021; 126 (11):1.

Chicago/Turabian Style

Qing Chu; Guangjian Yan; Jianbo Qi; Xihan Mu; Linyuan Li; Yiyi Tong; Yingji Zhou; Yanan Liu; Donghui Xie; Martin Wild. 2021. "Quantitative Analysis of Terrain Reflected Solar Radiation in Snow‐Covered Mountains: A Case Study in Southeastern Tibetan Plateau." Journal of Geophysical Research: Atmospheres 126, no. 11: 1.

Journal article
Published: 29 May 2021 in ISPRS Journal of Photogrammetry and Remote Sensing
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The canopy bidirectional reflectance distribution function (BRDF) plays a pivotal role in estimating the biophysical parameters of plants, whereas soil background anisotropy creates challenges for their retrieval. Soil optical properties affect canopy anisotropic characteristics, especially in open-canopy areas. However, the remote sensing of background anisotropy is challenging due to the difficulties of information extraction in complex forest ecosystems and varying illumination conditions. This study develops an efficient photogrammetric technique to extract the background soil bidirectional reflectance factor (BRF) from unmanned aerial vehicle (UAV)-based multiangular images and to verify the need for accurate soil anisotropy information in canopy radiative transfer modeling. Soil BRF profiles were measured over three open-canopy sample plots from multiangular remotely sensed multispectral images collected with a hexacopter. As validation, reference soil BRF profiles were synchronously acquired by a ground-based multiangular imaging system. A high level of consistency between the ground- and UAV-measured soil BRF was observed with an RMSE of less than 0.012. Uncertainty analysis of the measured soil BRF showed that multiple scattering between sunlit soil in large sunflecks and foliage elements contributed less than 5%. Both results demonstrated that soil anisotropy can be accurately extracted from UAV multiangular measurements. To explicitly demonstrate that the use of soil anisotropy can reduce uncertainties in canopy radiative transfer simulations, we simulated the canopy BRF with Lambertian soil and with anisotropic soil using a three-dimensional (3D) radiative transfer model under different soil moisture content (SMC) levels, canopy cover (CC) levels and solar zenith angles (SZAs) with simulated realistic forest scenes. We found that less CC, lower SZAs and less SMC lead to a more significant influence of soil anisotropy on canopy reflectance; e.g., the reflectance bias reaches up to 0.3 in the hotspot direction. This illustrates that neglecting soil anisotropy can cause considerable errors in the modeling of the canopy BRF of open forests (i.e., CC levels of less than 0.5). The proposed technique facilitates the characterization of anisotropic forest background soil, which is important for advancing canopy radiative transfer modeling and validation and for the retrieval of vegetation parameters.

ACS Style

Linyuan Li; Xihan Mu; Jianbo Qi; Jan Pisek; Peter Roosjen; Guangjian Yan; Huaguo Huang; Shouyang Liu; Frédéric Baret. Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV-based multiangular images. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 177, 263 -278.

AMA Style

Linyuan Li, Xihan Mu, Jianbo Qi, Jan Pisek, Peter Roosjen, Guangjian Yan, Huaguo Huang, Shouyang Liu, Frédéric Baret. Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV-based multiangular images. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 177 ():263-278.

Chicago/Turabian Style

Linyuan Li; Xihan Mu; Jianbo Qi; Jan Pisek; Peter Roosjen; Guangjian Yan; Huaguo Huang; Shouyang Liu; Frédéric Baret. 2021. "Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV-based multiangular images." ISPRS Journal of Photogrammetry and Remote Sensing 177, no. : 263-278.

Journal article
Published: 28 April 2021 in Remote Sensing
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Analysis Ready Data (ARD) has been greatly recommended by the Committee on Earth Observation Satellites (CEOS) for simplifying and fostering long time series analysis at large scale with minimum additional user effort. Landsat ARD has been successfully made and widely used for large scale analysis. Subsequently, the Chinese satellite data similar to Landsat data have been processed and will be processed into ARDs to promote the use of the Chinese satellite data. At the first stage of the mission, the 4 Wide Field Viewing (WFV) data on GaoFen 1 (GF1) covering the whole of China and the surrounding areas have been processed into ARD. The ARD is provided as standard tiles under a common and unified projection with per pixel quality assurance and metadata for tracing back and further processing data, which are finally stored into a Hierarchical Data File (HDF); furthermore, all spectral bands are georegistered and radiometrically cross-calibrated as top of atmosphere (TOA) reflectance and are atmospherically corrected as surface reflectance (SR). Therefore, the ARD can be further used easily to produce land cover and land cover change maps and retrieve geophysical and biophysical parameters.

