Upscale integration of normalized difference vegetation index: the problem of spatial heterogeneity

Aman, Asma Mohammad Nusrat; Randriamanantena, H.P.; Podaire, A.; Frouin, Robert

doi:10.1109/36.134082

Cited by 59 publications

(29 citation statements)

References 17 publications

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“…A linear mixing model was employed to solve the problem, although it was strictly valid only for the original reflectance values [25]. In the last decades, research has demonstrated that this linear mixing model would introduce very small errors to statistics when replacing the reflectance by NDVI (Equation (2)) [15,21,26].…”

Section: The Theoretical Basismentioning

confidence: 99%

An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM/ETM+ Images

et al. 2015

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Due to technical limitations, it is impossible to have high resolution in both spatial and temporal dimensions for current NDVI datasets. Therefore, several methods are developed to produce high resolution (spatial and temporal) NDVI time-series datasets, which face some limitations including high computation loads and unreasonable assumptions. In this study, an unmixing-based method, NDVI Linear Mixing Growth Model (NDVI-LMGM), is proposed to achieve the goal of accurately and efficiently blending MODIS NDVI time-series data and multi-temporal Landsat TM/ETM+ images. This method firstly unmixes the NDVI temporal changes in MODIS time-series to different land cover types and then uses unmixed NDVI temporal changes to predict Landsat-like NDVI dataset. The test over a forest site shows high accuracy (average difference: −0.0070; average absolute difference: 0.0228; and average absolute relative difference: 4.02%) and computation efficiency of NDVI-LMGM (31 seconds using a personal computer). Experiments over more complex landscape and long-term time-series demonstrated that NDVI-LMGM performs well in each stage of vegetation growing season and is robust in regions with contrasting spatial and spatial variations. Comparisons between NDVI-LMGM and current methods (i.e., Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced STARFM (ESTARFM) and Weighted Linear Model (WLM)) show that NDVI-LMGM is more accurate and efficient than current methods. The proposed method will benefit land surface process research, which requires a dense NDVI time-series dataset with high spatial resolution.

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Section: The Theoretical Basismentioning

confidence: 99%

An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM/ETM+ Images

et al. 2015

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“…Changes in the quantity of vegetation cover directly affect water and energy budgets through plant transpiration, surface albedo, emissivity, and roughness [3]. Biophysical parameters retrieved from satellite observations are thus particularly important for improving our understanding of climatic, hydrologic, and geochemical cycles [4].…”

Section: Introductionmentioning

confidence: 99%

Inter-Algorithm Relationships for the Estimation of the Fraction of Vegetation Cover Based on a Two Endmember Linear Mixture Model with the VI Constraint

Obata

Yoshioka

2010

Remote Sensing

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Measurements of the fraction of vegetation cover (FVC), retrieved from remotely sensed reflectance spectra, serves as a useful measure of land cover changes on the regional and global scales. A linear mixture model (LMM) is frequently employed to analytically estimate the FVC using the spectral vegetation index (VI) as a constraint. Variations in the application of this algorithm arise due to differences in the choice of endmember spectra and VI model assumptions. As a result, the retrieved FVCs from a single spectrum depend on those choices. Therefore, the mechanism underlying this dependency must be understood fully to improve the interpretation of the results. The objective of this study is to clarify the relationships among algorithms based on the LMM. The relationships were derived analytically by limiting both the number of endmembers and the spectral wavelength band to two each. Numerical experiments were conducted to demonstrate and validate the derived relationships. It was found that the relationships between two algorithms of this kind could be characterized by a single parameter that was determined by the endmember spectra and the coefficients of a VI model equation used in the algorithms.

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“…Many studies have focused on the spatial scaling effect of LAI [10,11,[20][21][22][23][24][25][26][27][28][29]. For continuous canopies, Milne and Cohen [27] analyzed the variables including LAI at multiple scales, and Xu et al [26] found that the nonlinear relationship of the averaged vegetation area fraction within mixed pixel was the main cause of scaling effect.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Three Techniques for Correcting the Spatial Scaling Bias of Leaf Area Index

Jiang

Yao

et al. 2018

Remote Sensing

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Abstract:The correction of spatial scaling bias on the estimate of leaf area index (LAI) retrieved from remotely sensed data is an essential issue in quantitative remote sensing for vegetation monitoring. We analyzed three techniques, including Taylor's theorem (TT), Wavelet-Fractal technique (WF), and Fractal theory (FT), for correcting the scaling bias of LAI with empirical models in different functions (i.e., power, exponential, logarithmic and polynomial) on both simulated data and a real dataset over a cropland site. The results demonstrated that the scaling bias became greater when the model non-linearity increased. The spatial heterogeneity, which was characterized by the class-specific proportion, the between-class spectral difference and the number of classes within each coarse pixel, was found to be the primary factor in the scaling effect. These factors influenced the scaling effect collectively and existed dependently. With the RMSE less than 0.3 × 10 −6 m 2 /m 2 , TT was suggested for the correction with a polynomial LAI-NDVI functions. WF was preferred for neighboring scales rather than continuous scales. FT was not recommended for correcting the scaling bias caused by the significant non-linearity in LAI estimation models. This study illustrates the main causes of the scaling effect and provides a reference of technique selection for scaling bias correction to improve the application of remotely sensed estimates.

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Upscale integration of normalized difference vegetation index: the problem of spatial heterogeneity

Cited by 59 publications

References 17 publications

An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM/ETM+ Images

An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM/ETM+ Images

Inter-Algorithm Relationships for the Estimation of the Fraction of Vegetation Cover Based on a Two Endmember Linear Mixture Model with the VI Constraint

Evaluation of Three Techniques for Correcting the Spatial Scaling Bias of Leaf Area Index

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