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Wanjuan Song
Aerospace Information Research Institute, China Academy of Sciences, Beijing 100083, China

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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: 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.

Research article
Published: 07 October 2020 in Science Advances
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Soil respiration (Rs) represents the largest flux of CO2 from terrestrial ecosystems to the atmosphere, but its spatial and temporal changes as well as the driving forces are not well understood. We derived a product of annual global Rs from 2000 to 2014 at 1 km by 1 km spatial resolution using remote sensing data and biome-specific statistical models. Different from the existing view that climate change dominated changes in Rs, we showed that land-cover change played a more important role in regulating Rs changes in temperate and boreal regions during 2000–2014. Significant changes in Rs occurred more frequently in areas with significant changes in short vegetation cover (i.e., all vegetation shorter than 5 m in height) than in areas with significant climate change. These results contribute to our understanding of global Rs patterns and highlight the importance of land-cover change in driving global and regional Rs changes.

ACS Style

Ni Huang; Lei Wang; Xiao-Peng Song; T. Andrew Black; Rachhpal S. Jassal; Ranga B. Myneni; Chaoyang Wu; Wanjuan Song; Dabin Ji; Shanshan Yu; Zheng Niu. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Science Advances 2020, 6, eabb8508 .

AMA Style

Ni Huang, Lei Wang, Xiao-Peng Song, T. Andrew Black, Rachhpal S. Jassal, Ranga B. Myneni, Chaoyang Wu, Wanjuan Song, Dabin Ji, Shanshan Yu, Zheng Niu. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Science Advances. 2020; 6 (41):eabb8508.

Chicago/Turabian Style

Ni Huang; Lei Wang; Xiao-Peng Song; T. Andrew Black; Rachhpal S. Jassal; Ranga B. Myneni; Chaoyang Wu; Wanjuan Song; Dabin Ji; Shanshan Yu; Zheng Niu. 2020. "Spatial and temporal variations in global soil respiration and their relationships with climate and land cover." Science Advances 6, no. 41: eabb8508.

Journal article
Published: 05 October 2018 in Remote Sensing
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Earth’s reflectivity is among the key parameters of climate research. National Aeronautics and Space Administration (NASA)’s Earth Polychromatic Imaging Camera (EPIC) onboard National Oceanic and Atmospheric Administration (NOAA)’s Deep Space Climate Observatory (DSCOVR) spacecraft provides spectral reflectance of the entire sunlit Earth in the near backscattering direction every 65 to 110 min. Unlike EPIC, sensors onboard the Earth Orbiting Satellites (EOS) sample reflectance over swaths at a specific local solar time (LST) or over a fixed area. Such intrinsic sampling limits result in an apparent Earth’s reflectivity. We generated spectral reflectance over sampling areas using EPIC data. The difference between the EPIC and EOS estimates is an uncertainty in Earth’s reflectivity. We developed an Earth Reflector Type Index (ERTI) to discriminate between major Earth atmosphere components: clouds, cloud-free ocean, bare and vegetated land. Temporal variations in Earth’s reflectivity are mostly determined by clouds. The sampling area of EOS sensors may not be sufficient to represent cloud variability, resulting in biased estimates. Taking EPIC reflectivity as a reference, low-earth-orbiting-measurements at the sensor-specific LST tend to overestimate EPIC values by 0.8%to 8%. Biases in geostationary orbiting approximations due to a limited sampling area are between -0.7% and 12%. Analyses of ERTI-based Earth component reflectivity indicate that the disagreement between EPIC and EOS estimates depends on the sampling area, observation time and vary between -10% and 23%.

ACS Style

Wanjuan Song; Yuri Knyazikhin; Guoyong Wen; Alexander Marshak; Matti Mõttus; Kai Yan; Bin Yang; Baodong Xu; Taejin Park; Chi Chen; Yelu Zeng; Guangjian Yan; Xihan Mu; Ranga B. Myneni. Implications of Whole-Disc DSCOVR EPIC Spectral Observations for Estimating Earth’s Spectral Reflectivity Based on Low-Earth-Orbiting and Geostationary Observations. Remote Sensing 2018, 10, 1594 .

AMA Style

Wanjuan Song, Yuri Knyazikhin, Guoyong Wen, Alexander Marshak, Matti Mõttus, Kai Yan, Bin Yang, Baodong Xu, Taejin Park, Chi Chen, Yelu Zeng, Guangjian Yan, Xihan Mu, Ranga B. Myneni. Implications of Whole-Disc DSCOVR EPIC Spectral Observations for Estimating Earth’s Spectral Reflectivity Based on Low-Earth-Orbiting and Geostationary Observations. Remote Sensing. 2018; 10 (10):1594.