ACS Style

Bo Zhong; Aixia Yang; Qinhuo Liu; Shanlong Wu; Xiaojun Shan; Xihan Mu; Longfei Hu; Junjun Wu. Analysis Ready Data of the Chinese GaoFen Satellite Data. Remote Sensing 2021, 13, 1709 .

AMA Style

Bo Zhong, Aixia Yang, Qinhuo Liu, Shanlong Wu, Xiaojun Shan, Xihan Mu, Longfei Hu, Junjun Wu. Analysis Ready Data of the Chinese GaoFen Satellite Data. Remote Sensing. 2021; 13 (9):1709.

Chicago/Turabian Style

Bo Zhong; Aixia Yang; Qinhuo Liu; Shanlong Wu; Xiaojun Shan; Xihan Mu; Longfei Hu; Junjun Wu. 2021. "Analysis Ready Data of the Chinese GaoFen Satellite Data." Remote Sensing 13, no. 9: 1709.

Journal article
Published: 12 April 2021 in Journal of Remote Sensing
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Both leaf inclination angle distribution (LAD) and leaf area index (LAI) dominate optical remote sensing signals. The G-function, which is a function of LAD and remote sensing geometry, is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD. Large uncertainties are thus introduced. However, because numerous tiny leaves grow on conifers, it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval. In this study, we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval. Specifically, a Multi-Directional Imager (MDI) was developed to capture stereo images of the branches, and the needles were reconstructed. The accuracy of the inclination angles calculated from the reconstructed needles was high. Moreover, we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and three-dimensional (3D) tree models. Results show that the constant G assumption introduces large errors in LAI retrieval, which could be as large as 53% in the zenithal viewing direction used by spaceborne LiDAR. As a result, accurate LAD estimation is recommended. In the absence of such data, our results show that a viewing zenith angle between 45 and 65 degrees is a good choice, at which the errors of LAI retrieval caused by the spherical assumption will be less than 10% for coniferous canopies.

ACS Style

Guangjian Yan; Hailan Jiang; Jinghui Luo; Xihan Mu; Fan Li; Jianbo Qi; Ronghai Hu; Donghui Xie; Guoqing Zhou. Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies. Journal of Remote Sensing 2021, 2021, 1 -15.

AMA Style

Guangjian Yan, Hailan Jiang, Jinghui Luo, Xihan Mu, Fan Li, Jianbo Qi, Ronghai Hu, Donghui Xie, Guoqing Zhou. Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies. Journal of Remote Sensing. 2021; 2021 ():1-15.

Chicago/Turabian Style

Guangjian Yan; Hailan Jiang; Jinghui Luo; Xihan Mu; Fan Li; Jianbo Qi; Ronghai Hu; Donghui Xie; Guoqing Zhou. 2021. "Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies." Journal of Remote Sensing 2021, no. : 1-15.

Journal article
Published: 22 March 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Bidirectional reflectance distribution function (BRDF) models are used to correct surface bidirectional effects and estimate land surface albedo. Many operational BRDF/albedo algorithms adopt a Roujean linear kernel-driven BRDF (RLKB) model because of its simple form and good performance in fitting multidirectional surface reflectance values. However, this model does not explicitly consider topographic effects, resulting in errors when applied over rugged terrain. To address this issue, we proposed a hybrid algorithm suitable for both flat and rugged terrain, called topographical kernel-driven (Topo-KD). First, we constructed a linear kernel-driven BRDF model considering terrain (LKB_T) which describes the topographic effects with a mountain radiative transfer (MRT) model. Then, the Topo-KD algorithm adaptively selects the most suitable model (RLKB or LKB_T) according to the terrain conditions and fitting residuals. The performances of Topo-KD and RLKB using the RossThick-LiSparseReciprocal (RTLSR) kernel are compared using simulated data sets and moderate-resolution imaging spectroradiometer (MODIS) observations. The results show that the BRDF of the pixel is affected by topography. But the RTLSR model does not specifically account for it, resulting in larger biases over rugged terrain than the Topo-KD algorithm in both the red and near-infrared (NIR) bands. The experiment using MODIS data sets demonstrates that the Topo-KD algorithm reduces fitting residuals in the red and NIR bands by 21.5% and 27.4% compared with the RTLSR model. These results indicate that the Topo-KD algorithm can be a better choice for retrieving land surface parameters and describing the radiative transfer process in mountainous areas.

