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Dr. Yelu Zeng
Carnegie Institution of Washington, Washington, D.C., United States

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0 Canopy
0 Fluorescence
0 Remote Sensing
0 Vegetation
0 Phenology

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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.

Primary research article
Published: 09 February 2021 in Global Change Biology
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Remote sensing of solar‐induced fluorescence (SIF) opens a new window for quantifying a key ecological variable, the terrestrial ecosystem gross primary production (GPP), because of the revealed strong SIF‐GPP correlation. However, similar to many other remotely sensed metrics, SIF observations suffer from the sun‐sensor geometry effects, which may have important impacts on the SIF‐GPP relationship but remain poorly understood. Here we used remotely sensed SIF, globally distributed tower GPP data, and a mechanistic model to provide a systematic analysis. Our results reveal that leaf physiology, canopy structure and sun‐sensor geometries all affect the SIF‐GPP relationship. In particular, we found that SIF observations in the sun‐tracking hotspot direction can be a better proxy of GPP due to the similar responses of light use efficiency and SIF escaping probability in the hotspot direction to the increasing incoming solar radiation. Such conclusions are supported by a variety of modeling simulations and satellite observations over various plant function types, at different time scales and with satellite observational modes. This study demonstrates the potential and advantage of normalizing SIF observations to the hotspot direction for better global GPP estimations. This study also demonstrates the great potentials of current and future space‐borne sun‐tracking satellite missions for a significant improvement in measuring and monitoring, at a wide range of spatial and temporal scales, the changes of terrestrial ecosystem GPP in response to anticipated changes in the Earth’s environmental conditions.

ACS Style

Dalei Hao; Ghassem R. Asrar; Yelu Zeng; Xi Yang; Xing Li; Jingfeng Xiao; Kaiyu Guan; Jianguang Wen; Qing Xiao; Joseph A. Berry; Min Chen. Potential of hotspot solar‐induced chlorophyll fluorescence for better tracking terrestrial photosynthesis. Global Change Biology 2021, 27, 2144 -2158.

AMA Style

Dalei Hao, Ghassem R. Asrar, Yelu Zeng, Xi Yang, Xing Li, Jingfeng Xiao, Kaiyu Guan, Jianguang Wen, Qing Xiao, Joseph A. Berry, Min Chen. Potential of hotspot solar‐induced chlorophyll fluorescence for better tracking terrestrial photosynthesis. Global Change Biology. 2021; 27 (10):2144-2158.

Chicago/Turabian Style

Dalei Hao; Ghassem R. Asrar; Yelu Zeng; Xi Yang; Xing Li; Jingfeng Xiao; Kaiyu Guan; Jianguang Wen; Qing Xiao; Joseph A. Berry; Min Chen. 2021. "Potential of hotspot solar‐induced chlorophyll fluorescence for better tracking terrestrial photosynthesis." Global Change Biology 27, no. 10: 2144-2158.

Preprint content
Published: 31 December 2020
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Sun-induced chlorophyll fluorescence (SIF) is a promising new tool for remotely estimating photosynthesis. However, the degree to which incoming sunlight and the structure of the canopy rather than leaf physiology contribute to SIF variations is still not well characterized. Here we demonstrate that the canopy structure-related near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is a robust proxy of far-red SIF across a wide range of spatial and temporal scales. Our findings indicate that contributions from leaf physiology to SIF variability are small compared to its structure and radiation components. NIRvP captured spatio-temporal patterns of photosynthesis better than SIF, which seems to be mostly due to the retrieval noise of SIF. Our results highlight the promise of using widely available NIRvP data for vegetation monitoring and also indicate the potential of using SIF and NIRvP in combination to extract physiological information from SIF.

ACS Style

Benjamin DeChant; Youngryel Ryu; Grayson Badgley; Philipp Köhler; Uwe Rascher; Mirco Migliavacca; Yongguang Zhang; Giulia Tagliabue; Kaiyu Guan; Micol Rossini; Yves Goulas; Yelu Zeng; Christian Frankenberg; Joseph A. Berry. NIRvP: a robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales. 2020, 1 .

AMA Style

Benjamin DeChant, Youngryel Ryu, Grayson Badgley, Philipp Köhler, Uwe Rascher, Mirco Migliavacca, Yongguang Zhang, Giulia Tagliabue, Kaiyu Guan, Micol Rossini, Yves Goulas, Yelu Zeng, Christian Frankenberg, Joseph A. Berry. NIRvP: a robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales. . 2020; ():1.

