Comparison of MODIS fPAR Products with Landsat-5 TM-Derived fPAR over Semiarid Rangelands of Idaho

Chen, Fang; Weber, Keith T.; Anderson, J. H.; Gokhale, Bhushan

doi:10.2747/1548-1603.47.3.360

Cited by 11 publications

(5 citation statements)

References 53 publications

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“…Moreover, following a similar approach, the method can be adapted to produce sequences of synthetic images for other ecosystem variables, provided that they can be calculated from both moderate-and medium-resolution resolution instruments. Examples include additional vegetation indices, leaf area index [72], fraction of photosynthetically active radiation [73][74][75][76], albedo [77], net primary productivity, snow, water and burn indices, etc. This approach is pixel-based, does not require parameter tuning and can be implemented independently of the number of existing medium-resolution images.…”

Section: Discussionmentioning

confidence: 99%

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

2014

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A data assimilation method to produce complete temporal sequences of synthetic medium-resolution images is presented. The method implements a Kalman filter recursive algorithm that integrates medium and moderate resolution imagery. To demonstrate the approach, time series of 30-m spatial resolution NDVI images at 16-day time steps were generated using Landsat NDVI images and MODIS NDVI products at four sites with different ecosystems and land cover-land use dynamics. The results show that the time series of synthetic NDVI images captured seasonal land surface dynamics and maintained the spatial structure of the landscape at higher spatial resolution. The time series of synthetic medium-resolution NDVI images were validated within a Monte Carlo simulation framework. Normalized residuals decreased as the number of available observations increased, ranging from 0.2 to below 0.1. Residuals were also significantly lower for time series of synthetic NDVI images generated at combined recursion (smoothing) than individually at forward and backward recursions (filtering). Conversely, the uncertainties of the synthetic images also decreased when the number of available observations increased and combined recursions were implemented.

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Section: Discussionmentioning

confidence: 99%

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

2014

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“…In addition, it is not feasible to apply the groundbased methods to measure FPAR across large landscapes [9]. Even at some regional scales, these methods are also difficult to use to measure FPAR for studying spatial patterns of FPAR [10]. Remote sensing provides an alternative and unique method to obtain repeated, rapid and inexpensive estimates of FPAR over large areas [6,11].…”

Section: Introductionmentioning

confidence: 99%

Estimating FPAR of maize canopy using airborne discrete-return LiDAR data

Luo

Wang

et al. 2014

Opt. Express

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The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter for ecosystem modeling, crop growth monitoring and yield prediction. Ground-based FPAR measurements are time consuming and labor intensive. Remote sensing provides an alternative method to obtain repeated, rapid and inexpensive estimates of FPAR over large areas. LiDAR is an active remote sensing technology and can be used to extract accurate canopy structure parameters. A method to estimating FPAR of maize from airborne discrete-return LiDAR data was developed and tested in this study. The raw LiDAR point clouds were processed to separate ground returns from vegetation returns using a filter method over a maize field in the Heihe River Basin, northwest China. The fractional cover (fCover) of maize canopy was computed using the ratio of canopy return counts or intensity sums to the total of returns or intensities. FPAR estimation models were established based on linear regression analysis between the LiDAR-derived fCover and the field-measured FPAR (R(2) = 0.90, RMSE = 0.032, p < 0.001). The reliability of the constructed regression model was assessed using the leave-one-out cross-validation procedure and results show that the regression model is not overfitting the data and has a good generalization capability. Finally, 15 independent field-measured FPARs were used to evaluate accuracy of the LiDAR-predicted FPARs and results show that the LiDAR-predicted FPAR has a high accuracy (R(2) = 0.89, RMSE = 0.034). In summary, this study suggests that the airborne discrete-return LiDAR data could be adopted to accurately estimate FPAR of maize.

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“…The advantage of BP is that, unlike most VI, they do not depend on variables such as geometry of illumination or sensor characteristics. They are then often considered as very good candidates to replace classical vegetation indices for characterizing and monitoring green vegetation [31][32][33][34]. The parameters exploited here are presented in Table 4.…”

Section: Computing Vegetation Indices and Biophysical Parametersmentioning

confidence: 99%

Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Duthoit²,

et al. 2017

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Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dorée symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dorée symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dorée and healthy pixel misclassification, an operational Flavescence dorée mapping technique using UAV-based imagery can still be proposed.

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Comparison of MODIS fPAR Products with Landsat-5 TM-Derived fPAR over Semiarid Rangelands of Idaho

Cited by 11 publications

References 53 publications

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

Estimating FPAR of maize canopy using airborne discrete-return LiDAR data

Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

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