ACS Style

Kai Yan; Hanliang Li; Wanjuan Song; Yiyi Tong; Dalei Hao; Yelu Zeng; Xihan Mu; Guangjian Yan; Yuan Fang; Ranga B. Myneni; Crystal Schaaf. Extending a Linear Kernel-Driven BRDF Model to Realistically Simulate Reflectance Anisotropy Over Rugged Terrain. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Kai Yan, Hanliang Li, Wanjuan Song, Yiyi Tong, Dalei Hao, Yelu Zeng, Xihan Mu, Guangjian Yan, Yuan Fang, Ranga B. Myneni, Crystal Schaaf. Extending a Linear Kernel-Driven BRDF Model to Realistically Simulate Reflectance Anisotropy Over Rugged Terrain. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Kai Yan; Hanliang Li; Wanjuan Song; Yiyi Tong; Dalei Hao; Yelu Zeng; Xihan Mu; Guangjian Yan; Yuan Fang; Ranga B. Myneni; Crystal Schaaf. 2021. "Extending a Linear Kernel-Driven BRDF Model to Realistically Simulate Reflectance Anisotropy Over Rugged Terrain." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

Journal article
Published: 18 March 2021 in Remote Sensing
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Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study.

ACS Style

Hailan Jiang; Ronghai Hu; Guangjian Yan; Shiyu Cheng; Fan Li; Jianbo Qi; Linyuan Li; Donghui Xie; Xihan Mu. Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data. Remote Sensing 2021, 13, 1159 .

AMA Style

Hailan Jiang, Ronghai Hu, Guangjian Yan, Shiyu Cheng, Fan Li, Jianbo Qi, Linyuan Li, Donghui Xie, Xihan Mu. Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data. Remote Sensing. 2021; 13 (6):1159.

Chicago/Turabian Style

Hailan Jiang; Ronghai Hu; Guangjian Yan; Shiyu Cheng; Fan Li; Jianbo Qi; Linyuan Li; Donghui Xie; Xihan Mu. 2021. "Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data." Remote Sensing 13, no. 6: 1159.

Journal article
Published: 25 February 2021 in Journal of Meteorological Research
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High spatial resolution and high temporal frequency fractional vegetation cover (FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite (HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index (NDVI) was acquired by using the continuous correction (CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product (GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors (MEs) of forest, cropland, and grassland were −0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809, root-mean-square deviation (RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial-temporal consistency and similar magnitude (RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

ACS Style

Xihan Mu; Tian Zhao; Gaiyan Ruan; Jinling Song; Jindi Wang; Guangjian Yan; Tim R. Mcvicar; Kai Yan; Zhan Gao; Yaokai Liu; Yuanyuan Wang. High Spatial Resolution and High Temporal Frequency (30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets: Method Development and Validation. Journal of Meteorological Research 2021, 35, 128 -147.

AMA Style

Xihan Mu, Tian Zhao, Gaiyan Ruan, Jinling Song, Jindi Wang, Guangjian Yan, Tim R. Mcvicar, Kai Yan, Zhan Gao, Yaokai Liu, Yuanyuan Wang. High Spatial Resolution and High Temporal Frequency (30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets: Method Development and Validation. Journal of Meteorological Research. 2021; 35 (1):128-147.

Chicago/Turabian Style

Xihan Mu; Tian Zhao; Gaiyan Ruan; Jinling Song; Jindi Wang; Guangjian Yan; Tim R. Mcvicar; Kai Yan; Zhan Gao; Yaokai Liu; Yuanyuan Wang. 2021. "High Spatial Resolution and High Temporal Frequency (30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets: Method Development and Validation." Journal of Meteorological Research 35, no. 1: 128-147.

Journal article
Published: 20 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Remote sensing estimation based on the dimidiate pixel model (DPM) using vegetation indices (VIs) is a common approach for mapping fractional vegetation cover (FVC). The major drawback of DPM is that it does not consider real endmember conditions and multiple scattering between soil and vegetation. An analysis of FVC uncertainties caused by these model deficiencies is still lacking. Here, we first calculated the FVC theoretical uncertainty caused by reflectance uncertainties based on the law of prapagation of uncertainty (LPU). Then, we tested the performance of DPM using six VIs over 3-D forest scenes. We simulated both Aqua-MODIS and Landsat-OLI surface reflectance (SR) at their corresponding spatial resolutions and spectral response functions (SRFs) using a well-validated 3-D radiative transfer (RT) model which helps to separate the model and input uncertainties. We found that ratio vegetation index (RVI)- and enhanced vegetation index (EVI)-based models were most affected by sensors, followed by the normalized difference vegetation index (NDVI)-, enhanced vegetation index 2 (EVI2)-, renormalized difference vegetation index (RDVI)-, and difference vegetation index (DVI)-based models. Without considering SR uncertainties, the DVI-based model performed best (FVC absolute difference < 0.1); however, the commonly used NDVI model reached a maximum difference of 0.35. At the same time, input uncertainty increased the uncertainty of FVC retrieval. We noticed that the increase of solar zenith angle (SZA) resulted in a clear increase of retrieved FVC under the uniform distribution, which can be explained by the increased shadow proportion. Besides, model accuracy was dominated by the purity of soil (vegetation) endmember in low (high) vegetation cover area. This study provides a reference for the selection of the optimal VI for FVC retrieval based on the DPM.