Chicago/Turabian Style

Wanjuan Song; Yuri Knyazikhin; Guoyong Wen; Alexander Marshak; Matti Mõttus; Kai Yan; Bin Yang; Baodong Xu; Taejin Park; Chi Chen; Yelu Zeng; Guangjian Yan; Xihan Mu; Ranga B. Myneni. 2018. "Implications of Whole-Disc DSCOVR EPIC Spectral Observations for Estimating Earth’s Spectral Reflectivity Based on Low-Earth-Orbiting and Geostationary Observations." Remote Sensing 10, no. 10: 1594.

Journal article
Published: 20 September 2018 in Remote Sensing
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This paper presents a simple radiative transfer model based on spectral invariant properties (SIP). The canopy structure parameters, including the leaf angle distribution and multi-angular clumping index, are explicitly described in the SIP model. The SIP model has been evaluated on its bidirectional reflectance factor (BRF) in the angular space at the radiation transfer model intercomparison platform, and in the spectrum space by the PROSPECT+SAIL (PROSAIL) model. The simulations of BRF by SIP agreed well with the reference values in both the angular space and spectrum space, with a root-mean-square-error (RMSE) of 0.006. When compared with the widely-used Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model on fPAR, the RMSE was 0.006 and the R2 was 0.99, which shows a high accuracy. This study also suggests the newly proposed vegetation index, the near-infrared (NIR) reflectance of vegetation (NIRv), was a good linear approximation of the canopy structure parameter, the directional area scattering factor (DASF), with an R2 of 0.99. NIRv was not influenced much by the soil background contribution, but was sensitive to the leaf inclination angle. The sensitivity of NIRv to canopy structure and the robustness of NIRv to the soil background suggest NIRv is a promising index in future biophysical variable estimations with the support of the SIP model, especially for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) observations near the hot spot directions.

ACS Style

Yelu Zeng; Baodong Xu; Gaofei Yin; Shengbiao Wu; Guoqing Hu; Kai Yan; Bin Yang; Wanjuan Song; Jing Li. Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations. Remote Sensing 2018, 10, 1508 .

AMA Style

Yelu Zeng, Baodong Xu, Gaofei Yin, Shengbiao Wu, Guoqing Hu, Kai Yan, Bin Yang, Wanjuan Song, Jing Li. Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations. Remote Sensing. 2018; 10 (10):1508.

Chicago/Turabian Style

Yelu Zeng; Baodong Xu; Gaofei Yin; Shengbiao Wu; Guoqing Hu; Kai Yan; Bin Yang; Wanjuan Song; Jing Li. 2018. "Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations." Remote Sensing 10, no. 10: 1508.

Journal article
Published: 01 November 2017 in Agricultural and Forest Meteorology
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ACS Style

Xihan Mu; Ronghai Hu; Yelu Zeng; Tim R. McVicar; Huazhong Ren; Wanjuan Song; Yuanyuan Wang; Raffaele Casa; Jianbo Qi; Donghui Xie; Guangjian Yan. Estimating structural parameters of agricultural crops from ground-based multi-angular digital images with a fractional model of sun and shade components. Agricultural and Forest Meteorology 2017, 246, 162 -177.

AMA Style

Xihan Mu, Ronghai Hu, Yelu Zeng, Tim R. McVicar, Huazhong Ren, Wanjuan Song, Yuanyuan Wang, Raffaele Casa, Jianbo Qi, Donghui Xie, Guangjian Yan. Estimating structural parameters of agricultural crops from ground-based multi-angular digital images with a fractional model of sun and shade components. Agricultural and Forest Meteorology. 2017; 246 ():162-177.

Chicago/Turabian Style

Xihan Mu; Ronghai Hu; Yelu Zeng; Tim R. McVicar; Huazhong Ren; Wanjuan Song; Yuanyuan Wang; Raffaele Casa; Jianbo Qi; Donghui Xie; Guangjian Yan. 2017. "Estimating structural parameters of agricultural crops from ground-based multi-angular digital images with a fractional model of sun and shade components." Agricultural and Forest Meteorology 246, no. : 162-177.

