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Jing Li
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

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Journal article
Published: 23 August 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Spatiotemporally continuous long-term leaf area index (LAI) products are urgently needed to monitor environmental changes. The current filter- or curve-fitting-based time series reconstructive algorithms fail to reconstruct the LAI time series with many continuous missing values or missing values in key phenological periods, which are common issues in high-spatial-resolution LAI time series. In this article, a meteorological data-driven backpropagation neural network (MBPNN) was proposed to reconstruct discontinuous LAI profiles with a two-step process using vegetation phenological information. As the basis of the strong dependence of vegetation growth on meteorological conditions, a reasonable growth trajectory of reconstructed LAI can be guaranteed by the algorithm even though if many observed values are missing. Validations for reconstructed LAI were conducted both spatially and temporally based on reference maps and field-measured long-term observations. The results showed that the LAI predicted by the MBPNN had a similar accuracy (RMSE = 0.4076) as the Landsat LAI inversions (RMSE = 0.4083) and a similar reconstructed trajectory as the field-measured LAI series even though over 100 days of continuous data were missing (RMSE = 0.1620). A comparison with the Harmonic ANalysis of Time Series (HANTS) algorithm showed that the accuracy of MBPNN was more stable regardless of the size/position of the missing data, and the proposed method performed much better when the data were continuously missing for 50 days or more.

ACS Style

Xinran Zhu; Jing Li; Qinhuo Liu; Wentao Yu; Songze Li; Jing Zhao; Yadong Dong; Zhaoxing Zhang; Hu Zhang; Shangrong Lin. Use of a BP Neural Network and Meteorological Data for Generating Spatiotemporally Continuous LAI Time Series. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.

AMA Style

Xinran Zhu, Jing Li, Qinhuo Liu, Wentao Yu, Songze Li, Jing Zhao, Yadong Dong, Zhaoxing Zhang, Hu Zhang, Shangrong Lin. Use of a BP Neural Network and Meteorological Data for Generating Spatiotemporally Continuous LAI Time Series. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.

Chicago/Turabian Style

Xinran Zhu; Jing Li; Qinhuo Liu; Wentao Yu; Songze Li; Jing Zhao; Yadong Dong; Zhaoxing Zhang; Hu Zhang; Shangrong Lin. 2021. "Use of a BP Neural Network and Meteorological Data for Generating Spatiotemporally Continuous LAI Time Series." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 10 April 2021 in International Journal of Applied Earth Observation and Geoinformation
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Satellite-based light use efficiency (LUE) models are important tools for estimating regional and global vegetation gross primary productivity (GPP). However, all LUE models assume a constant value of maximum LUE at canopy scale (LUEmaxcanopy) over a given vegetation type. This assumption is not supported by observed plant traits regulating LUEmaxcanopy, which varies greatly even within the same ecosystem type. In this study, we developed an improved satellite data driven GPP model by identifying the potential maximal GPP (GPPPOT) and their dominant climate control factor in various plant functional types (PFT), which takes into account both plant trait and climatic control inter-dependence. We selected 161 sites from the FLUXNET2015 dataset with eddy covariance CO2 flux data and continuous meteorology to derive GPPPOT and their dominant climate control factor of vegetation growth for 42 natural PFTs. Results showed that (1) under the same phenology and incident photosynthetic active radiation, the maximal variance of GPPPOT is found in different PFTs of forests (10.9 g C m−2 day−1) and in different climatic zones of grasslands (>10 g C m−2 day−1); (2) intra-annual change of GPP in tropical and arid climate zones is mostly driven by vapor pressure deficit (VPD) changes, while temperature is the dominant climate control factor in temperate, boreal and polar climate zones; even under the same climate condition, physiological stress in photosynthesis is different across PFTs; (3) the model that takes into account the plant trait difference across PFTs had a higher agreement with flux tower-based GPP data (GPPflux) than the GPP products that omit PFT differences. Such agreement was highest for natural vegetation cover sites (R2 = 0.77, RMSE = 1.79 g C m−2 day−1). These results suggest that global scale GPP models should incorporate both plant traits and their dominant climate control factor variance in various PFT to reduce the uncertainties in terrestrial carbon assessments.