ACS Style

Kai Yan; Si Gao; Haojing Chi; Jianbo Qi; Wanjuan Song; Yiyi Tong; Xihan Mu; Guangjian Yan. Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.

AMA Style

Kai Yan, Si Gao, Haojing Chi, Jianbo Qi, Wanjuan Song, Yiyi Tong, Xihan Mu, Guangjian Yan. Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.

Chicago/Turabian Style

Kai Yan; Si Gao; Haojing Chi; Jianbo Qi; Wanjuan Song; Yiyi Tong; Xihan Mu; Guangjian Yan. 2021. "Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 10 September 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Occlusion effect, an inherent problem of terrestrial laser scanning (TLS) measurements, limits the potential of TLS data in tree attribute estimation. Multiple scans seek to mitigate this effect to provide enhanced scan completeness. However, the numbers and locations of the scans (i.e., the scan design) are usually determined via a subjective assessment of the tree density, spatial patterns of trees, and attributes to be derived. These could cause suboptimal scan completeness and limit tree attribute estimation. This study proposed an iterative-mode scan design to minimize the occlusion effect. First, we introduced a PoTo index based on visibility analysis to evaluate how many trees can be scanned from a location and to select effective candidates for the optimal TLS location. Second, we introduced a cumulative degree of ring closure (CDRC) to quantify the scan completeness for each candidate and determine the optimal TLS location. The TLS data sets of virtual forests with field-measured and synthetic plot parameter settings were simulated according to iterative- and regular-mode designs by using a Heidelberg light detection and ranging (LiDAR) Operations Simulator (HELIOS). The results demonstrated that an iterative-mode design can improve the scan completeness of trees compared to the regular-mode design. The tree attribute (diameter at breast height (DBH), tree height, stem curve, and crown volume) estimates of the iterative-mode design were less erroneous than those of the regular-mode design (e.g., the root-mean-square error (RMSE) could decrease the stem curve estimation by 38% and the crown volume estimation by 15%). This study suggests that the iterative-mode design can obtain an improved quality of the TLS data, especially for dense stands.

ACS Style

Linyuan Li; Xihan Mu; Maxime Soma; Peng Wan; Jianbo Qi; Ronghai Hu; Wuming Zhang; Yiyi Tong; Guangjian Yan. An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 3547 -3566.

AMA Style

Linyuan Li, Xihan Mu, Maxime Soma, Peng Wan, Jianbo Qi, Ronghai Hu, Wuming Zhang, Yiyi Tong, Guangjian Yan. An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (4):3547-3566.

Chicago/Turabian Style

Linyuan Li; Xihan Mu; Maxime Soma; Peng Wan; Jianbo Qi; Ronghai Hu; Wuming Zhang; Yiyi Tong; Guangjian Yan. 2020. "An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects." IEEE Transactions on Geoscience and Remote Sensing 59, no. 4: 3547-3566.

Journal article
Published: 18 June 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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As a primary nighttime light (NTL) data source, the day/night band (DNB) sensor of the visible infrared imaging radiometer suite (VIIRS) is used in a wide range of studies. However, this signal is influenced by the atmosphere and may cause uncertainty while monitoring ground NTL. Given the lack of the quantitative analysis of the atmospheric effect on NTL, this study analyzes the relationship between VIIRS DNB NTL radiance and aerosol optical depth (AOD) in the urban areas of Beijing and three other Chinese cities of different urbanization levels. Results suggest a significantly negative relationship between the NTL radiance and AOD. The linear and log-linear models generate similar coefficients of determination ( R 2 ) for the AOD and NTL data, which vary for different urban centers. In Beijing, where the aerosol robotic network observations are available, R 2 reached 0.655 between the monthly NTL radiance and AOD. A slight decrease of R 2 occurred while using the Himawari AOD. The relationship between the NTL radiance and AOD varies among the cities. The NTL radiance may decrease by approximately 10 nW.cm −2 sr −1 when daily AOD increases one unit. For Beijing, this decrease may be above 15 nW.cm −2 sr −1 , which is comparable with the threshold used to extract urban areas. These findings underscore the importance of AOD in the application of NTL data that are potentially useful in the reconstruction of stable time series VIIRS DNB images by removing the aerosol effects.