Chicago/Turabian Style

Benjamin DeChant; Youngryel Ryu; Grayson Badgley; Philipp Köhler; Uwe Rascher; Mirco Migliavacca; Yongguang Zhang; Giulia Tagliabue; Kaiyu Guan; Micol Rossini; Yves Goulas; Yelu Zeng; Christian Frankenberg; Joseph A. Berry. 2020. "NIRvP: a robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales." , no. : 1.

Journal article
Published: 24 December 2020 in Remote Sensing of Environment
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Recent advances in remote sensing of solar-induced chlorophyll fluorescence (SIF) have improved the capabilities of monitoring large-scale Gross Primary Productivity (GPP). However, SIF observations are subject to directional effects which can lead to considerable uncertainties in various applications. Practical approaches for normalizing directional SIF observations to nadir viewing, to minimize the directional effects, have not been well studied. Here we developed two practical and physically-solid approaches for removing the directional effects of anisotropic SIF observations: one is based on near-infrared or red reflectance of vegetation (NIRv and Redv), and the other is based on the kernel-driven model with multi-angular SIF measurements. The first approach uses surface reflectance while the second approach directly leverages multi-angular SIF measurements. The performance of the two approaches was evaluated using a dataset of multi-angular measurements of SIF and reflectance collected with a high-resolution field spectrometer over different plant canopies. Results show that the relative mean absolute errors between the normalized nadir SIF and the observed SIF at nadir decrease by 3–6% (far-red) and 6–8% (red) for the first approach, and by 7–13% and 6–11% for the second approach, compared to the original data, respectively. The effectiveness and simplicity of our proposed approaches provide great potential to generate long-term and consistent SIF data records with minimized directional effects.

ACS Style

Dalei Hao; Yelu Zeng; Han Qiu; Khelvi Biriukova; Marco Celesti; Mirco Migliavacca; Micol Rossini; Ghassem R. Asrar; Min Chen. Practical approaches for normalizing directional solar-induced fluorescence to a standard viewing geometry. Remote Sensing of Environment 2020, 255, 112171 .

AMA Style

Dalei Hao, Yelu Zeng, Han Qiu, Khelvi Biriukova, Marco Celesti, Mirco Migliavacca, Micol Rossini, Ghassem R. Asrar, Min Chen. Practical approaches for normalizing directional solar-induced fluorescence to a standard viewing geometry. Remote Sensing of Environment. 2020; 255 ():112171.

Chicago/Turabian Style

Dalei Hao; Yelu Zeng; Han Qiu; Khelvi Biriukova; Marco Celesti; Mirco Migliavacca; Micol Rossini; Ghassem R. Asrar; Min Chen. 2020. "Practical approaches for normalizing directional solar-induced fluorescence to a standard viewing geometry." Remote Sensing of Environment 255, no. : 112171.

Short communication
Published: 04 November 2020 in Remote Sensing of Environment
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Leaf optical spectra reflect the combination of leaf biochemical, morphological and physiological properties, and play an important role in many ecological and Earth system processes. Radiative transfer models are widely used to simulate leaf spectra by quantifying photon transfer processes of reflection, transmission and absorption within a plant leaf. Recent advances in spectral invariants theory offer a unique and efficient approach for modeling the canopy-scale radiative transfer processes, but remain underexplored for applications at the leaf scale. In this study, we developed a leaf-scale optical property model based on the spectrally invariant properties (leaf-SIP) of plant leaves. Similar to the canopy-scale model, the leaf-SIP model decouples leaf-scale radiative transfer process into two parts: wavelength-dependent contribution from leaf chemical components and wavelength-independent contribution from leaf structures, described by two spectrally invariant parameters (i.e., a photon recollision probability p and a scattering asymmetry parameter q). We implemented the leaf-SIP model by parameterizing p and q with a measurable leaf morphological trait, the leaf mass per area (LMA). We evaluated the performance of the leaf-SIP model with two in situ datasets (i.e., LOPEX and ANGERS) and the widely used PROSPECT leaf optical model. The results show that the leaf spectra simulated by the leaf-SIP model agreed well with in situ datasets and the simulations of the PROSPECT model, with a small root mean squared error (RMSE), bias, and high coefficients of determination (R2) of 0.026, 0.035, 0.95 and 0.037, 0.049, 0.91 for leaf reflectance and leaf transmittance, respectively. Our results also show that the leaf-SIP model can be used with measured leaf spectra to accurately estimate several key leaf functional traits, such as the leaf chlorophyll content, equivalent water thickness, and LMA. The leaf-SIP model provides an efficient and physical way of accurately simulating leaf spectra and retrieving key leaf functional traits from hyperspectral measurements.