ACS Style

Shangrong Lin; Jing Li; Qinhuo Liu; Beniamino Gioli; Eugenie Paul-Limoges; Nina Buchmann; Mana Gharun; Lukas Hörtnagl; Lenka Foltýnová; Jiří Dušek; Longhui Li; Wenping Yuan. Improved global estimations of gross primary productivity of natural vegetation types by incorporating plant functional type. International Journal of Applied Earth Observation and Geoinformation 2021, 100, 102328 .

AMA Style

Shangrong Lin, Jing Li, Qinhuo Liu, Beniamino Gioli, Eugenie Paul-Limoges, Nina Buchmann, Mana Gharun, Lukas Hörtnagl, Lenka Foltýnová, Jiří Dušek, Longhui Li, Wenping Yuan. Improved global estimations of gross primary productivity of natural vegetation types by incorporating plant functional type. International Journal of Applied Earth Observation and Geoinformation. 2021; 100 ():102328.

Chicago/Turabian Style

Shangrong Lin; Jing Li; Qinhuo Liu; Beniamino Gioli; Eugenie Paul-Limoges; Nina Buchmann; Mana Gharun; Lukas Hörtnagl; Lenka Foltýnová; Jiří Dušek; Longhui Li; Wenping Yuan. 2021. "Improved global estimations of gross primary productivity of natural vegetation types by incorporating plant functional type." International Journal of Applied Earth Observation and Geoinformation 100, no. : 102328.

Journal article
Published: 23 March 2021 in IEEE Geoscience and Remote Sensing Letters
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Vegetation index (VI) derived from remotely sensed images is a proxy of terrestrial vegetation information and widely used in land monitoring and global change studies. Recently, the prediction of vegetation properties has been an interest in related communities. With the accumulation of satellite records over the past few decades, the spatial-temporal prediction of VI becomes feasible. In this letter, we developed deep recurrent neural networks (RNNs) with long short-term memory (LSTM) and gated recurrent units (GRUs) to predict the short-term VI based on historical observations. The pixel-based fully connected networks GRU and LSTM (FCGRU and FCLSTM) and patch-based convolutional networks (ConvGRU and ConvLSTM) are established and compared with the traditional multilayer perceptron (MLP) model. Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 normalized difference VI (NDVI) data sets were used in the experiments. The prediction performance is evaluated globally in different regions, different vegetation types, and different growing seasons. Results demonstrate that the RNN models can predict VI with high accuracy (average root mean square error (RMSE) around 0.03), which is superior to the MLP model. In general, the pixel-based RNN models performed better than the patch-based models especially in regions with a larger proportion of outliers. And the prediction accuracy is stable over different vegetation types and growing seasons.

ACS Style

Wentao Yu; Jing Li; Qinhuo Liu; Jing Zhao; Yadong Dong; Cong Wang; Shangrong Lin; Xinran Zhu; Hu Zhang. Spatial-Temporal Prediction of Vegetation Index With Deep Recurrent Neural Networks. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.

AMA Style

Wentao Yu, Jing Li, Qinhuo Liu, Jing Zhao, Yadong Dong, Cong Wang, Shangrong Lin, Xinran Zhu, Hu Zhang. Spatial-Temporal Prediction of Vegetation Index With Deep Recurrent Neural Networks. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.