ACS Style

Xuejun Wang; Xihan Mu; Guangjian Yan. Quantitative Analysis of Aerosol Influence on Suomi-NPP VIIRS Nighttime Light in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 3557 -3568.

AMA Style

Xuejun Wang, Xihan Mu, Guangjian Yan. Quantitative Analysis of Aerosol Influence on Suomi-NPP VIIRS Nighttime Light in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 ():3557-3568.

Chicago/Turabian Style

Xuejun Wang; Xihan Mu; Guangjian Yan. 2020. "Quantitative Analysis of Aerosol Influence on Suomi-NPP VIIRS Nighttime Light in China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. : 3557-3568.

Journal article
Published: 27 May 2020 in IEEE Transactions on Geoscience and Remote Sensing
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ACS Style

Guangjian Yan; Qing Chu; Yiyi Tong; Xihan Mu; Jianbo Qi; Yingji Zhou; Yanan Liu; Tianxing Wang; Donghui Xie; Wuming Zhang; Kai Yan; Shengbo Chen; Hongmin Zhou. An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains. IEEE Transactions on Geoscience and Remote Sensing 2020, 1 -18.

AMA Style

Guangjian Yan, Qing Chu, Yiyi Tong, Xihan Mu, Jianbo Qi, Yingji Zhou, Yanan Liu, Tianxing Wang, Donghui Xie, Wuming Zhang, Kai Yan, Shengbo Chen, Hongmin Zhou. An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains. IEEE Transactions on Geoscience and Remote Sensing. 2020; ():1-18.

Chicago/Turabian Style

Guangjian Yan; Qing Chu; Yiyi Tong; Xihan Mu; Jianbo Qi; Yingji Zhou; Yanan Liu; Tianxing Wang; Donghui Xie; Wuming Zhang; Kai Yan; Shengbo Chen; Hongmin Zhou. 2020. "An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains." IEEE Transactions on Geoscience and Remote Sensing , no. : 1-18.

Journal article
Published: 01 February 2020 in Remote Sensing of Environment
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ACS Style

Guangjian Yan; Zhong-Hu Jiao; Tianxing Wang; Xihan Mu. Modeling surface longwave radiation over high-relief terrain. Remote Sensing of Environment 2020, 237, 1 .

AMA Style

Guangjian Yan, Zhong-Hu Jiao, Tianxing Wang, Xihan Mu. Modeling surface longwave radiation over high-relief terrain. Remote Sensing of Environment. 2020; 237 ():1.

Chicago/Turabian Style

Guangjian Yan; Zhong-Hu Jiao; Tianxing Wang; Xihan Mu. 2020. "Modeling surface longwave radiation over high-relief terrain." Remote Sensing of Environment 237, no. : 1.

Journal article
Published: 16 January 2020 in Remote Sensing
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Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red–green–blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling.

ACS Style

Linyuan Li; Jun Chen; Xihan Mu; Weihua Li; Guangjian Yan; Donghui Xie; Wuming Zhang. Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation. Remote Sensing 2020, 12, 298 .

AMA Style

Linyuan Li, Jun Chen, Xihan Mu, Weihua Li, Guangjian Yan, Donghui Xie, Wuming Zhang. Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation. Remote Sensing. 2020; 12 (2):298.

Chicago/Turabian Style

Linyuan Li; Jun Chen; Xihan Mu; Weihua Li; Guangjian Yan; Donghui Xie; Wuming Zhang. 2020. "Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation." Remote Sensing 12, no. 2: 298.

Review
Published: 10 December 2019 in ISPRS Journal of Photogrammetry and Remote Sensing
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Green fractional vegetation cover (fc) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of fc via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a comprehensive and systematic review of RA algorithms considering these factors based on the scientific papers published from January 1990 to November 2019. This review revealed that the key issues related to RA algorithms is the determination of the appropriate normalized difference vegetation index (NDVI) values of the full vegetation cover and bare soil (denoted hereafter by NDVI∞ and NDVIs, respectively). The existing methods used to correct for these issues were investigated, and their advantages and disadvantages are discussed in depth. In literature trends, we found that the number of reported studies in which RA algorithms were used has increased consistently over time, and that most authors tend to utilize the linear NDVI model, rather than other models in the RA algorithm family. We also found that RA algorithms have been utilized to analyze the images with spatial resolutions ranging from the sub-meter to kilometer, most commonly, using images of 30-m spatial resolution. Finally, current challenges and forward-looking insights in remote estimation of fc using RA algorithms are discussed to guide future research and directions.