ACS Style

Shengbiao Wu; Yelu Zeng; Dalei Hao; Qinhuo Liu; Jing Li; Xiuzhi Chen; Ghassem R. Asrar; Gaofei Yin; Jianguang Wen; Bin Yang; Peng Zhu; Min Chen. Quantifying leaf optical properties with spectral invariants theory. Remote Sensing of Environment 2020, 253, 112131 .

AMA Style

Shengbiao Wu, Yelu Zeng, Dalei Hao, Qinhuo Liu, Jing Li, Xiuzhi Chen, Ghassem R. Asrar, Gaofei Yin, Jianguang Wen, Bin Yang, Peng Zhu, Min Chen. Quantifying leaf optical properties with spectral invariants theory. Remote Sensing of Environment. 2020; 253 ():112131.

Chicago/Turabian Style

Shengbiao Wu; Yelu Zeng; Dalei Hao; Qinhuo Liu; Jing Li; Xiuzhi Chen; Ghassem R. Asrar; Gaofei Yin; Jianguang Wen; Bin Yang; Peng Zhu; Min Chen. 2020. "Quantifying leaf optical properties with spectral invariants theory." Remote Sensing of Environment 253, no. : 112131.

Journal article
Published: 15 July 2020 in Journal of Geophysical Research: Biogeosciences
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We test the relationship between canopy photosynthesis and reflected near infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near infrared and red radiation from vegetation, with light emitting diode sensors. And, we interpreted the observed relationships between photosynthesis and reflected near infrared radiation using simulations based on the multi‐layer, biophysical model, CanVeg. Measurements of reflected near infrared radiation were highly correlated with measurements of canopy photosynthesis on half‐hourly, daily, seasonal, annual and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near infrared radiation was greatest for the fertilized and irrigated C4 corn crop, intermediate for the C3 tules on nutrient rich organic soil and nitrogen fixing alfalfa and least for the native annual grasslands and oak savanna on nutrient poor, mineral soils. Reflected near infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications.

ACS Style

Dennis D. Baldocchi; Youngryel Ryu; Benjamin Dechant; Elke Eichelmann; Kyle Hemes; Siyan Ma; Camilo Rey Sanchez; Robert Shortt; Daphne Szutu; Alex Valach; Joe Verfaillie; Grayson Badgley; Yelu Zeng; Joseph A. Berry. Outgoing Near‐Infrared Radiation From Vegetation Scales With Canopy Photosynthesis Across a Spectrum of Function, Structure, Physiological Capacity, and Weather. Journal of Geophysical Research: Biogeosciences 2020, 125, 1 .

AMA Style

Dennis D. Baldocchi, Youngryel Ryu, Benjamin Dechant, Elke Eichelmann, Kyle Hemes, Siyan Ma, Camilo Rey Sanchez, Robert Shortt, Daphne Szutu, Alex Valach, Joe Verfaillie, Grayson Badgley, Yelu Zeng, Joseph A. Berry. Outgoing Near‐Infrared Radiation From Vegetation Scales With Canopy Photosynthesis Across a Spectrum of Function, Structure, Physiological Capacity, and Weather. Journal of Geophysical Research: Biogeosciences. 2020; 125 (7):1.

Chicago/Turabian Style

Dennis D. Baldocchi; Youngryel Ryu; Benjamin Dechant; Elke Eichelmann; Kyle Hemes; Siyan Ma; Camilo Rey Sanchez; Robert Shortt; Daphne Szutu; Alex Valach; Joe Verfaillie; Grayson Badgley; Yelu Zeng; Joseph A. Berry. 2020. "Outgoing Near‐Infrared Radiation From Vegetation Scales With Canopy Photosynthesis Across a Spectrum of Function, Structure, Physiological Capacity, and Weather." Journal of Geophysical Research: Biogeosciences 125, no. 7: 1.