Chicago/Turabian Style

Wentao Yu; Jing Li; Qinhuo Liu; Jing Zhao; Yadong Dong; Cong Wang; Shangrong Lin; Xinran Zhu; Hu Zhang. 2021. "Spatial-Temporal Prediction of Vegetation Index With Deep Recurrent Neural Networks." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Journal article
Published: 29 January 2021 in Remote Sensing
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High-quality Normalized Difference Vegetation Index (NDVI) time series are essential in studying vegetation phenology, dynamic monitoring, and global change. Gap filling is the most important issue in reconstructing NDVI time series from satellites with high spatial resolution, e.g., the Landsat series and Chinese GaoFen-1/6 series. Due to the sparse revisit frequencies of high-resolution satellites, traditional reconstruction approaches face the challenge of dealing with large gaps in raw NDVI time series data. In this paper, a climate incorporated gap-filling (CGF) method is proposed for the reconstruction of Landsat historical NDVI time series data. The CGF model considers the relationship of the NDVI time series and climate conditions between two adjacent years. Climate variables, including downward solar shortwave radiation, precipitation, and temperature, are used to characterize the constrain factors of vegetation growth. Radial basis function networks (RBFNs) are used to link the NDVI time series between two adjacent years with variabilities in climatic conditions. An RBFN predicted a background NDVI time series in the target year, and the observed NDVI values in this year were used to adjust the predicted NDVI time series. Finally, the NDVI time series were recursively reconstructed from 2018 to 1986. The experiments were performed in a heterogeneous region in the Qilian Mountains. The results demonstrate that the proposed method can accurately reconstruct and generate continuous 30 m 8-day NDVI time series using Landsat observations. The CGF method outperforms traditional time series reconstruction methods (e.g., the harmonic analysis of time series (HANTS) and Savitzky–Golay (SG) filter methods) when the raw time series is contaminated with large gaps, which widely exist in Landsat images.

ACS Style

Wentao Yu; Jing Li; Qinhuo Liu; Jing Zhao; Yadong Dong; Xinran Zhu; Shangrong Lin; Hu Zhang; Zhaoxing Zhang. Gap Filling for Historical Landsat Ndvi Time Series by Integrating Climate Data. Remote Sensing 2021, 13, 484 .

AMA Style

Wentao Yu, Jing Li, Qinhuo Liu, Jing Zhao, Yadong Dong, Xinran Zhu, Shangrong Lin, Hu Zhang, Zhaoxing Zhang. Gap Filling for Historical Landsat Ndvi Time Series by Integrating Climate Data. Remote Sensing. 2021; 13 (3):484.

Chicago/Turabian Style

Wentao Yu; Jing Li; Qinhuo Liu; Jing Zhao; Yadong Dong; Xinran Zhu; Shangrong Lin; Hu Zhang; Zhaoxing Zhang. 2021. "Gap Filling for Historical Landsat Ndvi Time Series by Integrating Climate Data." Remote Sensing 13, no. 3: 484.

Letter
Published: 16 July 2020 in Remote Sensing
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The normalization of topographic effects on vegetation indices (VIs) is a prerequisite for their proper use in mountainous areas. We assessed the topographic effects on the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the soil adjusted vegetation index (SAVI), and the near-infrared reflectance of terrestrial vegetation (NIRv) calculated from Sentinel-2. The evaluation was based on two criteria: the correlation with local illumination condition and the dependence on aspect. Results show that topographic effects can be neglected for the NDVI, while they heavily influence the SAVI, EVI, and NIRv: the local illumination condition explains 19.85%, 25.37%, and 26.69% of the variation of the SAVI, EVI, and NIRv, respectively, and the coefficients of variation across different aspects are, respectively, 8.13%, 10.46%, and 14.07%. We demonstrated the applicability of existing correction methods, including statistical-empirical (SE), sun-canopy-sensor with C-correction (SCS + C), and path length correction (PLC), dedicatedly designed for reflectance, to normalize topographic effects on VIs. Our study will benefit vegetation monitoring with VIs over mountainous areas.

ACS Style

Rui Chen; Gaofei Yin; Guoxiang Liu; Jing Li; Aleixandre Verger. Evaluation and Normalization of Topographic Effects on Vegetation Indices. Remote Sensing 2020, 12, 2290 .