ACS Style

Lin Gao; Xiaofei Wang; Brian Alan Johnson; Qingjiu Tian; Yu Wang; Jochem Verrelst; Xihan Mu; Xingfa Gu. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS Journal of Photogrammetry and Remote Sensing 2019, 159, 364 -377.

AMA Style

Lin Gao, Xiaofei Wang, Brian Alan Johnson, Qingjiu Tian, Yu Wang, Jochem Verrelst, Xihan Mu, Xingfa Gu. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 159 ():364-377.

Chicago/Turabian Style

Lin Gao; Xiaofei Wang; Brian Alan Johnson; Qingjiu Tian; Yu Wang; Jochem Verrelst; Xihan Mu; Xingfa Gu. 2019. "Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review." ISPRS Journal of Photogrammetry and Remote Sensing 159, no. : 364-377.

Journal article
Published: 01 October 2019 in ISPRS Journal of Photogrammetry and Remote Sensing
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Remote sensing via unmanned aerial vehicles (UAVs) is becoming a very important tool for augmenting traditional spaceborne and airborne remote sensing techniques. Commercial RGB cameras are often the payload on UAVs, because they are inexpensive, easy to operate and require little data processing. RGB images are increasingly being used for mapping of fractional vegetation cover (FVC). However, the presence of significantly mixed pixels in close-range RGB images prevents the accurate estimation of FVC. Even where pixel unmixing is applied, limited quantitative spectral information and colour variability within these images could lead to profound errors and uncertainties. This paper proposes a colour mixture analysis (CMA) method based on the Hue-Saturation-Value (HSV) colour space to alleviate the above-mentioned concerns, thereby improving the accuracy and efficiency of FVC estimation from UAV-captured RGB images. First, the a priori colour information of the pure vegetation and background endmembers are extracted from the Hue channel of the UAV proximal sensing images, obviating ground-based image capture and the attendant cost and inconvenience. Second, the relationship between the probability distribution of mixed pixels and that of the two endmembers is estimated. Finally, we estimate FVC from UAV remote sensing images with a maximum a posteriori parameter (MAP) estimator. Two UAV-captured RGB image datasets and a synthetic RGB image dataset were used to test the new method. CMA was compared with three other FVC estimation algorithms, namely, FCLS, HAGFVC and LAB2. The FVC estimates by CMA were found to be highly accurate, with root mean squared errors (RMSE) of less than 0.007 and mean absolute error (MAE) of less than 0.01 for both field datasets. The accuracy was shown to be superior to that of all three algorithms. A comprehensive analysis of the estimation accuracy under various spatial resolutions and vegetation cover levels was conducted using both field and synthetic datasets. Results show that the CMA method can robustly and accurately estimate FVC across the full range of vegetation coverage and various resolutions. Uncertainty and sensitivity analysis of colour variability due to heterogeneity and shadow were also tested. Overall, CMA was shown to be robust to variation in colour and illumination.

ACS Style

Guangjian Yan; Linyuan Li; André Coy; Xihan Mu; Shengbo Chen; Donghui Xie; Wuming Zhang; Qingfeng Shen; Hongmin Zhou. Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing. ISPRS Journal of Photogrammetry and Remote Sensing 2019, 158, 23 -34.

AMA Style

Guangjian Yan, Linyuan Li, André Coy, Xihan Mu, Shengbo Chen, Donghui Xie, Wuming Zhang, Qingfeng Shen, Hongmin Zhou. Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing. ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 158 ():23-34.

Chicago/Turabian Style

Guangjian Yan; Linyuan Li; André Coy; Xihan Mu; Shengbo Chen; Donghui Xie; Wuming Zhang; Qingfeng Shen; Hongmin Zhou. 2019. "Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing." ISPRS Journal of Photogrammetry and Remote Sensing 158, no. : 23-34.