Proceedings article
Published: 01 July 2017 in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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Mixed pixels have a significant impact on the accurate estimation of Fractional Vegetation Cover (FVC) using digital photos acquired by Unmanned Aerial Vehicle (UAV). A single threshold is inadequate for the separation of vegetation and background when images contain numerous mixed pixels. We propose a spectral unmixing method to measure FVC with UAV-acquired digital images. In this method, the spectral mean values of vegetation and background are obtained as a priori spectral knowledge from the photos taken at a very low flight altitude around 5 meters above ground level (AGL). Two thresholds with high confidence level derived from the a priori knowledge are determined to select pure vegetation and background pixels from the photos taken at high flight altitudes ranging from dozens to hundreds of meters AGL. For the mixed pixels, endmember spectra are undertook by mean values of those two pure components. Images with different aggregation levels were generated from a 10 meters AGL image. A comparison with four commonly used methods indicated that our method could robustly characterize the FVC in a good agreement with the ground truth, and the accuracy of FVC estimates over corn crops was around 0.01 in terms of root mean square error (RMSE) value. All aggregated images produced stable FVC estimates and the corresponding standard deviation (STD) was around 0.01 with relative average deviation (RAD) being less than 0.15.

ACS Style

Linyuan Li; Guangjian Yan; Xihan Mu; Suhong; Liu; Yiming Chen; Kai Yan; Jinghui Luo; Wanjuan Song. Estimation of fractional vegetation cover using mean-based spectral unmixing method. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017, 3178 -3180.

AMA Style

Linyuan Li, Guangjian Yan, Xihan Mu, Suhong, Liu, Yiming Chen, Kai Yan, Jinghui Luo, Wanjuan Song. Estimation of fractional vegetation cover using mean-based spectral unmixing method. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2017; ():3178-3180.

Chicago/Turabian Style

Linyuan Li; Guangjian Yan; Xihan Mu; Suhong; Liu; Yiming Chen; Kai Yan; Jinghui Luo; Wanjuan Song. 2017. "Estimation of fractional vegetation cover using mean-based spectral unmixing method." 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 3178-3180.

Journal article
Published: 01 June 2017 in International Journal of Applied Earth Observation and Geoinformation
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ACS Style

Wanjuan Song; Xihan Mu; Gaiyan Ruan; Zhan Gao; Linyuan Li; Guangjian Yan. Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. International Journal of Applied Earth Observation and Geoinformation 2017, 58, 168 -176.

AMA Style

Wanjuan Song, Xihan Mu, Gaiyan Ruan, Zhan Gao, Linyuan Li, Guangjian Yan. Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. International Journal of Applied Earth Observation and Geoinformation. 2017; 58 ():168-176.

Chicago/Turabian Style

Wanjuan Song; Xihan Mu; Gaiyan Ruan; Zhan Gao; Linyuan Li; Guangjian Yan. 2017. "Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method." International Journal of Applied Earth Observation and Geoinformation 58, no. : 168-176.

Journal article
Published: 05 February 2016 in IEEE Transactions on Geoscience and Remote Sensing
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Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beer's law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one.

ACS Style

Guangjian Yan; Ronghai Hu; Yiting Wang; Huazhong Ren; Wanjuan Song; Jianbo Qi; Ling Chen. Scale Effect in Indirect Measurement of Leaf Area Index. IEEE Transactions on Geoscience and Remote Sensing 2016, 54, 3475 -3484.

AMA Style

Guangjian Yan, Ronghai Hu, Yiting Wang, Huazhong Ren, Wanjuan Song, Jianbo Qi, Ling Chen. Scale Effect in Indirect Measurement of Leaf Area Index. IEEE Transactions on Geoscience and Remote Sensing. 2016; 54 (6):3475-3484.

Chicago/Turabian Style

Guangjian Yan; Ronghai Hu; Yiting Wang; Huazhong Ren; Wanjuan Song; Jianbo Qi; Ling Chen. 2016. "Scale Effect in Indirect Measurement of Leaf Area Index." IEEE Transactions on Geoscience and Remote Sensing 54, no. 6: 3475-3484.

Journal article
Published: 02 December 2015 in Remote Sensing
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Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. This paper focuses on the sampling methods used to validate the coarse-resolution fractional vegetation cover (FVC) product in the Heihe River Basin, where the patterns of spatial variations in and between land cover types vary significantly in the different growth stages of vegetation. A sampling method, called the mean of surface with non-homogeneity (MSN) method, and three other sampling methods are examined with real-world data obtained in 2012. A series of 15-m-resolution fractional vegetation cover reference maps were generated using the regressions of field-measured and satellite data. The sampling methods were tested using the 15-m-resolution normalized difference vegetation index (NDVI) and land cover maps over a complete period of vegetation growth. Two scenes were selected to represent the situations in which sampling locations were sparsely and densely distributed. The results show that the FVCs estimated using the MSN method have errors of approximately less than 0.03 in the two selected scenes. The validation accuracy of the sampling methods varies with variations in the stratified non-homogeneity in the different growing stages of the vegetation. The MSN method, which considers both heterogeneity and autocorrelations between strata, is recommended for use in the determination of samplings prior to the design of an experimental campaign. In addition, the slight scaling bias caused by the non-linear relationship between NDVI and FVC samples is discussed. The positive or negative trend of the biases predicted using a Taylor expansion is found to be consistent with that of the real biases.

