Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks From MODIS Surface Reflectance

Jia, Kun; Liang, Shunlin; Liu, Suhong; Li, Yuwei; Xiao, Zhiqiang; Yao, Yunjun; Jiang, Bo; Zhao, Xiang; Wang, Xiaoxia; Xu, Shuai; Cui, Jiao

doi:10.1109/tgrs.2015.2409563

Cited by 156 publications

(110 citation statements)

References 36 publications

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“…Machine learning methods have been applied widely to land surface parameter product generation using remotely sensed data due to their capability of nonlinear fitting and computational efficiency. For example, Baret et al used back-propagation neural networks (BPNNs) to produce the GEOV1 leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of vegetation cover (FCOVER) products [1,21,27]. Durbha et al [28] used support vector regression (SVR) to retrieve LAI from Multi-angle Imaging SpectroRadiometer (MISR) data with satisfactory results.…”

Section: Methodsmentioning

confidence: 99%

“…The training samples used to develop the FVC estimation algorithm from MODIS surface reflectance data with GRNNs were used in this study [27]. The sampling locations consisted of the …”

Section: Training Samplesmentioning

confidence: 99%

“…Recently, Jia et al [27] developed an operational algorithm for estimating FVC from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data using general regression neural networks (GRNNs). Their method was used to generate the Global LAnd Surface Satellite (GLASS) FVC product, which was supported by China's National High Technology Research and Development Program.…”

Section: Introductionmentioning

confidence: 99%

“…Validation results demonstrated that the GRNNs-generated GLASS FVC product had accuracy that was comparable to the accuracy of the GEOV1 FVC product, which was considered to be the best global FVC product available at the time. Moreover, the spatial and temporal continuities of the GLASS FVC were superior to those of the GEOV1 FVC [27]. Balancing the estimation accuracy and the computational efficiency of the estimation algorithm is a key issue for generating lengthy time series, and high-resolution and global-scale FVC products.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data

et al. 2016

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Long-term global land surface fractional vegetation cover (FVC) products are essential for various applications. Currently, several global FVC products have been generated from medium spatial resolution remote sensing data. However, validation results indicate that there are inconsistencies and spatial and temporal discontinuities in the current FVC products. Therefore, the Global LAnd Surface Satellite (GLASS) FVC product algorithm using general regression neural networks (GRNNs), which achieves an FVC estimation accuracy comparable to that of the GEOV1 FVC product with much improved spatial and temporal continuities, was developed. However, the computational efficiency of the GRNNs method is low and unsatisfactory for generating the long-term GLASS FVC product. Therefore, the objective of this study was to discover an alternative algorithm for generating the GLASS FVC product that has both an accuracy comparable to that of the GRNNs method and adequate computational efficiency. Four commonly used machine learning methods, back-propagation neural networks (BPNNs), GRNNs, support vector regression (SVR), and multivariate adaptive regression splines (MARS), were evaluated. After comparing its performance of training accuracy and computational efficiency with the other three methods, the MARS model was preliminarily selected as the most suitable algorithm for generating the GLASS FVC product. Direct validation results indicated that the performance of the MARS model (R 2 = 0.836, RMSE = 0.1488) was comparable to that of the GRNNs method (R 2 = 0.8353, RMSE = 0.1495), and the global land surface FVC generated from the MARS model had good spatial and temporal consistency with that generated from the GRNNs method. Furthermore, the computational efficiency of MARS was much higher than that of the GRNNs method. Therefore, the MARS model is a suitable algorithm for generating the GLASS FVC product from Moderate Resolution Imaging Spectroradiometer (MODIS) data.

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Section: Methodsmentioning

confidence: 99%

“…The training samples used to develop the FVC estimation algorithm from MODIS surface reflectance data with GRNNs were used in this study [27]. The sampling locations consisted of the …”

Section: Training Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data

et al. 2016

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“…However, in terms of FVC products, the current FVC products were obtained mainly from low-or medium-resolution remote sensing data such as SPOT-VGT, SEAWIFS, MERIS, MODIS and AVHRR data [1,22,[28][29][30], which limits the FVC applications to the regional and local scales [31]. The development of FVC products from decametric spatial resolution sensors will be better for addressing these applications closely related to agriculture, ecosystem and environmental management.…”

Section: Introductionmentioning

confidence: 99%

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

et al. 2017

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Abstract:Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R 2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R 2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R 2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data.

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Fractional Vegetation Cover

Chu¹

2019

Remote Sensing of Land Use and Land Cover in Mountain Region

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Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks From MODIS Surface Reflectance

Cited by 156 publications

References 36 publications

Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data

Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

Fractional Vegetation Cover

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