Journal article
Published: 30 April 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Topographic correction is a prerequisite for generating radiometrically consistent Landsat 8 OLI vegetation reflectances in support of temporally continuous and spatially mosaicked applications. Path length correction (PLC) is a physically solid topographic correction method that avoids the involvement of any empirical parameter and is therefore suitable for reproducing the inherent reflectance of vegetation. This article compared two different implementation pathways of PLC, i.e., the explicit method (EM) and the implicit method (IM), which are based on the numerical inverse and analytical approximation of the PLC model, respectively. The results show that both EM and IM can obviously reduce the topographic effects on Landsat 8 OLI vegetation reflectances. EM performed slightly better than IM in eliminating the correlation between the topographic characteristics and the vegetation reflectances: the coefficient of determination between the green/red/ near-infrared (Nir) band reflectance and the local illumination was reduced from 0.257/0.148/0.467 for the uncorrected (UNCORR) case to 0.016/0.004/0.012 and 0.027/0.014/0.094 for the EM and IM corrected results, respectively. The coefficient of variation of the three band reflectances across different aspects was reduced from 16.5%/18.5%/18.7% for the UNCORR case to 3.2%/1.8%/0.9% and 5.3%/7.1%/7.3% for the EM and IM corrected results, respectively. In addition, the intraclass reflectance variability was also reduced after both the EM and IM corrections. Nevertheless, due to the ill-posed nature of the numerical inverse process, EM cannot fully reproduce the inherent vegetation reflectances, and the reflectances after topographic correction overestimated the inherent vegetation values. In contrast, the IM can achieve an appropriate tradeoff between topographic effect elimination and vegetation inherent reflectance preservation. In addition, IM is computationally very efficient compared to EM: using an ordinary laptop, IM can finish the topographic correction for a Landsat OLI image within several seconds, while this would take more than 20 h for EM. This article highlights the potential of using IM for generating radiometrically consistent Landsat 8 OLI vegetation reflectances.

ACS Style

Gaofei Yin; Lei Ma; Wei Zhao; Yelu Zeng; Baodong Xu; Shengbiao Wu. Topographic Correction for Landsat 8 OLI Vegetation Reflectances Through Path Length Correction: A Comparison Between Explicit and Implicit Methods. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 8477 -8489.

AMA Style

Gaofei Yin, Lei Ma, Wei Zhao, Yelu Zeng, Baodong Xu, Shengbiao Wu. Topographic Correction for Landsat 8 OLI Vegetation Reflectances Through Path Length Correction: A Comparison Between Explicit and Implicit Methods. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (12):8477-8489.

Chicago/Turabian Style

Gaofei Yin; Lei Ma; Wei Zhao; Yelu Zeng; Baodong Xu; Shengbiao Wu. 2020. "Topographic Correction for Landsat 8 OLI Vegetation Reflectances Through Path Length Correction: A Comparison Between Explicit and Implicit Methods." IEEE Transactions on Geoscience and Remote Sensing 58, no. 12: 8477-8489.

Journal article
Published: 23 April 2020 in Remote Sensing
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Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time.

ACS Style

Xuanlong Ma; Alfredo Huete; Ngoc Tran; Jian Bi; Sicong Gao; Yelu Zeng. Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8. Remote Sensing 2020, 12, 1339 .

AMA Style

Xuanlong Ma, Alfredo Huete, Ngoc Tran, Jian Bi, Sicong Gao, Yelu Zeng. Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8. Remote Sensing. 2020; 12 (8):1339.

Chicago/Turabian Style

Xuanlong Ma; Alfredo Huete; Ngoc Tran; Jian Bi; Sicong Gao; Yelu Zeng. 2020. "Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8." Remote Sensing 12, no. 8: 1339.

Journal article
Published: 12 March 2020 in Remote Sensing
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Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consistent measurement criteria. Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several LAI, FAPAR, and FVC retrievals were derived for non-vegetated pixels. The rate of best retrievals is similar between LAI and FAPAR estimates, both accounting for 87% of all vegetation pixels, while it is almost 100% for FVC estimates. Additionally, the Sentinel-2 FAPAR and FVC estimates agree well with ground-measurements-derived (GMD) reference maps, whereas a large discrepancy is observed for Sentinel-2 LAI estimates by comparing with both GMD effective LAI (LAIe) and actual LAI (LAI) reference maps. Furthermore, the uncertainties of Sentinel-2 LAI, FAPAR and FVC estimates are 1.09 m2/m2, 1.14 m2/m2, 0.13 and 0.17 through comparisons to ground LAIe, LAI, FAPAR, and FVC measurements, respectively. Given the temporal difference between Sentinel-2 observations and ground measurements, Sentinel-2 LAI estimates are more consistent with LAIe than LAI values. The robustness of evaluation results can be further improved as long as more multi-temporal ground measurements across different regions are obtained. Overall, this study provides fundamental information about the performance of Sentinel-2 LAI, FAPAR, and FVC estimates, which imbues our confidence in the broad applications of these decametric products.