ACS Style

Xihan Mu; Maogui Hu; Wanjuan Song; Gaiyan Ruan; Yong Ge; Jinfeng Wang; Shuai Huang; Guangjian Yan. Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover. Remote Sensing 2015, 7, 16164 -16182.

AMA Style

Xihan Mu, Maogui Hu, Wanjuan Song, Gaiyan Ruan, Yong Ge, Jinfeng Wang, Shuai Huang, Guangjian Yan. Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover. Remote Sensing. 2015; 7 (12):16164-16182.

Chicago/Turabian Style

Xihan Mu; Maogui Hu; Wanjuan Song; Gaiyan Ruan; Yong Ge; Jinfeng Wang; Shuai Huang; Guangjian Yan. 2015. "Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover." Remote Sensing 7, no. 12: 16164-16182.

Journal article
Published: 14 August 2015 in Remote Sensing
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Taking photographs with a commercially available digital camera is an efficient and objective method for determining the green fractional vegetation cover (FVC) for field validation of satellite products. However, classifying leaves under shadows in processing digital images remains challenging and results in classification errors. To address this problem, an automatic shadow-resistant algorithm in the Commission Internationale d’Eclairage L*a*b* color space (SHAR-LABFVC) based on a documented FVC estimation algorithm (LABFVC) is proposed in this paper. The hue saturation intensity (HSI) is introduced in SHAR-LABFVC to enhance the brightness of shaded parts of the image. The lognormal distribution is used to fit the frequency of vegetation greenness and to classify vegetation and the background. Real and synthesized images are used for evaluation, and the results are in good agreement with the visual interpretation, particularly when the FVC is high and the shadows are deep, indicating that SHAR-LABFVC is shadow resistant. Without specific improvements to reduce the shadow effect, the underestimation of FVC can be up to 0.2 in the flourishing period of vegetation at a scale of 10 m. Therefore, the proposed algorithm is expected to improve the validation accuracy of remote sensing products.

ACS Style

Wanjuan Song; Xihan Mu; Guangjian Yan; Shuai Huang. Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC). Remote Sensing 2015, 7, 10425 -10443.

AMA Style

Wanjuan Song, Xihan Mu, Guangjian Yan, Shuai Huang. Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC). Remote Sensing. 2015; 7 (8):10425-10443.

Chicago/Turabian Style

Wanjuan Song; Xihan Mu; Guangjian Yan; Shuai Huang. 2015. "Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC)." Remote Sensing 7, no. 8: 10425-10443.

Journal article
Published: 15 August 2014 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Fractional vegetation cover (FVC) is one of the most important criteria for surface vegetation status. This criterion corresponds to the complement of gap fraction unity at the nadir direction and accounts for the amount of horizontal vegetation distribution. This study aims to directly validate the accuracy of FVC products over crops at coarse resolutions (1 km) by employing field measurements and high-resolution data. The study area was within an oasis in the Heihe Basin, Northwest China, where the Heihe Watershed Allied Telemetry Experimental Research was conducted. Reference FVC was generated through upscaling, which fitted field-measured data with spaceborne and airborne data to retrieve high-resolution FVC, and then high-resolution FVC was aggregated with a coarse scale. The fraction of green vegetation cover product (i.e., GEOV1 FVC) of SPOT/VEGETATION data taken during the GEOLAND2 project was compared with reference data. GEOV1 FVC was generally overestimated for crops in the study area compared with our estimates. Reference FVC exhibits a systematic uncertainty, and GEOV1 can overestimate FVC by up to 0.20. This finding indicates the necessity of reanalyzing and improving GEOV1 FVC over croplands.

ACS Style

Xihan Mu; Shuai Huang; Huazhong Ren; Guangjian Yan; Wanjuan Song; Gaiyan Ruan. Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014, 8, 439 -446.

AMA Style

Xihan Mu, Shuai Huang, Huazhong Ren, Guangjian Yan, Wanjuan Song, Gaiyan Ruan. Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2014; 8 (2):439-446.

Chicago/Turabian Style

Xihan Mu; Shuai Huang; Huazhong Ren; Guangjian Yan; Wanjuan Song; Gaiyan Ruan. 2014. "Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 2: 439-446.