Abstract
High-spatial-resolution satellites usually have the constraint of a low temporal frequency, which leads to long periods without information in cloudy areas. Furthermore, low-spatial-resolution satellites have higher revisit cycles. Combining information from high- and low- spatial-resolution satellites is thought a key factor for studies that require dense time series of high-resolution images, e.g., crop monitoring. There are several fusion methods in the bibliography, but they are time-consuming and complicated to implement. Moreover, the local evaluation of the fused images is rarely analyzed. In this paper, we present a simple and fast fusion method based on a weighted average of two input images (H and L), which are weighted by their temporal validity to the image to be fused. The method was applied to two years (2009–2010) of Landsat and MODIS (MODerate Imaging Spectroradiometer) images that were acquired over a cropped area in Brazil. The fusion method was evaluated at global and local scales. The results show that the fused images reproduced reliable crop temporal profiles and correctly delineated the boundaries between two neighboring fields. The greatest advantages of the proposed method are the execution time and ease of use, which allow us to obtain a fused image in less than five minutes.
Highlights
Time series of satellite images allow the extraction of vegetation phenology and are thought a key factor in many studies, including vegetation monitoring [1], biomass production study [2] and land cover classification [3]
The results of the experiments were compared based on global validation indices
This is an example of the outliers’ problem in the ESTARFM, which leads to higher error values. We believe that this problem is due to the fact of using two input H images, which obligates to take images that are far in time. These results show that the enriched H time series with the weighted average (WA) method are highly valuable for classification purposes, and they are valuable for vegetation monitoring
Summary
Time series of satellite images allow the extraction of vegetation phenology and are thought a key factor in many studies, including vegetation monitoring (crops, forests, etc.) [1], biomass production study [2] and land cover classification [3]. Monitoring agricultural activities is crucial for the assessment of productivity and food supplies, and it requires information that highly varies in space and time [4]. The use of image time series is highly valuable for land cover classification purposes because it allows capturing information of the vegetation at different growth stages, which leads to more accurate classifications than by using single images [1,3]
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