ACS Style

Qiong Hu; Jingya Yang; Baodong Xu; Jianxi Huang; Muhammad Sohail Memon; Gaofei Yin; Yelu Zeng; Jing Zhao; Ke Liu. Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery. Remote Sensing 2020, 12, 912 .

AMA Style

Qiong Hu, Jingya Yang, Baodong Xu, Jianxi Huang, Muhammad Sohail Memon, Gaofei Yin, Yelu Zeng, Jing Zhao, Ke Liu. Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery. Remote Sensing. 2020; 12 (6):912.

Chicago/Turabian Style

Qiong Hu; Jingya Yang; Baodong Xu; Jianxi Huang; Muhammad Sohail Memon; Gaofei Yin; Yelu Zeng; Jing Zhao; Ke Liu. 2020. "Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery." Remote Sensing 12, no. 6: 912.

Journal article
Published: 12 February 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Topography significantly complicates the radiative transfer process of vegetation and further causes variation in reflectance observed by remote sensors. Leaf area index (LAI) inversion based on reflectance data is subsequently influenced by topography. Neglecting the topographic effects may lead to large biases when estimating LAI over rugged terrain. How the topography influences the LAI inversion process has rarely been explored. In this study, the topographic effects on LAI inversion over sloped terrain are quantitatively investigated and analyzed based on a dataset generated from the discrete anisotropy radiative transfer (DART) model. An ANN (artificial neural network) model is established to represent the flat surface LAI inversion algorithms. Then the reflectance of sloped terrain is input into the ANN model to obtain the biased LAI inversion values. The results reveal that topography effects on LAI inversion are related to canopy density and generally lead to an underestimation except for sparse canopies. The mean relative bias could reach 51% when the slope angle reaches 60°. The variation trends of inverted LAI are closely related to the local incident angle. The different levels of bias in reflectance at red and near-infrared (NIR) bands lead to different patterns of inversion errors for different canopies densities. Finally, we compared the existing strategies (geometric correction and topographic correction strategies) designed for LAI inversion over sloped terrain. It is found that these strategies apply in different situations. The results are helpful in understanding the topographic effects and further finding a better strategy for LAI inversion over sloped terrain.

ACS Style

Wentao Yu; Jing Li; Qinhuo Liu; Gaofei Yin; Yelu Zeng; Shangrong Lin; Jing Zhao. A Simulation-Based Analysis of Topographic Effects on LAI Inversion Over Sloped Terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 794 -806.

AMA Style

Wentao Yu, Jing Li, Qinhuo Liu, Gaofei Yin, Yelu Zeng, Shangrong Lin, Jing Zhao. A Simulation-Based Analysis of Topographic Effects on LAI Inversion Over Sloped Terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):794-806.

Chicago/Turabian Style

Wentao Yu; Jing Li; Qinhuo Liu; Gaofei Yin; Yelu Zeng; Shangrong Lin; Jing Zhao. 2020. "A Simulation-Based Analysis of Topographic Effects on LAI Inversion Over Sloped Terrain." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 794-806.

Journal article
Published: 30 January 2020 in IEEE Transactions on Geoscience and Remote Sensing
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The in situ measurement of the leaf area index (LAI) from gap fraction is often affected by terrain slope. Path length correction (PLC) is commonly used to mitigate the topographic effect on the LAI measurements. However, the terrain-induced uncertainty and the accuracy improvement of the PLC for LAI measurements have not been systematically analyzed, hindering the establishment of an appropriate protocol for LAI measurements over mountainous regions. In this article, the above knowledge gap was filled using a computer simulation framework, which enables the estimated LAI before and after PLC to be benchmarked against the known and precise model truth. The simulation was achieved by using CANOPIX software and a dedicatedly designed ray-tracing method for continuous and discrete canopies, respectively. Simulations show that the slope distorts the angular pattern of the gap fraction, i.e., increasing the gap fraction in the down-slope direction and reducing it in the up-slope direction. The horizontally equivalent hemispheric gap fraction from the PLC can reconstruct the azimuthally symmetric angular pattern of the real horizontal surface. The azimuthally averaged gap fraction for sloping terrain can both be underestimated or overestimated depending on the LAI and can be successfully corrected through PLC. The topography-induced uncertainty in LAI measurements is found to be ~14.3% and >20% for continuous and discrete canopies, respectively. This uncertainty can be, respectively, reduced to ~1.8% and <7.3% after PLC, meeting the up-to-date uncertainty threshold of 15% established by the Global Climate Observing System (GCOS). Closer analysis shows that the topographic effect is influenced by fractional crown cover, and the largest uncertainty which corresponds to extensively clumping canopy can reach nearly up to 50%. The accuracy of the estimated LAI after PLC safely meets the GCOS uncertainty threshold even for this extreme case. This study demonstrates the necessity of a topographic correction for LAI measurements and the applicability of PLC for reconstructing the horizontally equivalent gap fraction and improving the LAI measurements over sloping terrains. The results of this article throw light on the design of a protocol for LAI measurements over mountainous regions.