AMA Style

Rui Chen, Gaofei Yin, Guoxiang Liu, Jing Li, Aleixandre Verger. Evaluation and Normalization of Topographic Effects on Vegetation Indices. Remote Sensing. 2020; 12 (14):2290.

Chicago/Turabian Style

Rui Chen; Gaofei Yin; Guoxiang Liu; Jing Li; Aleixandre Verger. 2020. "Evaluation and Normalization of Topographic Effects on Vegetation Indices." Remote Sensing 12, no. 14: 2290.

Journal article
Published: 19 June 2020 in IEEE Geoscience and Remote Sensing Letters
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Topographic and angular corrections on Sentinel-2 imagery are crucial for the generation of consistent surface reflectance. We propose a novel topographic-angular integrated normalization approach based on the combination of the path length correction (PLC) and C-factor approaches. The PLC-C normalization approach is a semiphysical method with limited use of auxiliary data: only a digital elevation model and a fixed set of kernel coefficients, ensuring its transferability for operational implementation. For the validation, we used two Sentinel-2A images over a mountainous area observed in backward (BS) and forward scattering (FS) directions from laterally adjacent orbit swaths. PLC-C significantly reduced both the topographic and directional anisotropy effects: the overlapping ratio between BS and FS observations was increased from 84.1% to 92.8% for the near-infrared band, and from 81.0% to 93.1% for the red band; the coefficient of variation of the reflectances across different aspects, which was used as a criterion of topographic effects, was reduced from 9.8% / 12.2% to 3.6%/5.7% in BS/FS direction for the near-infrared band, and from 8.1%/9.7% to 4.5%/4.2% for the red band. PLC-C will contribute to the generation of analysis ready data from Sentinel-2 top of canopy reflectance.

ACS Style

Gaofei Yin; Jing Li; Baodong Xu; Yelu Zeng; Shengbiao Wu; Kai Yan; Aleixandre Verger; Guoxiang Liu. PLC-C: An Integrated Method for Sentinel-2 Topographic and Angular Normalization. IEEE Geoscience and Remote Sensing Letters 2020, 18, 1446 -1450.

AMA Style

Gaofei Yin, Jing Li, Baodong Xu, Yelu Zeng, Shengbiao Wu, Kai Yan, Aleixandre Verger, Guoxiang Liu. PLC-C: An Integrated Method for Sentinel-2 Topographic and Angular Normalization. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (8):1446-1450.

Chicago/Turabian Style

Gaofei Yin; Jing Li; Baodong Xu; Yelu Zeng; Shengbiao Wu; Kai Yan; Aleixandre Verger; Guoxiang Liu. 2020. "PLC-C: An Integrated Method for Sentinel-2 Topographic and Angular Normalization." IEEE Geoscience and Remote Sensing Letters 18, no. 8: 1446-1450.

Journal article
Published: 03 April 2020 in International Journal of Applied Earth Observation and Geoinformation
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Gap probability theory provides a theoretical equation to calculate fractional vegetation cover (FVC). However, the main algorithms used in present FVC products generation are still the linear mixture model and machine learning methods. The reason to limit the gap probability theory applied in the product algorithm is the availability and accuracy of leaf area index (LAI) and clumping index (CI) products. With the improvement of the LAI and CI products, it is necessary to assess whether the algorithm based on gap probability theory using the present products can improve the accuracy of FVC products. In this study, we generated the FVC estimates based on the gap probability theory (FVCgap) with a resolution of 500 m every 8 days for Europe. FVCgap estimates were validated with field FVC measurements of ImagineS from 2013 to 2015 for crop types. Two existing FVC products, Geoland2 Version1 (GEOV1) and Multisource data Synergized Quantitative remote sensing production system (MuSyQ), were used to inter-compare with the FVCgap estimates. FVCgap estimates showed a better agreement with field FVC measurements, with lowest root mean square error (RMSE) (0.1211) and bias (0.0224), than GEOV1 and MuSyQ FVC products. The inter-annual and seasonal variations of FVCgap estimates were also showed the most consistent with field measurements.