Journal article
Published: 06 March 2019 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Rugged terrain, as a large percentage of the Earth's terrestrial surface, is frequently reported to cause directionality of land surface thermal radiation (LSTR), and seriously affects the retrieval accuracy of land surface temperature (LST) and surface longwave radiation from satellite measurements. Therefore, modeling topographic effects on surface thermal anisotropy is essential to understand surface radiative processes. The directional brightness temperature (DBT) and equivalent brightness temperature (EBT) models at the pixel scale are proposed to indicate thermal anisotropy, considering viewing geometry, topographic effects, and subpixel variations based on the thermal infrared radiative transfer equation. A simulated data set of DBT and EBT at the 1-km resolution was obtained based on LST, emissivity, and terrain data with 30-m resolution. The terrain, coupled with solar and viewing geometries and subgrid variation, significantly affects the directionality of LSTR, and results in a remarkable bias between DBT and EBT. For the nadir observation, the bias is from −0.8 to 1 K, and reaches −5 to 2 K when viewing zenith angle becomes 50°. The maximal deviation is about 9 K over the most rugged mountains, which causes ${\text{57.6}}\;{\text{W/m}}^{2}$ bias of longwave radiation based on a 300 K blackbody. Furthermore, when LST is retrieved from DBT, the uncertainty of broadband emissivity of 0.01 causes LST bias of ∼0.35 K. The models are considered to be very helpful in exploring terrain-induced thermal anisotropy, and enlightening in reducing estimation bias of remote sensing products over complex terrain.

ACS Style

Zhong-Hu Jiao; Guangjian Yan; Tianxing Wang; Xihan Mu; Jing Zhao. Modeling of Land Surface Thermal Anisotropy Based on Directional and Equivalent Brightness Temperatures Over Complex Terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 410 -423.

AMA Style

Zhong-Hu Jiao, Guangjian Yan, Tianxing Wang, Xihan Mu, Jing Zhao. Modeling of Land Surface Thermal Anisotropy Based on Directional and Equivalent Brightness Temperatures Over Complex Terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019; 12 (2):410-423.

Chicago/Turabian Style

Zhong-Hu Jiao; Guangjian Yan; Tianxing Wang; Xihan Mu; Jing Zhao. 2019. "Modeling of Land Surface Thermal Anisotropy Based on Directional and Equivalent Brightness Temperatures Over Complex Terrain." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 2: 410-423.

Journal article
Published: 20 February 2019 in IEEE Geoscience and Remote Sensing Letters
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ACS Style

Jianbo Qi; Donghui Xie; Linyuan Li; Wuming Zhang; Xihan Mu; Guangjian Yan. Estimating Leaf Angle Distribution From Smartphone Photographs. IEEE Geoscience and Remote Sensing Letters 2019, 16, 1190 -1194.

AMA Style

Jianbo Qi, Donghui Xie, Linyuan Li, Wuming Zhang, Xihan Mu, Guangjian Yan. Estimating Leaf Angle Distribution From Smartphone Photographs. IEEE Geoscience and Remote Sensing Letters. 2019; 16 (8):1190-1194.

Chicago/Turabian Style

Jianbo Qi; Donghui Xie; Linyuan Li; Wuming Zhang; Xihan Mu; Guangjian Yan. 2019. "Estimating Leaf Angle Distribution From Smartphone Photographs." IEEE Geoscience and Remote Sensing Letters 16, no. 8: 1190-1194.

Journal article
Published: 20 February 2019 in Earth and Space Science
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The sky view factor (SVF) is a crucial variable widely used to quantify the characteristics of surface structures and estimate surface radiation budget. Many SVF models based on raster data have been developed but not yet evaluated in a more quantitative and uniform manner. In this paper, four typical SVF models (Dozier‐Frew (D‐F), Manners, Lindberg‐Grimmond (L‐G), and Helbig_h) are evaluated using the SVF derived from simulated fisheye images based on the digital surface model (DSM) and digital elevation model (DEM) data. The SVF calculated by D‐F method using DSM data has the best accuracy, with a mean bias error (MBE) of –0.007, root‐mean‐square error (RMSE) of 0.069, and coefficient of determination (R2) of 0.914. For the SVF value derived from DEM data, L‐G method shows good performance, with an MBE of 0.013, RMSE of 0.032, and R2 of 0.897. The pixels near the edges of buildings, within the valley or along ridgelines have higher SVF deviations. In addition, the slope angle calculated using DSM data has some artificial defects that make the significant impact on the SVF biases due to their calculation method and the discontinuous surface in urban areas. Thus, L‐G and Helbig_h methods are more applicable for the DSM data due to the difficulty in defining slope and aspect angles. Moreover, the high accordance of SVFs between Helbig_h and L‐G methods implys that the Helbig_h method is an alternative in virtue of its simpler form and lower computation cost than L‐G method.

ACS Style

Zhong-Hu Jiao; Huazhong Ren; Xihan Mu; Jing Zhao; Tianxing Wang; Jiaji Dong. Evaluation of Four Sky View Factor Algorithms Using Digital Surface and Elevation Model Data. Earth and Space Science 2019, 6, 222 -237.