ACS Style

Gaofei Yin; Biao Cao; Jing Li; Weiliang Fan; Yelu Zeng; Baodong Xu; Wei Zhao. Path Length Correction for Improving Leaf Area Index Measurements Over Sloping Terrains: A Deep Analysis Through Computer Simulation. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 4573 -4589.

AMA Style

Gaofei Yin, Biao Cao, Jing Li, Weiliang Fan, Yelu Zeng, Baodong Xu, Wei Zhao. Path Length Correction for Improving Leaf Area Index Measurements Over Sloping Terrains: A Deep Analysis Through Computer Simulation. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (7):4573-4589.

Chicago/Turabian Style

Gaofei Yin; Biao Cao; Jing Li; Weiliang Fan; Yelu Zeng; Baodong Xu; Wei Zhao. 2020. "Path Length Correction for Improving Leaf Area Index Measurements Over Sloping Terrains: A Deep Analysis Through Computer Simulation." IEEE Transactions on Geoscience and Remote Sensing 58, no. 7: 4573-4589.

Journal article
Published: 12 December 2019 in IEEE Transactions on Geoscience and Remote Sensing
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The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves R² from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.

ACS Style

Yelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Weiliang Fan; Yixuan Ouyang; Kai Yan; Dalei Hao; Min Chen. A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory. IEEE Transactions on Geoscience and Remote Sensing 2019, 58, 2571 -2589.

AMA Style

Yelu Zeng, Jing Li, Qinhuo Liu, Alfredo R. Huete, Baodong Xu, Gaofei Yin, Weiliang Fan, Yixuan Ouyang, Kai Yan, Dalei Hao, Min Chen. A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory. IEEE Transactions on Geoscience and Remote Sensing. 2019; 58 (4):2571-2589.

Chicago/Turabian Style

Yelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Weiliang Fan; Yixuan Ouyang; Kai Yan; Dalei Hao; Min Chen. 2019. "A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory." IEEE Transactions on Geoscience and Remote Sensing 58, no. 4: 2571-2589.

Journal article
Published: 01 October 2019 in Remote Sensing of Environment
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Dalei Hao; Ghassem R. Asrar; Yelu Zeng; Qing Zhu; Jianguang Wen; Qing Xiao; Min Chen. Estimating hourly land surface downward shortwave and photosynthetically active radiation from DSCOVR/EPIC observations. Remote Sensing of Environment 2019, 232, 1 .

AMA Style

Dalei Hao, Ghassem R. Asrar, Yelu Zeng, Qing Zhu, Jianguang Wen, Qing Xiao, Min Chen. Estimating hourly land surface downward shortwave and photosynthetically active radiation from DSCOVR/EPIC observations. Remote Sensing of Environment. 2019; 232 ():1.

Chicago/Turabian Style

Dalei Hao; Ghassem R. Asrar; Yelu Zeng; Qing Zhu; Jianguang Wen; Qing Xiao; Min Chen. 2019. "Estimating hourly land surface downward shortwave and photosynthetically active radiation from DSCOVR/EPIC observations." Remote Sensing of Environment 232, no. : 1.

Journal article
Published: 01 October 2019 in Remote Sensing of Environment
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Yelu Zeng; Grayson Badgley; Benjamin Dechant; Youngryel Ryu; Min Chen; J.A. Berry. A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence. Remote Sensing of Environment 2019, 232, 111209 .

AMA Style

Yelu Zeng, Grayson Badgley, Benjamin Dechant, Youngryel Ryu, Min Chen, J.A. Berry. A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence. Remote Sensing of Environment. 2019; 232 ():111209.