ACS Style

Jing Zhao; Jing Li; Qinhuo Liu; Baodong Xu; Wentao Yu; Shangrong Lin; Zhang Hu. Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory. International Journal of Applied Earth Observation and Geoinformation 2020, 90, 102112 .

AMA Style

Jing Zhao, Jing Li, Qinhuo Liu, Baodong Xu, Wentao Yu, Shangrong Lin, Zhang Hu. Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory. International Journal of Applied Earth Observation and Geoinformation. 2020; 90 ():102112.

Chicago/Turabian Style

Jing Zhao; Jing Li; Qinhuo Liu; Baodong Xu; Wentao Yu; Shangrong Lin; Zhang Hu. 2020. "Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory." International Journal of Applied Earth Observation and Geoinformation 90, no. : 102112.

Journal article
Published: 06 March 2020 in Remote Sensing of Environment
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Solar Induced chlorophyll Fluorescence (SIF) shows promise as an approach for estimating gross primary production (GPP) remotely. However, sun-target-sensor geometry and within-canopy absorption of SIF can alter the relationship between measured SIF and GPP, because sensors can only retrieve some unknown fraction of the total emitted SIF. Radiative transfer models that allow for variation in canopy structure and sensor angles are therefore needed to properly interpret SIF measurements. Spectral invariants allow decoupling of the wavelength-independent canopy structure and the wavelength-dependent leaf and soil spectrum in the radiative transfer process. Here we develop a simple analytical Fluorescence Radiative Transfer model based on Escape and Recollision probability (FluorRTER) to investigate the impact of canopy structure and sun-target-sensor geometry on SIF emissions. SIF simulations using the FluorRTER model agreed well the one-dimensional Soil-Canopy Observation of Photochemistry and Energy balance (SCOPE) model and the three-dimensional Fluorescence model with Weighted Photon Spread (FluorWPS) model. The fractional vegetation cover (FVC) and clumping effect have a large influence the SIF emission of 3D discontinuous canopies. For a moderate solar zenith angle (30°) and a clumped canopy (FVC = 0.6), the difference between the directional observed SIF of a 3D discontinuous canopy and a 1D homogeneous canopy was as large as 43.2% and 38.4% for Photosystem I + II fluorescence at 685 nm and at 740 nm, respectively. By bridging the gap between observed SIF and total emitted SIF over 3D heterogeneous vegetation canopies, the FluorRTER model can assist with the angular normalization of SIF measurements and enable the more robust interpretation of how variations in SIF from directional and hemispherical in-situ, airborne and satellite observations relate to leaf and whole-canopy physiological processes.

ACS Style

Yelu Zeng; Grayson Badgley; Min Chen; Jing Li; Leander D.L. Anderegg; Ari Kornfeld; Qinhuo Liu; Baodong Xu; Bin Yang; Kai Yan; Joseph A. Berry. A radiative transfer model for solar induced fluorescence using spectral invariants theory. Remote Sensing of Environment 2020, 240, 111678 .

AMA Style

Yelu Zeng, Grayson Badgley, Min Chen, Jing Li, Leander D.L. Anderegg, Ari Kornfeld, Qinhuo Liu, Baodong Xu, Bin Yang, Kai Yan, Joseph A. Berry. A radiative transfer model for solar induced fluorescence using spectral invariants theory. Remote Sensing of Environment. 2020; 240 ():111678.

Chicago/Turabian Style

Yelu Zeng; Grayson Badgley; Min Chen; Jing Li; Leander D.L. Anderegg; Ari Kornfeld; Qinhuo Liu; Baodong Xu; Bin Yang; Kai Yan; Joseph A. Berry. 2020. "A radiative transfer model for solar induced fluorescence using spectral invariants theory." Remote Sensing of Environment 240, no. : 111678.

