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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.
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 StyleXinran 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 StyleXinran 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.
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.
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 StyleWentao 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 StyleWentao 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.
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.
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 StyleWentao 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 StyleWentao 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.
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.
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 StyleQiong 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 StyleQiong 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.
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.
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 StyleWentao 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 StyleWentao 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.
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.
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 StyleShangrong 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 StyleShangrong 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.
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.
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 StyleWentao 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 StyleWentao 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.
In recent years, China has developed and launched several satellites with high spatial resolutions, such as the resources satellite No. 3 (ZY-3) with a multi-spectral camera (MUX) and 5.8 m spatial resolution, the satellite GaoFen No. 1 (GF-1) with a wide field of view (WFV) camera and 16 m spatial resolution, and the environment satellite (HJ-1A/B) with a charge-coupled device (CCD) sensor and 30 m spatial resolution. First, to analyze the potential application of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD to extract the leaf area index (LAI) at the regional scale, this study estimated LAI from the relationships between physical model-based spectral vegetation indices (SVIs) and LAI values that were generated from look-up tables (LUTs), simulated from the combination of the PROSPECT-5B leaf model and the scattering by arbitrarily inclined leaves with the hot-spot effect (SAILH) canopy reflectance model. Second, to assess the surface reflectance quality of these sensors after data preprocessing, the well-processed surface reflectance products of the Landsat-8 operational land imager (OLI) sensor with a convincing data quality were used to compare the performances of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD sensors both in theory and reality. Apart from several reflectance fluctuations, the reflectance trends were coincident, and the reflectance values of the red and near-infrared (NIR) bands were comparable among these sensors. Finally, to analyze the accuracy of the LAI estimated from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD, the LAI estimations from these sensors were validated based on LAI field measurements in Huailai, Hebei Province, China. The results showed that the performance of the LAI that was inversed from ZY-3 MUX was better than that from GF-1 WFV, and HJ-1 CCD, both of which tended to be systematically underestimated. In addition, the value ranges and accuracies of the LAI inversions both decreased with decreasing spatial resolution.
Jing Zhao; Jing Li; Qinhuo Liu; Hongyan Wang; Chen Chen; Baodong Xu; Shanlong Wu. Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize. Remote Sensing 2018, 10, 68 .
AMA StyleJing Zhao, Jing Li, Qinhuo Liu, Hongyan Wang, Chen Chen, Baodong Xu, Shanlong Wu. Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize. Remote Sensing. 2018; 10 (2):68.
Chicago/Turabian StyleJing Zhao; Jing Li; Qinhuo Liu; Hongyan Wang; Chen Chen; Baodong Xu; Shanlong Wu. 2018. "Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize." Remote Sensing 10, no. 2: 68.
The primary restriction on high resolution remote sensing data is the limit observation frequency. Using a network of multiple sensors is an efficient approach to increase the observations in a specific period. This study explores a leaf area index (LAI) inversion method based on a 30 m multi-sensor dataset generated from HJ1/CCD and Landsat8/OLI, from June to August 2013 in the middle reach of the Heihe River Basin, China. The characteristics of the multi-sensor dataset, including the percentage of valid observations, the distribution of observation angles and the variation between different sensor observations, were analyzed. To reduce the possible discrepancy between different satellite sensors on LAI inversion, a quality control system for the observations was designed. LAI is retrieved from the high quality of single-sensor observations based on a look-up table constructed by a unified model. The averaged LAI inversion over a 10-day period is set as the synthetic LAI value. The percentage of valid LAI inversions increases significantly from 6.4% to 49.7% for single-sensors to 75.9% for multi-sensors. LAI retrieved from the multi-sensor dataset show good agreement with the field measurements. The correlation coefficient (R2) is 0.90, and the average root mean square error (RMSE) is 0.42. The network of multiple sensors with 30 m spatial resolution can generate LAI products with reasonable accuracy and meaningful temporal resolution.
Jing Zhao; Jing Li; Qinhuo Liu; Wenjie Fan; Bo Zhong; Shanlong Wu; Le Yang; Yelu Zeng; Baodong Xu; Gaofei Yin. Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China. Remote Sensing 2015, 7, 6862 -6885.
AMA StyleJing Zhao, Jing Li, Qinhuo Liu, Wenjie Fan, Bo Zhong, Shanlong Wu, Le Yang, Yelu Zeng, Baodong Xu, Gaofei Yin. Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China. Remote Sensing. 2015; 7 (6):6862-6885.
Chicago/Turabian StyleJing Zhao; Jing Li; Qinhuo Liu; Wenjie Fan; Bo Zhong; Shanlong Wu; Le Yang; Yelu Zeng; Baodong Xu; Gaofei Yin. 2015. "Leaf Area Index Retrieval Combining HJ1/CCD and Landsat8/OLI Data in the Heihe River Basin, China." Remote Sensing 7, no. 6: 6862-6885.
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.
A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer scale is an important step in the validation process for moderate-resolution leaf area index (LAI) products. Current LAI-sampling strategies are unable to consider the vegetation seasonal changes and are better suited for single-day LAI product validation, whereas the increasingly used wireless sensor network for LAI measurement (LAINet) requires an optimal sampling strategy across both spatial and temporal scales. In this study, we developed an efficient and robust LAI Sampling strategy based on Multi-temporal Prior knowledge (SMP) for long-term, fixed-position LAI observations. The SMP approach employed multi-temporal vegetation index (VI) maps and the vegetation classification map as a priori knowledge. The SMP approach minimized the multi-temporal bias of the VI frequency histogram between the ESUs and the entire site and maximized the nearest-neighbor index to ensure that ESUs were dispersed in the geographical space. The SMP approach was compared with four sampling strategies including random sampling, systematic sampling, sampling based on the land-cover map and a sampling strategy based on vegetation index prior knowledge using the PROSAIL model-based simulation analysis in the Heihe River basin. The results indicate that the ESUs selected using the SMP method spread more evenly in both the multi-temporal feature space and geographical space over the vegetation cycle. By considering the temporal changes in heterogeneity, the average root-mean-square error (RMSE) of the LAI reference maps can be reduced from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%. The SMP technique was applied to assign the LAINet ESU locations at the Huailai Remote Sensing Experimental Station in Beijing, China, from 4 July to 28 August 2013, to validate three MODIS C5 LAI products. The results suggest that the average R2, RMSE, bias and relative uncertainty for the three MODIS LAI products were 0.60, 0.33, −0.11, and 12.2%, respectively. The MCD15A2 product performed best, exhibiting a RMSE of 0.20, a bias of −0.07 and a relative uncertainty of 7.4%. Future efforts are needed to obtain more long-term validation datasets using the SMP approach on different vegetation types for validating moderate-resolution LAI products in time series.
Yelu Zeng; Jing Li; Qinhuo Liu; Yonghua Qu; Alfredo R. Huete; Baodong Xu; Geofei Yin; Jing Zhao. An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities. Remote Sensing 2015, 7, 1300 -1319.
AMA StyleYelu Zeng, Jing Li, Qinhuo Liu, Yonghua Qu, Alfredo R. Huete, Baodong Xu, Geofei Yin, Jing Zhao. An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities. Remote Sensing. 2015; 7 (2):1300-1319.
Chicago/Turabian StyleYelu Zeng; Jing Li; Qinhuo Liu; Yonghua Qu; Alfredo R. Huete; Baodong Xu; Geofei Yin; Jing Zhao. 2015. "An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities." Remote Sensing 7, no. 2: 1300-1319.