Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring

Abstract

Abstract:The Sentinel-2 data by European Space Agency were recently made available for free. Their technical features suggest synergies with Landsat-8 dataset by NASA (National Aeronautics and Space Administration), especially in the agriculture context were observations should be as dense as possible to give a rather complete description of macro-phenology of crops. In this work some preliminary results are presented concerning geometric and spectral consistency of the two compared datasets. Tests were performed specifically focusing on the agriculture-devoted part of Piemonte Region (NW Italy). Geometric consistencies of Sentinel-2 and Landsat-8 datasets were tested "absolutely" (in respect of a selected reference frame) and "relatively" (one in respect of the other) by selecting, respectively, 160 and 100 well distributed check points. Spectral differences affecting at-the-ground reflectance were tested after images calibration performed by dark object subtraction approach. A special focus was on differences affecting derivable NDVI and NDWI spectral indices, being the most widely used in the agriculture remote sensing application context. Results are encouraging and suggest that this approach can successfully enter the ordinary remote sensing-supported precision farming workflow.

Preliminary Evaluation of the Consistency of Landsat 8 and Sentinel-2 Time Series Products in An Urban Area—An Example in Beijing, China

“…Therefore, to set different threshold for OLI and MSI data should be considered in collaboration applications of the two data. Although many studies utilized various applications with different dates data and showed that a difference of less than three days does not lead to significant changes in the correlation coefficient [28,32,47], the compared result of the only one pair from the different dates in this study shows the lowest correlation and largest scatter and RMSD among all comparisons. This may be due to the presence of thin clouds which were not completely excluded by cloud removal or atmospheric correction [48] and influenced the comparison of these data pairs in summer [37].…”

“…Furthermore, the comparison between OLI and MSI data was also processed when these two data were combined in various research fields. For example, the reflection difference between OLI and MSI bands was less than 0.1 overall and the NDVI and NDWI of these two data were generally consistent in the field of agriculture [28]. A study showed that Sentinel-2A and Landsat 8 are consistent in TOA reflectance products and remotesensing reflectance (Rrs) products within 1% and 6%, respectively in monitoring water quality [22].…”

A Comparison of the Signal from Diverse Optical Sensors for Monitoring Alpine Grassland Dynamics

“…An additional topographic correction could be very helpful to correct the images in a proper manner although it is difficult to be adequately geolocated in heterogeneous mountainous areas. As weather, or cloudiness, is the crucial constraint of optical Sentinel-2 data, the combination with other remote sensing data, such as Landsat 8, may lead to a slightly better time series [32,34,69]. However, the combination of remote sensing imagery with other optical sensors, such as Phenocams and SRS, provides reliable information on the slightest changes of the spectral signal each day and makes it easier to monitor the dynamics in grassland communities.…”

“…The combination of the same sensor on multiple spaceborne platforms considerably lowers the revisit time of the sensor to a few days and simultaneously provides information with a spatial resolution from 10 to 20 m [31][32][33]. With its spatial and spectral characteristics, Sentinel-2 is considered to be well-suited for synergetic applications with other remote sensing platforms, such as Landsat 8 [34].…”

Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

“…Table 3 also summarizes the statistical accuracy index corresponding to the classification map in Figure 7. Each classification map was evaluated using six indexes, and included the overall accuracy (OA), producer's accuracy (PA), user's accuracy (UA), area under curve (AUC), f_scores, and kappa coefficient [41]. Manually delineated ground truth polygons (shapefile format) from Section 2.3 were then converted into raster format with the same resolution of 30-m, and were all used as validation samples in a total of 100,445 pixels in Zone 1, 49,968 pixels in Zone 2, and 24,432 pixels in Zone 3.…”