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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.
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 StyleWeiliang 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 StyleWeiliang 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.
Gaofei Yin; Ainong Li; Huaan Jin; Wei Zhao; Jinhu Bian; Yonghua Qu; Yelu Zeng; Baodong Xu. Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO. Agricultural and Forest Meteorology 2017, 233, 209 -221.
AMA StyleGaofei Yin, Ainong Li, Huaan Jin, Wei Zhao, Jinhu Bian, Yonghua Qu, Yelu Zeng, Baodong Xu. Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO. Agricultural and Forest Meteorology. 2017; 233 ():209-221.
Chicago/Turabian StyleGaofei Yin; Ainong Li; Huaan Jin; Wei Zhao; Jinhu Bian; Yonghua Qu; Yelu Zeng; Baodong Xu. 2017. "Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO." Agricultural and Forest Meteorology 233, no. : 209-221.
Current bidirectional reflectance distribution function (BRDF) inversions using ordinary least squares (OLS) criterion can be easily contaminated by observations with residual cloud and undetected high aerosols, which leads to abrupt fluctuations in the normalized difference vegetation index (NDVI) time series. The OLS criterion assumes the noise has Gaussian distribution, which is often violated due to positive noise biases caused by clouds and high aerosols. A changing-weight iterative BRDF/NDVI inversion algorithm (CWI) based on a posteriori variance estimation of observation errors is presented to explicitly consider the asymmetrically distributed noise and observations with unequal accuracy in the BRDF retrieval. CWI employs a posteriori variance estimation and an NDVI-based indicator to iteratively adjust the weight of each observation according to its noise level. The validation results suggest CWI performs better than the Li-Gao and OLS approaches. The rmse was reduced from 0.074 to 0.028, and the relative error decreased from 13.4% to 3.8% at the U.S. Department of Agriculture Beltsville Agricultural Research Center site. Similarly, at the Harvard Forest site, the rmse was reduced from 0.086 to 0.031, and the relative error decreased from 9.5% to 2.7%. The average noise and relative noise of the CWI NDVI time series over ten EOS Land Validation Core Sites from 2003-2009 was smaller (0.028, 3.7%) than those of MOD13A2 (0.041, 5.2%), MYD13A2 (0.039, 4.9%) and MCD43B4 (0.030, 4.4%). The results demonstrate the robustness of the CWI approach in suppressing the influence of contaminated observations in BRDF retrievals by producing results that are less affected by undetected clouds and high aerosols.
Yelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Jing Zhao; Le Yang; Weiliang Fan; Shengbiao Wu; Kai Yan. An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors. IEEE Transactions on Geoscience and Remote Sensing 2016, 54, 6481 -6496.
AMA StyleYelu Zeng, Jing Li, Qinhuo Liu, Alfredo R. Huete, Baodong Xu, Gaofei Yin, Jing Zhao, Le Yang, Weiliang Fan, Shengbiao Wu, Kai Yan. An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors. IEEE Transactions on Geoscience and Remote Sensing. 2016; 54 (11):6481-6496.
Chicago/Turabian StyleYelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Jing Zhao; Le Yang; Weiliang Fan; Shengbiao Wu; Kai Yan. 2016. "An Iterative BRDF/NDVI Inversion Algorithm Based on A Posteriori Variance Estimation of Observation Errors." IEEE Transactions on Geoscience and Remote Sensing 54, no. 11: 6481-6496.
Continuous leaf area index (LAI) observations from global ground stations are an important reference dataset for the validation of remotely sensed LAI products. In this study, a pragmatic approach is presented for evaluating the spatial representativeness of station-observed LAI dataset in the product pixel grid. Three evaluation indicators, including dominant vegetation type percent (DVTP), relative absolute error (RAE) and coefficient of sill (CS), were established to quantify different levels of spatial representativeness. The DVTP was used to evaluate whether the station-observed vegetation type was the dominant one in the pixel grid, and the RAE and CS were applied to quantify the point-to-area consistency for a given station observation and the spatial heterogeneity caused by the different density of vegetation within the pixel, respectively. The proposed approach was applied to 25 stations from the Chinese Ecosystem Research Network, and results show significant differences of representativeness errors at different levels. The spatial representativeness for different stations varied seasonally with different vegetation growth stages due to temporal changes in heterogeneity, but the spatial representativeness remained consistent at interannual timeframes due to the relatively stable vegetation structure and pattern between adjacent years. A large error can occur in MOD15A2 product validation when the representativeness level of station LAI observations is low. This approach can effectively distinguish various levels of spatial representativeness for the station-observed LAI dataset at the pixel grid scale, which can consequently improve the reliability of LAI product validation by selecting LAI observations with a high degree of representativeness.
Baodong Xu; Jing Li; Qinhuo Liu; Alfredo Huete; Qiang Yu; Yelu Zeng; Gaofei Yin; Jing Zhao; Le Yang. Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2016, 9, 3267 -3282.