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: 09 February 2020 in Remote Sensing of Environment
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Land cover mixture at moderate- to coarse-resolution is an important cause for the uncertainty of global leaf area index (LAI) products. The accuracy of LAI retrievals over land-water mixed pixels is adversely impacted because water absorbs considerable solar radiation and thus can greatly lower pixel-level reflectance especially in the near-infrared wavelength. Here we proposed an approach named Reduced Water Effect (RWE) to improve the accuracy of LAI retrievals by accounting for water-induced negative bias in reflectances. The RWE consists of three parts: water area fraction (WAF) calculation, subpixel water reflectance computation in land-water mixed pixels and LAI retrieval using the operational MODIS LAI algorithm. The performance of RWE was carefully evaluated using the aggregated Landsat ETM+ reflectance of water pixels over different regions and observation dates and the aggregated 30-m LAI reference maps over three sites in the moderate-resolution pixel grid (500-m). Our results suggest that the mean absolute errors of water endmember reflectance in red and NIR bands were both <0.016, which only introduced mean absolute (relative) errors of <0.15 (15%) for the pixel-level LAI retrievals. The validation results reveal that the accuracy of RWE LAI was higher than that of MODIS LAI over land-water mixed pixels especially for pixels with larger WAFs. Additionally, the mean relative difference between RWE LAI and aggregated 30-m LAI did not vary with WAF, indicating that water effects were significantly reduced by the RWE method. A comparison between RWE and MODIS LAI shows that the maximum absolute and relative differences caused by water effects were 0.9 and 100%, respectively. Furthermore, the impact of water mixed in pixels can induce the LAI underestimation and change the day selected for compositing the 8-day LAI product. These results indicate that RWE can effectively reduce water effects on the LAI retrieval of land-water mixed pixels, which is promising for the improvement of global LAI products.

ACS Style

Baodong Xu; Jing Li; Taejin Park; Qinhuo Liu; Yelu Zeng; Gaofei Yin; Kai Yan; Chi Chen; Jing Zhao; Weiliang Fan; Yuri Knyazikhin; Ranga B. Myneni. Improving leaf area index retrieval over heterogeneous surface mixed with water. Remote Sensing of Environment 2020, 240, 111700 .

AMA Style

Baodong Xu, Jing Li, Taejin Park, Qinhuo Liu, Yelu Zeng, Gaofei Yin, Kai Yan, Chi Chen, Jing Zhao, Weiliang Fan, Yuri Knyazikhin, Ranga B. Myneni. Improving leaf area index retrieval over heterogeneous surface mixed with water. Remote Sensing of Environment. 2020; 240 ():111700.

Chicago/Turabian Style

Baodong Xu; Jing Li; Taejin Park; Qinhuo Liu; Yelu Zeng; Gaofei Yin; Kai Yan; Chi Chen; Jing Zhao; Weiliang Fan; Yuri Knyazikhin; Ranga B. Myneni. 2020. "Improving leaf area index retrieval over heterogeneous surface mixed with water." Remote Sensing of Environment 240, no. : 111700.