Chicago/Turabian Style

Yelu Zeng; Grayson Badgley; Benjamin Dechant; Youngryel Ryu; Min Chen; J.A. Berry. 2019. "A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence." Remote Sensing of Environment 232, no. : 111209.

Journal article
Published: 24 January 2019 in Remote Sensing
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Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.

ACS Style

Gaofei Yin; Aleixandre Verger; Yonghua Qu; Wei Zhao; Baodong Xu; Yelu Zeng; Ke Liu; Jing Li; Qinhuo Liu. Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion. Remote Sensing 2019, 11, 244 .

AMA Style

Gaofei Yin, Aleixandre Verger, Yonghua Qu, Wei Zhao, Baodong Xu, Yelu Zeng, Ke Liu, Jing Li, Qinhuo Liu. Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion. Remote Sensing. 2019; 11 (3):244.

Chicago/Turabian Style

Gaofei Yin; Aleixandre Verger; Yonghua Qu; Wei Zhao; Baodong Xu; Yelu Zeng; Ke Liu; Jing Li; Qinhuo Liu. 2019. "Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion." Remote Sensing 11, no. 3: 244.

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: 22 June 2018 in IEEE Transactions on Geoscience and Remote Sensing
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Estimation of daily downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. The combination of satellite-based instantaneous measurements and temporal extrapolation models is the most feasible way to capture daily radiation variations at large scales. However, previous studies did not pay enough attention to topographic effects and simple temporal extrapolation methods were applied directly to rugged terrains which cover a large amount of the land surface. This paper, divided into two parts, aims at analyzing the topographic uncertainties of existing models and proposing a better method based on a mountain radiative transfer (MRT) model to calculate daily DSR. As the first part, this paper analyze the spatiotemporal variations of DSR influenced by topographic effects and checks the applicability of three temporal extrapolation methods on cloud-free days. Considering that clouds also have a strong influence on solar radiation, cloud-free days are chosen for targeted analysis of topographic effects on DSR. Three indices, the coefficient of variation, entropy-based dispersion coefficient (CH), and sill of semivariogram, are put forward to give a quantitative description of spatial heterogeneity. Our results show that the topography can dramatically strengthen the spatial heterogeneity of DSR. The index, CH, has an advantage for quantifying spatial heterogeneity as it offers a tradeoff between accuracy and efficiency. Spatial heterogeneity distorts the daily variation of DSR. Application of extrapolation methods in rugged terrains leads to overestimation of daily average DSR up to 60 W/m2 and a maximum 200 W/m2 error of instantaneous DSR on cloud-free days. This paper makes a quantitative analysis of topographic effects under different spatiotemporal conditions, which lays the foundation for developing a new extrapolation method.

ACS Style

Guangjian Yan; Yiyi Tong; Kai Yan; Xihan Mu; Qing Chu; Yingji Zhou; Yanan Liu; Jianbo Qi; Linyuan Li; Yelu Zeng; Hongmin Zhou; Donghui Xie; Wuming Zhang. Temporal Extrapolation of Daily Downward Shortwave Radiation Over Cloud-Free Rugged Terrains. Part 1: Analysis of Topographic Effects. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 6375 -6394.

AMA Style

Guangjian Yan, Yiyi Tong, Kai Yan, Xihan Mu, Qing Chu, Yingji Zhou, Yanan Liu, Jianbo Qi, Linyuan Li, Yelu Zeng, Hongmin Zhou, Donghui Xie, Wuming Zhang. Temporal Extrapolation of Daily Downward Shortwave Radiation Over Cloud-Free Rugged Terrains. Part 1: Analysis of Topographic Effects. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (11):6375-6394.

Chicago/Turabian Style

Guangjian Yan; Yiyi Tong; Kai Yan; Xihan Mu; Qing Chu; Yingji Zhou; Yanan Liu; Jianbo Qi; Linyuan Li; Yelu Zeng; Hongmin Zhou; Donghui Xie; Wuming Zhang. 2018. "Temporal Extrapolation of Daily Downward Shortwave Radiation Over Cloud-Free Rugged Terrains. Part 1: Analysis of Topographic Effects." IEEE Transactions on Geoscience and Remote Sensing 56, no. 11: 6375-6394.