AMA StyleBaodong Xu, Jing Li, Qinhuo Liu, Alfredo Huete, Qiang Yu, Yelu Zeng, Gaofei Yin, Jing Zhao, Le Yang. Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2016; 9 (7):3267-3282.
Chicago/Turabian StyleBaodong Xu; Jing Li; Qinhuo Liu; Alfredo Huete; Qiang Yu; Yelu Zeng; Gaofei Yin; Jing Zhao; Le Yang. 2016. "Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 7: 3267-3282.
Spatio-temporally continuous leaf area index (LAI) is required for surface process simulation, climate modelling and global change study. As a result of cloud contamination and other factors, the current LAI products are spatially and temporally discontinuous. A multi-sensor integration method was proposed in this paper to combine Terra-Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua-MODIS, FY (FengYun) 3A-MEdium Resolution Spectrum Imager (MERSI) and FY3B-MERSI data to improve LAI spatio-temporal continuity. It consists of a normalization algorithm to eliminate the difference between MODIS and MERSI data in spatial and spectral aspects, a daily LAI retrieval algorithm based on neural networks and a maximum value compositing algorithm. The feasibility of our LAI retrieval method to improve continuity was assessed at national scale (in China). Results show that (1) the combination of multi-sensor data can significantly improve LAI temporal continuity, especially for mountainous regions which are characterized by high frequency of cloud coverage; (2) the improvement in spatial continuity is obvious as can be seen from the increase of retrieval ratio, defined as the ratio of the number of retrieved pixels to the total number of pixels, from 0.78 for GEOV1 LAI product, and 0.88 for MOD15A2 LAI product to 0.98 for multi-sensor LAI product.
Gaofei Yin; Jing Li; Qinhuo Liu; Bo Zhong; Ainong Li. Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data. Remote Sensing Letters 2016, 7, 771 -780.
AMA StyleGaofei Yin, Jing Li, Qinhuo Liu, Bo Zhong, Ainong Li. Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data. Remote Sensing Letters. 2016; 7 (8):771-780.
Chicago/Turabian StyleGaofei Yin; Jing Li; Qinhuo Liu; Bo Zhong; Ainong Li. 2016. "Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data." Remote Sensing Letters 7, no. 8: 771-780.
Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model’s potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.
Gaofei Yin; Jing Li; Qinhuo Liu; Weiliang Fan; Baodong Xu; Yelu Zeng; Jing Zhao. Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection. Remote Sensing 2015, 7, 4604 -4625.
AMA StyleGaofei Yin, Jing Li, Qinhuo Liu, Weiliang Fan, Baodong Xu, Yelu Zeng, Jing Zhao. Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection. Remote Sensing. 2015; 7 (4):4604-4625.
Chicago/Turabian StyleGaofei Yin; Jing Li; Qinhuo Liu; Weiliang Fan; Baodong Xu; Yelu Zeng; Jing Zhao. 2015. "Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection." Remote Sensing 7, no. 4: 4604-4625.
The development of efficient and systematic groundbased spatial sampling strategies is critical for the validation of medium-resolution satellite-derived leaf area index (LAI) products, particularly over heterogeneous land surfaces. In this paper, a new sampling strategy based on high-resolution vegetation index prior knowledge (SSVIP) is proposed to generate accurate LAI reference maps over heterogeneous areas. To capture the variability across a site, the SSVIP is designed to 1) stratify the nonhomogeneous area into zones with minimum within-class variance; 2) assign the number of samples to each zone using Neyman optimal allocation; and 3) determine the spatial distribution of samples with a nearest neighbor index. The efficiency of the proposed method was examined using different vegetation types and pixel heterogeneities. The results indicate that the SSVIP approach can properly divide a heterogeneous area into different vegetation cover zones. Whereas the LAI reference maps generated by SSVIP attain the target accuracy of 0.1 LAI units in cropland and broadleaf forest sites, the current sampling strategy based on vegetation type has a root mean square error (RMSE) of 0.14 for the same number of samples. SSVIP was compared with the current sampling strategy at 24 VALERI sites, and the results suggested that samples selected by SSVIP were more representative in the feature space and geographical space, which further indicated the reasonable validation over heterogeneous land surfaces.
Yelu Zeng; Jing Li; Qinhuo Liu; Longhui Li; Baodong Xu; Gaofei Yin; Jingjing Peng. A Sampling Strategy for Remotely Sensed LAI Product Validation Over Heterogeneous Land Surfaces. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014, 7, 3128 -3142.
AMA StyleYelu Zeng, Jing Li, Qinhuo Liu, Longhui Li, Baodong Xu, Gaofei Yin, Jingjing Peng. A Sampling Strategy for Remotely Sensed LAI Product Validation Over Heterogeneous Land Surfaces. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2014; 7 (7):3128-3142.
Chicago/Turabian StyleYelu Zeng; Jing Li; Qinhuo Liu; Longhui Li; Baodong Xu; Gaofei Yin; Jingjing Peng. 2014. "A Sampling Strategy for Remotely Sensed LAI Product Validation Over Heterogeneous Land Surfaces." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 7: 3128-3142.