AMA Style

Zhong-Hu Jiao, Huazhong Ren, Xihan Mu, Jing Zhao, Tianxing Wang, Jiaji Dong. Evaluation of Four Sky View Factor Algorithms Using Digital Surface and Elevation Model Data. Earth and Space Science. 2019; 6 (2):222-237.

Chicago/Turabian Style

Zhong-Hu Jiao; Huazhong Ren; Xihan Mu; Jing Zhao; Tianxing Wang; Jiaji Dong. 2019. "Evaluation of Four Sky View Factor Algorithms Using Digital Surface and Elevation Model Data." Earth and Space Science 6, no. 2: 222-237.

Journal article
Published: 17 December 2018 in Remote Sensing of Environment
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Three-dimensional (3D) radiative transfer modeling of the transport and interaction of radiation through earth surfaces is challenging due to the complexity of the landscapes as well as the intensive computational cost of 3D radiative transfer simulations. To reduce computation time, current models work with schematic landscapes or with small-scale realistic scenes. The computer graphics community provides the most accurate and efficient models (known as renderers) but they were not designed specifically for performing scientific radiative transfer simulations. In this study, we propose LESS, a new 3D radiative transfer modeling framework. LESS employs a weighted forward photon tracing method to simulate multispectral bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km2) spectral images. The backward path tracing also has been extended to simulate thermal infrared radiation by using an on-the-fly computation of the sunlit and shaded scene components. This framework is achieved through the development of a user-friendly graphic user interface (GUI) and a set of tools to help construct the landscape and set parameters. The accuracy of LESS is evaluated with other models as well as field measurements in terms of directional BRFs and pixel-wise simulated image comparisons, which shows very good agreement. LESS has the potential in simulating datasets of realistically reconstructed landscapes. Such simulated datasets can be used as benchmarks for various applications in remote sensing, forestry investigation and photogrammetry.

ACS Style

Jianbo Qi; Donghui Xie; Tiangang Yin; Guangjian Yan; Jean Philippe Gastellu-Etchegorry; Linyuan Li; Wuming Zhang; Xihan Mu; Leslie K. Norford. LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment 2018, 221, 695 -706.

AMA Style

Jianbo Qi, Donghui Xie, Tiangang Yin, Guangjian Yan, Jean Philippe Gastellu-Etchegorry, Linyuan Li, Wuming Zhang, Xihan Mu, Leslie K. Norford. LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment. 2018; 221 ():695-706.

Chicago/Turabian Style

Jianbo Qi; Donghui Xie; Tiangang Yin; Guangjian Yan; Jean Philippe Gastellu-Etchegorry; Linyuan Li; Wuming Zhang; Xihan Mu; Leslie K. Norford. 2018. "LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes." Remote Sensing of Environment 221, no. : 695-706.

Review
Published: 06 December 2018 in Agricultural and Forest Meteorology
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Leaf area index (LAI) is a key parameter of vegetation structure in the fields of agriculture, forestry, and ecology. Optical indirect methods based on the Beer-Lambert law are widely adopted in numerous fields given their high efficiency and feasibility for LAI estimation. These methods have undergone considerable progress in the past decades, thereby making them operational in ground-based LAI measurement and even in airborne estimation. However, several challenges remain, given the requirement of increasing accuracy and new applications. Clumping effect correction attained significant progress for continuous canopies with non-randomly disturbed leaves while non-continuous canopies are rarely studied. Convenient and operational measurement of leaf angle distribution and woody components is lacked. Accurate and comprehensive validations are still very difficult due to the limitations of direct measurement. The introduction of active laser scanning technology is a driving force for addressing several challenges, but its three-dimensional information has not been fully explored and utilized. In order to update the general knowledge and identify the possible error source, this study comprehensively reviews the temporal development, theoretical framework, and issues of indirect LAI measurement, followed by current methods, instruments, and platforms. Latest methods and instruments are introduced and compared to traditional ones. Current challenges, recent advances, and future perspectives are discussed to provide recommendations for further research.

ACS Style

Guangjian Yan; Ronghai Hu; Jinghui Luo; Marie Weiss; Hailan Jiang; Xihan Mu; Donghui Xie; Wuming Zhang. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology 2018, 265, 390 -411.

AMA Style

Guangjian Yan, Ronghai Hu, Jinghui Luo, Marie Weiss, Hailan Jiang, Xihan Mu, Donghui Xie, Wuming Zhang. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology. 2018; 265 ():390-411.

Chicago/Turabian Style

Guangjian Yan; Ronghai Hu; Jinghui Luo; Marie Weiss; Hailan Jiang; Xihan Mu; Donghui Xie; Wuming Zhang. 2018. "Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives." Agricultural and Forest Meteorology 265, no. : 390-411.