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: 31 May 2019 in Remote Sensing
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Gross primary productivity (GPP) is the most important component of terrestrial carbon flux. Red-edge (680–780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The European Space Agency (ESA) Sentinel-2A and B satellites provide red-edge bands at 20-m spatial resolution on a five-day revisit period, which can be used for global estimation of GPP. Previous studies focused mostly on improving cropland GPP estimation using red-edge bands. In this study, we firstly evaluated the relationship between eight vegetation indices (VIs) retrieved from Sentinel-2 imagery in association with incident photosynthetic active radiation (PARin) and carbon flux tower GPP (GPPEC) across three forest and two grassland sites in Australia. We derived a time series of five red-edge VIs and three non-red-edge VIs over the CO2 flux tower footprints at 16-day time intervals and compared both temporal and spatial variations. The results showed that the relationship between the red-edge index (CIr, ρ 783 ρ 705 − 1 ) multiplied by PARin and GPPEC had the highest correlation (R2 = 0.77, root-mean-square error (RMSE) = 0.81 gC∙m−2∙day−1) at the two grassland sites. The CIr also showed consistency (rRMSE defined as RMSE/mean GPP, lower than 0.25) across forest and grassland sites. The high spatial resolution of the Sentinel-2 data provided more detailed information to adequately characterize the GPP variance at spatially heterogeneous areas. The high revisit period of Sentinel-2 exhibited temporal variance in GPP at the grassland sites; however, at forest sites, the flux-tower-based GPP variance could not be fully tracked by the limited satellite images. These results suggest that the high-spatial-resolution red-edge index from Sentinel-2 can improve large-scale spatio-temporal GPP assessments.

ACS Style

Shangrong Lin; Jing Li; Qinhuo Liu; Longhui Li; Jing Zhao; Wentao Yu. Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity. Remote Sensing 2019, 11, 1303 .

AMA Style

Shangrong Lin, Jing Li, Qinhuo Liu, Longhui Li, Jing Zhao, Wentao Yu. Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity. Remote Sensing. 2019; 11 (11):1303.

Chicago/Turabian Style

Shangrong Lin; Jing Li; Qinhuo Liu; Longhui Li; Jing Zhao; Wentao Yu. 2019. "Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity." Remote Sensing 11, no. 11: 1303.

Research letter
Published: 21 May 2019 in Geophysical Research Letters
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Accurate phenological characterization of dryland ecosystems has remained a challenge due to the complex composition of plant functional types, each having distinct phenological dynamics, sensitivity to climate and disturbance. Solar‐induced chlorophyll fluorescence (SIF), a proxy for photosynthesis, offers potential to alleviate such challenge. We here explore this potential using dryland systems along the North Australian Tropical Transect (NATT) with SIF derived from Orbiting Carbon Observatory‐2. SIF identified the seasonal onset and senescence of Gross Primary Production at eddy covariance sites with improved accuracy over Enhanced Vegetation Index (EVI) and Near Infrared Reflectance of terrestrial Vegetation (NIRv) from Moderate Resolution Imaging Spectroradiometer, especially at inland xeric shrublands. At regional scale, SIF depicted both earlier onset and senescence across NATT. We hypothesized that SIF outperformed EVI and NIRv mainly because, unlike reflectance, it is not contaminated by background soil and its total signal is contributed by mixed plant species in additive way.

ACS Style

Cong Wang; Jason Beringer; Lindsay B. Hutley; James Cleverly; Jing Li; Qinhuo Liu; Ying Sun. Phenology Dynamics of Dryland Ecosystems Along the North Australian Tropical Transect Revealed by Satellite Solar‐Induced Chlorophyll Fluorescence. Geophysical Research Letters 2019, 46, 5294 -5302.

AMA Style

Cong Wang, Jason Beringer, Lindsay B. Hutley, James Cleverly, Jing Li, Qinhuo Liu, Ying Sun. Phenology Dynamics of Dryland Ecosystems Along the North Australian Tropical Transect Revealed by Satellite Solar‐Induced Chlorophyll Fluorescence. Geophysical Research Letters. 2019; 46 (10):5294-5302.

Chicago/Turabian Style

Cong Wang; Jason Beringer; Lindsay B. Hutley; James Cleverly; Jing Li; Qinhuo Liu; Ying Sun. 2019. "Phenology Dynamics of Dryland Ecosystems Along the North Australian Tropical Transect Revealed by Satellite Solar‐Induced Chlorophyll Fluorescence." Geophysical Research Letters 46, no. 10: 5294-5302.

