Fusion of Sentinel-2 images

Wang, Qunming; Shi, Wenzhong; Li, Zhongbin; Atkinson, Peter M.

doi:10.1016/j.rse.2016.10.030

Cited by 185 publications

(100 citation statements)

References 42 publications

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“…New sensors, such as Chinese GaoFen and overseas Sentinel-2A sensor, were designed for data enhancement to offer unprecedented perspectives on land cover/use and vegetation [19][20][21][22]. These new missions revisit the same area more frequently (every four or ten days) [23,24]. These data's fine spatial resolution, global coverage and relatively fine temporal resolution make them of great utility for mapping crop distribution [25,26].…”

Section: Introductionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

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Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

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Section: Introductionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

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“…In order to increase the spatial resolution of the Vegetation Red-Edge and Short Wave infrared bands, pansharpening techniques should be performed. However, the main pan-sharpening approaches were originally developed for image fusion with a single fine band (Wang, Shi et al 2016). Sentinel-2 provides four 10-m bands that are highly correlated with the 20-m bands.…”

Section: Pre-processingmentioning

confidence: 99%

Sentinel-1 and Sentinel-2 Data Fusion for Wetlands Mapping: Balikdami, Turkey

Kaplan¹,

Avdan²

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Wetlands provide a number of environmental and socio-economic benefits such as their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Remote sensing technology has proven to be a useful and frequent application in monitoring and mapping wetlands. Combining optical and microwave satellite data can help with mapping and monitoring the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing radar and optical remote sensing data can increase the wetland classification accuracy. In this paper, data from the fine spatial resolution optical satellite, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, were fused for mapping wetlands. Both Sentinel-1 and Sentinel-2 images were pre-processed. After the pre-processing, vegetation indices were calculated using the Sentinel-2 bands and the results were included in the fusion data set. For the classification of the fused data, three different classification approaches were used and compared. The results showed significant improvement in the wetland classification using both multispectral and microwave data. Also, the presence of the red edge bands and the vegetation indices used in the data set showed significant improvement in the discrimination between wetlands and other vegetated areas. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, showing an overall classification accuracy of approximately 90% in the object-based classification method. For future research, we recommend multi-temporal image use, terrain data collection, as well as a comparison of the used method with the traditional image fusion techniques.

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“…The geostatistical approach known as kriging [31] has gained special acceptance as a tool for the interpolation of many types of data, including the surface temperature, precipitation, soil properties and elevation [32][33][34]. Kriging has been utilized for the downscaling of Landsat and MODIS images [35][36][37] and pansharpening during Sentinel-2 fusion tasks [38]. Kriging was developed to achieve the best linear unbiased prediction (BLUP) and obtain the optimum weights Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

A Rigorously-Weighted Spatiotemporal Fusion Model with Uncertainty Analysis

Wang

Huang

2017

Remote Sensing

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Abstract:Interest has been growing with regard to the use of remote sensing data characterized by a fine spatial resolution and frequent coverage for the monitoring of land surface dynamics. However, current satellite sensors are fundamentally limited by a trade-off between their spatial and temporal resolutions. Spatiotemporal fusion thus provides a feasible solution to overcome this limitation, and many blending algorithms have been developed. Among them, the popular spatial and temporal adaptive reflectance fusion model (STARFM) is based on a weighted function; however, it uses an ad hoc approach to estimate the weights of surrounding similar pixels. Additionally, an uncertainty analysis of the predicted result is not provided in the STARFM or any other fusion algorithm. This paper proposes a rigorously-weighted spatiotemporal fusion model (RWSTFM) based on geostatistics to blend the surface reflectances of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-5 Thematic Mapper (TM) imagery. The RWSTFM, which is based on ordinary kriging, derives the weights in terms of a fitted semivariance-distance relationship and calculates the estimation variance, which is a measure of the prediction uncertainty. The RWSTFM was tested using three datasets and compared with two commonly-used spatiotemporal reflectance fusion algorithms: the STARFM and the flexible spatiotemporal data fusion (FSDAF) method. The fusion results show that the proposed RWSTFM consistently outperformed the other algorithms both visually and quantitatively. Additionally, more than 70% of the squared error was accounted for by the estimation variance of the RWSTFM for all three of the datasets.

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Fusion of Sentinel-2 images

Cited by 185 publications

References 42 publications

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Sentinel-1 and Sentinel-2 Data Fusion for Wetlands Mapping: Balikdami, Turkey

A Rigorously-Weighted Spatiotemporal Fusion Model with Uncertainty Analysis

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