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Remote Sensing of Environment
Elsevier
Remote Sensing of Environment

Elsevier

0034-4257

Remote Sensing of Environment/Journal Remote Sensing of EnvironmentSCIISTPEI
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    Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

    Skakun, SergiiWevers, JanBrockmann, CarstenDoxani, Georgia...
    22页
    查看更多>>摘要：Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10-30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algo-rithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities.
      原文链接:
    • NSTL
    • Elsevier

    A novel framework to assess all-round performances of spatiotemporal fusion models

    Zhu, XiaolinZhan, WenfengZhou, JunxiongChen, Xuehong...
    22页
    查看更多>>摘要：Spatiotemporal data fusion, as a feasible and low-cost solution for producing time-series satellite images with both high spatial and temporal resolution, has undergone rapid development over the past two decades with more than one hundred spatiotemporal fusion methods developed. Accuracy assessment of fused images is crucial for users to select appropriate methods for real-world applications. However, commonly used assessment metrics do not comprehensively cover multiple aspects of spatiotemporal fused image quality, contain redundant information, and are not comparable across different study areas. To address these problems, this study proposed a novel framework to assess all-round performances of spatiotemporal fusion methods. Four accuracy metrics, including RMSE, AD, Edge, and local binary patterns (LBP), were selected as the optimal set of assessment metrics according to the assessment criteria. These metrics not only quantify the spectral and spatial information in the fused images but also greatly alleviate information redundancy and feature computational simplicity. Furthermore, inspired by Taylor diagrams, we designed an all-round performance assessment (APA) diagram to provide a visual tool for a comprehensive assessment of the performance of spatiotemporal fusion methods, supporting cross-comparison of different spatiotemporal fusion methods by considering the effects of input data and land surface characteristics. The case study in three typical sites demonstrated that the proposed framework can better differentiate the performances of six spatiotemporal fusion methods. This new framework can promote the cross-comparison of different spatiotemporal fusion methods and guide users to select suitable methods for real-world applications, as well as facilitate the establishment of a standard accuracy assessment procedure for spatiotemporal fusion methods.
      原文链接:
    • NSTL
    • Elsevier

    Conterminous United States Landsat-8 top of atmosphere and surface reflectance tasseled cap transformation coefficients

    Zhai, YongguangRoy, David P.Martins, Vitor S.Zhang, Hankui K....
    20页
    查看更多>>摘要：The tasseled cap transformation (TCT) has been widely used to decompose satellite multi-spectral information into "brightness", "greenness", and "wetness" components. Published TCT coefficients for the Landsat sensor series have mainly been derived using top of atmosphere (TOA) reflectance and sparse data sets. Studies to derive TCT coefficients for Landsat surface reflectance (SR) are lacking. In this study, the TCT coefficients were derived independently for Landsat-8 Operational Land Imager (OLI) SR and TOA reflectance using the Gram-Schmidt orthogonalization (GSO) method. To ensure that the derived TCT coefficients are robust and broadly applicable, representative samples of soil, vegetation, and water were selected from summer and autumn Landsat-8 OLI Analysis Ready Data (ARD) sampled from 40.4 million 30 m pixel locations across the conterminous United States (CONUS). Given that the blue band is susceptible to atmospheric contamination due to its shorter wavelength, two groups of TCT coefficients were derived: one from 6 bands (Blue, Green, Red, NIR, SWIR1, SWIR2) and one from 5 bands without the blue band. As TCT results cannot be validated in a formal way, the TCT components for CONUS summer TOA and SR composites were generated and compared with National Land Cover Database (NLCD) land cover classes to provide a synoptic assessment and provide confidence in the results. In addition, three ARD tiles selected to encompass a mix of land cover types, predominantly, desert in Nevada, wetland and urban in Florida, and agriculture in North Dakota, were used to analyze the seasonal variation of the TCT components. The results demonstrate that the derived Landsat-8 TCT coefficients can effectively characterize brightness, greenness, and wetness components across the CONUS, and show good consistency for discrimination of land cover types and track seasonal surface variations. There was no significant difference between each TCT component derived using the 6-band and 5-band TCT coefficients considering a large sample of CONUS pixels. Therefore, the 5-band TCT coefficients provided in this study are recommended for use, as the blue band is atmospherically sensitive and difficult to atmospherically correct reliably.
      原文链接:
    • NSTL
    • Elsevier

    Early- and in-season crop type mapping without current-year ground truth: Generating labels from historical information via a topology-based approach

    Lin, ChenxiZhong, LihengSong, Xiao-PengDong, Jinwei...
    22页
    查看更多>>摘要：Land cover classification in remote sensing is often faced with the challenge of limited ground truth labels. Incorporating historical ground information has the potential to significantly lower the expensive cost associated with collecting ground truth and, more importantly, enable early-and in-season mapping that is helpful to many pre-harvest decisions. In this study, we propose a new approach that can effectively transfer knowledge about the topology (i.e. relative position) of different crop types in the spectral feature space (e.g. the histogram of SWIR1 vs RDEG1 bands) to generate labels, thereby supporting crop classification in a different year. Importantly, our approach does not attempt to transfer classification decision boundaries that are susceptible to inter-annual variations of weather and management, but relies on the more robust and shift-invariant topology information. We tested this approach for mapping corn/soybeans in the US Midwest, paddy rice/corn/soybeans in Northeast China and multiple crops in Northern France using Landsat-8 and Sentinel-2 data. Results show that our approach automatically generates high-quality labels for crops in the target year immediately after each image becomes available. Based on these generated labels from our approach, the subsequent crop type mapping using a random forest classifier can reach the F1 score as high as 0.887 for corn as early as the silking stage and 0.851 for soybean as early as the flowering stage and the overall accuracy of 0.873 in the test state of Iowa. In Northeast China, F1 scores of paddy rice, corn and soybeans and the overall accuracy can exceed 0.85 two and half months ahead of harvest. In the Hauts-de-France region, the OA of multiple crop mapping could reach 0.837 based on labels generated from our approach. Overall, these results highlight the unique advantages of our approach in transferring historical knowledge and maximizing the timeliness of crop maps. Our approach supports a general paradigm shift towards learning transferrable and generalizable knowledge to facilitate land cover classification.
      原文链接:
    • NSTL
    • Elsevier

    Surface UV-assisted retrieval of spatially continuous surface ozone with high spatial transferability

    Song, GeLi, SiweiXing, JiaYang, Jie...
    15页
    查看更多>>摘要：With the rapid development of artificial intelligence, machine-learning has been used broadly in surface ozone retrievals. However, the accuracies of current machine-learning-based models tend to rely largely on the spatial and temporal patterns of the site observations of surface ozone instead of the internal physical and chemical mechanisms of ozone, which also limit the transferability of the retrieval models. In this study, the surface ultraviolet (UV) at 380 nm, which is an important component of the rate-determining step of ozone photochemical production, is involved as an indicator in the newly developed surface ozone retrieval algorithm. And the imputation of missing satellite observations of ozone nitrogen dioxide (NO2) column and surface UV is conducted to obtain spatially continuous surface ozone. Different validation schemes and case studies are used to comprehensively evaluate the new algorithm. With the involvement of the surface UV, the new retrieval algorithm shows the high accuracy (R2 = 0.853 and RMSE = 17.09 mu g/m3) in spatially continuous surface ozone estimation. More significantly, the new algorithm shows outstanding spatial transferability, which has been a critical challenge for machine-learning models on surface ozone estimation.
      原文链接:
    • NSTL
    • Elsevier