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: 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: 21 August 2018 in Remote Sensing
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Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structures with vertical stratifications that influence the carbon–water–energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature and VPD play important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from −50% to 30% because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1–2 gC/m2/day with uncertainties of approximately −30% to 20% because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with 10% less bias in DHS and more than 70% less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests.

ACS Style

Shangrong Lin; Jing Li; Qinhuo Liu; Alfredo Huete; Longhui Li. Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing. Remote Sensing 2018, 10, 1329 .

AMA Style

Shangrong Lin, Jing Li, Qinhuo Liu, Alfredo Huete, Longhui Li. Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing. Remote Sensing. 2018; 10 (9):1329.

Chicago/Turabian Style

Shangrong Lin; Jing Li; Qinhuo Liu; Alfredo Huete; Longhui Li. 2018. "Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing." Remote Sensing 10, no. 9: 1329.

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.

Journal article
Published: 01 February 2018 in Forests
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The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) algorithm has been successfully implemented for Visible Infrared Imager Radiometer Suite (VIIRS) observations by optimizing a small set of configurable parameters in Look-Up-Tables (LUTs). Our preliminary evaluation showed reasonable agreement between VIIRS and MODIS LAI/FPAR retrievals. However, there is a need for a more comprehensive investigation to assure continuity of multi-sensor global LAI/FPAR time series, as the preliminary evaluation was spatiotemporally limited. In this study, we use a multi-year (2012–2016) global LAI/FPAR product generated from VIIRS and MODIS to evaluate for spatiotemporal consistency. We also quantify uncertainty of the product by utilizing available ground measurements. For both consistency and uncertainty evaluation, we account for variations in biome type and temporal resolution. Our results indicate that the LAI/FPAR retrievals from VIIRS and MODIS are consistent at different spatial (i.e., global and site) and temporal (i.e., 8-day, seasonal and annual) scales. The estimate of mean discrepancy (−0.006 ± 0.013 for LAI and −0.002 ± 0.002 for FPAR) meets the stability requirement for long-term LAI/FPAR Earth System Data Records (ESDRs) from multi-sensors as suggested by the Global Climate Observing System (GCOS). It is noteworthy that the rate of retrievals from the radiative transfer-based main algorithm is also comparable between two sensors. However, a relatively larger discrepancy over tropical forests was observed due to reflectance saturation and an unexpected interannual variation of main algorithm success was noticed due to instability in input surface reflectances. The uncertainties/relative uncertainties of VIIRS and MODIS LAI (FPAR) products assessed through comparisons to ground measurements are estimated to be 0.60/42.2% (0.10/24.4%) and 0.55/39.3% (0.11/26%), respectively. Note that the validated LAI were only distributed in low domains (~2.5), resulting in large relative uncertainty. Therefore, more ground measurements are needed to achieve a more comprehensive evaluation result of product uncertainty. The results presented here generally imbue confidence in the consistency between VIIRS and MODIS LAI/FPAR products and the feasibility of generating long-term multi-sensor LAI/FPAR ESDRs time series.

ACS Style

Baodong Xu; Taejin Park; Kai Yan; Chi Chen; Yelu Zeng; Wanjuan Song; Gaofei Yin; Jing Li; Qinhuo Liu; Yuri Knyazikhin; Ranga B. Myneni. Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016. Forests 2018, 9, 73 .

AMA Style

Baodong Xu, Taejin Park, Kai Yan, Chi Chen, Yelu Zeng, Wanjuan Song, Gaofei Yin, Jing Li, Qinhuo Liu, Yuri Knyazikhin, Ranga B. Myneni. Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016. Forests. 2018; 9 (2):73.

Chicago/Turabian Style

Baodong Xu; Taejin Park; Kai Yan; Chi Chen; Yelu Zeng; Wanjuan Song; Gaofei Yin; Jing Li; Qinhuo Liu; Yuri Knyazikhin; Ranga B. Myneni. 2018. "Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016." Forests 9, no. 2: 73.