Journal article
Published: 01 June 2018 in Remote Sensing
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Spatial heterogeneity is present in the land surface at every scale and is one of the key factors that introduces inherent uncertainty into simulations of land surface processes and parameter retrieval based on remotely sensed data. Because of a lack of understanding of the heterogeneous characteristics of global mixed pixels, few studies have focused on modeling and inversion algorithms in heterogeneous areas. This paper presents a parameterization scheme to describe land cover heterogeneity quantitatively by composition and boundary information based on high-resolution land cover products. Global heterogeneity features at the 1-km scale are extracted from the ‘GlobeLand30’ land cover dataset with a spatial resolution of 30 m. The composition analysis of global mixed pixels shows that only 35% of pixels over the land surface of Earth are covered by a single land cover type, namely, pure pixels, and only 25.8% are located in vegetated areas. Pixels mixed with water are more common than pixels mixed with any other non-vegetation type. The fragmentation analysis of typical biomes based on the boundary length shows that the savanna is the most heterogeneous biome, while the evergreen broadleaf forest is the least heterogeneous. Deciduous needleleaf forests are significantly affected by canopy height differences, while crop and grass biomes are less affected. Lastly, the strengths and limitations of the method and the application of the land cover heterogeneity characteristics extracted in this study are discussed.

ACS Style

Wentao Yu; Jing Li; Qinhuo Liu; Yelu Zeng; Jing Zhao; Baodong Xu; Gaofei Yin. Global Land Cover Heterogeneity Characteristics at Moderate Resolution for Mixed Pixel Modeling and Inversion. Remote Sensing 2018, 10, 856 .

AMA Style

Wentao Yu, Jing Li, Qinhuo Liu, Yelu Zeng, Jing Zhao, Baodong Xu, Gaofei Yin. Global Land Cover Heterogeneity Characteristics at Moderate Resolution for Mixed Pixel Modeling and Inversion. Remote Sensing. 2018; 10 (6):856.

Chicago/Turabian Style

Wentao Yu; Jing Li; Qinhuo Liu; Yelu Zeng; Jing Zhao; Baodong Xu; Gaofei Yin. 2018. "Global Land Cover Heterogeneity Characteristics at Moderate Resolution for Mixed Pixel Modeling and Inversion." Remote Sensing 10, no. 6: 856.

Journal article
Published: 07 May 2018 in Remote Sensing
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Topographic correction methods rarely consider the canopy parameter effects directly and explicitly for sloping canopies. In order to address this problem, the topographic correction method MFM-GOST2 was developed by implementing the second version of the Geometric-Optical model for Sloping Terrains (the GOST2 model) in the multiple forward mode (MFM) inversion framework. First, a look up table (LUT) was constructed by multiple forward modeling of the GOST2 model; second, the radiance of a remotely sensed image and its corresponding topographic data were used for searching potential canopy parameter combinations from the LUT; and third, the corrected radiance was determined by averaging potential radiances of horizontal canopies from the LUT according to the canopy parameter combinations. The MFM-GOST2 and twelve generally used topographic correction methods were evaluated via a case study by visual analysis, linear relationship analysis, and the rose diagram analysis. The result showed that the MFM-GOST2 method successfully removed most of the topographic effects of a subset image of the Landsat-8 image in a case study. The case study also illustrates that the rose diagram analysis is a good way to evaluate topographic corrections, but the linear relationship analysis cannot be used independently for the evaluations because the decorrelation is not a sufficient condition to determine a successful topographic correction.

ACS Style

Weiliang Fan; Jing Li; Qinhuo Liu; Qian Zhang; Gaifei Yin; Ainong Li; Yelu Zeng; Baodong Xu; Xiaojun Xu; Guomo Zhou; Huaqiang Du. Topographic Correction of Forest Image Data Based on the Canopy Reflectance Model for Sloping Terrains in Multiple Forward Mode. Remote Sensing 2018, 10, 717 .

AMA Style

Weiliang Fan, Jing Li, Qinhuo Liu, Qian Zhang, Gaifei Yin, Ainong Li, Yelu Zeng, Baodong Xu, Xiaojun Xu, Guomo Zhou, Huaqiang Du. Topographic Correction of Forest Image Data Based on the Canopy Reflectance Model for Sloping Terrains in Multiple Forward Mode. Remote Sensing. 2018; 10 (5):717.

Chicago/Turabian Style

Weiliang Fan; Jing Li; Qinhuo Liu; Qian Zhang; Gaifei Yin; Ainong Li; Yelu Zeng; Baodong Xu; Xiaojun Xu; Guomo Zhou; Huaqiang Du. 2018. "Topographic Correction of Forest Image Data Based on the Canopy Reflectance Model for Sloping Terrains in Multiple Forward Mode." Remote Sensing 10, no. 5: 717.