Spatio-temporal MODIS EVI gap filling under cloud cover: An example in Scotland

Poggio, Laura; Gimona, Alessandro; Brown, Iain

doi:10.1016/j.isprsjprs.2012.06.003

Cited by 67 publications

(37 citation statements)

References 70 publications

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“…To overcome this problem, alternative methods have been developed to determine the values of contaminated pixels based on the correlation of data within the spatial dimension combined with the temporal dimension [22]. Cho and Suh [23] used a land cover map for 2006-2008 to define the spatial neighborhood, and estimated the NDVIs of contaminated pixels by weighting the NDVIs of high-quality pixels that had the same land cover type within a predetermined window.…”

Section: Introductionmentioning

confidence: 99%

A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets

Yuan

et al. 2015

Remote Sensing

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Reconstructing normalized difference vegetation index (NDVI) time series datasets is essential for monitoring long-term changes in terrestrial vegetation. Here, a temporal-spatial iteration (TSI) method was developed to estimate the NDVIs of contaminated pixels, based on reliable data. The NDVIs of contaminated pixels were first computed through linear interpolation of adjacent high-quality pixels in the time series. Then, the NDVIs of remaining contaminated pixels were determined based on the NDVI of a high-quality pixel located in the same ecological zone, showing the most similar NDVI change trajectories. These two steps were repeated iteratively, using the estimated NDVIs as high-quality pixels to predict undetermined NDVIs of contaminated pixels until the NDVIs of all contaminated pixels were estimated. A case study was conducted in Inner Mongolia, China. The accuracies of estimated NDVIs using TSI were higher than the asymmetric Gaussian, Savitzky-Golay, and window-regression methods. Root mean square error (RMSE) and mean absolute percent error (MAPE) decreased by 16.7%-86.6% and 18.3%-33.0%, respectively. The TSI method performed better over a range of environmental conditions, the variation of performance by the compared methods was 1.4-5 times that of the TSI method. The TSI method will be most applicable when large numbers of contaminated pixels exist. OPEN ACCESSRemote Sens. 2015, 7 8907

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Section: Introductionmentioning

confidence: 99%

A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets

Yuan

et al. 2015

Remote Sensing

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“…Previous research attempting to address the problem of missing data can be grouped into three categories. In the first, methods use multi-source satellite sensors to make up for the missing gaps by assuming low temporal variability between the auxiliary and target image with a well-controlled system error [11]. The transform function which is used to transform an auxiliary pixel to a missing data pixel might come from a spectral BRDF model [12] or a Principal Component Transformation (PCT) or other statistics-based models like Local Linear Histogram Matching (LLHM) [13].…”

Section: Introductionmentioning

confidence: 99%

“…The simplest way is to use the mean value of the images before and after the time of the current image to fill the gap at the same location [15]. These strategies have been applied in MODIS EVI products [11] and TM NDVI [16] gap filling. However, the approaches will fail because soil surface reflectance is highly variable due to soil surface moisture changes that occur over a short time.…”

Section: Introductionmentioning

confidence: 99%

Data-Gap Filling to Understand the Dynamic Feedback Pattern of Soil

et al. 2015

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Detailed and accurate information on the spatial variation of soil over low-relief areas is a critical component of environmental studies and agricultural management. Early studies show that the pattern of soil dynamics provides comprehensive information about soil and can be used as a new environmental covariate to indicate spatial variation in soil in low relief areas. In practice, however, data gaps caused by cloud cover can lead to incomplete patterns over a large area. Missing data reduce the accuracy of soil information and make it hard to compare two patterns from different locations. In this study, we introduced a new method to fill data gaps based on historical data. A strong correlation between MODIS OPEN ACCESSRemote Sens. 2015, 7 11802 band 7 and cumulated reference evapotranspiration ( 0 ) has been confirmed by theoretical derivation and by the real data. Based on this correlation, data gaps in MODIS band 7 can be predicted by daily evaporation data. Furthermore, correlations among bands are used to predict soil reflectance in MODIS bands 1-6 from MODIS band 7. A location in northeastern Illinois with a large area of low relief farmland was selected to examine this idea. The results show a good exponential relationship between MODIS band 7 and CET0 0.5 in most locations of the study area (with average R 2 = 0.55, p < 0.001, and average NRMSE 10.40%). A five-fold cross validation shows that the approach proposed in this study captures the regular pattern of soil surface reflectance change in bands 6 and 7 during the soil drying process, with a Normalized Root Mean Square Error (NRMSE) of prediction of 13.04% and 10.40%, respectively. Average NRMSE of bands 1-5 is less than 20%. This suggests that the proposed approach is effective for filling the data gaps from cloud cover and that the method reduces the data collection requirement for understanding the dynamic feedback pattern of soil, making it easier to apply to larger areas for soil mapping.

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“…The geostatistics approach has been used for filling out the gaps or the absence of data due to the presence of clouds [24][25][26]. Techniques that use spatial-temporal interpolation for pixel reconstruction [27][28][29] are also applied.…”

Section: Introductionmentioning

confidence: 99%

“…However, they do not consider the spatial and temporal correlation of the pixel values. The spatial-temporal interpolation method has to be flexible and able to provide reconstructed images reproducing spatial patterns and local features of the product analyzed [28,29]. Meanwhile, the quality information available on the MODIS products contributes to the appropriate interpretation and application, while the products considered as of low quality must be used with caution [30].…”

Section: Introductionmentioning

confidence: 99%

Window Regression: A Spatial-Temporal Analysis to Estimate Pixels Classified as Low-Quality in MODIS NDVI Time Series

2014

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MODerate resolution Imaging Spectroradiometer (MODIS) data are largely used in multitemporal analysis of various Earth-related phenomena, such as vegetation phenology, land use/land cover change, deforestation monitoring, and time series analysis. In general, the MODIS products used to undertake multitemporal analysis are composite mosaics of the best pixels over a certain period of time. However, it is common to find bad pixels in the composition that affect the time series analysis. We present a filtering methodology that considers the pixel position (location in space) and time (position in the temporal data series) to define a new value for the bad pixel. This methodology, called Window Regression (WR), estimates the value of the point of interest, based on the regression analysis of the data selected by a spatial-temporal window. The spatial window is represented by eight pixels neighboring the pixel under evaluation, and the temporal window selects a set of dates close to the date of interest (either earlier or later). Intensities of noises were simulated over time and space, using the MOD13Q1 product. The method presented and other techniques (4253H twice, Mean Value Iteration (MVI) and Savitzky-Golay) were evaluated using the Mean Absolute Percentage Error (MAPE) and Akaike Information Criteria (AIC). The tests revealed the consistently superior performance of the Window Regression approach to estimate new Normalized Difference Vegetation Index (NDVI) values irrespective of the intensity of the noise simulated.

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Spatio-temporal MODIS EVI gap filling under cloud cover: An example in Scotland

Cited by 67 publications

References 70 publications

A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets

A Temporal-Spatial Iteration Method to Reconstruct NDVI Time Series Datasets

Data-Gap Filling to Understand the Dynamic Feedback Pattern of Soil

Window Regression: A Spatial-Temporal Analysis to Estimate Pixels Classified as Low-Quality in MODIS NDVI Time Series

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