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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Cloud and cloud shadow detection
Opertional cloud and cloud shadow detection algorithm
Remote sensing data consistency
Temporally consistent remote sensing observations are essential for discovering scientific questions
Land disturbance
Land disturbance and land disturbance regime shift
publications
Making Landsat time series consistent: Evaluating and improving Landsat analysis ready data
Published in Remote Sensing, 2018
This paper examines the impacts of data resampling, cloud and cloud shadow detection, Bidirectional Reflectance Distribution Function (BRDF) correction, and topographic correction on the temporal consistency of the Landsat Time Series (LTS), by comparing Landsat Collection 1 ARD with standard Path/Row scenes.
Recommended citation: Shi Qiu, Yukun Lin, Rong Shang, Junxue Zhang, Lei Ma, and Zhe Zhu (2019). "Making Landsat time series consistent: Evaluating and improving Landsat analysis ready data." Remote Sensing. 11(1).
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Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images
Published in Remote Sensing of Environment, 2019
This paper introduces the Mountainous Fmask (MFmask) algorithm, a specialized version designed for mountainous areas that integrates DEM data and enhances shadow matching in the original Fmask algorithm. These improvements have been incorporated into the latest versions of Fmask.
Recommended citation: Shi Qiu, Binbin He, Zhe Zhu, Zhanmang Liao, and Xingwen Quan (2017). "Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images." Remote Sensing of Environment. 199: 107-119.
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Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery
Published in Remote Sensing of Environment, 2019
This paper introduces the Fmask 4 algorithm, which is currently used to generate the quality assessment band for NASA’s Harmonized Landsat and Sentinel-2 (HLS) data (see this paper for details).
Recommended citation: Shi Qiu, Zhe Zhu, and Binbin He (2019). "Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery." Remote Sensing of Environment. 111205.
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Cirrus clouds that adversely affect Landsat 8 images: What are they and how to detect them?
Published in Remote Sensing of Environment, 2020
This paper not only introduces an algorithm called Cmask (Cirrus cloud mask) for cirrus cloud detection in Landsat 8 imagery using time series of Cirrus Band, but also quantifies the effect of increasing Cirrus Band TOA reflectance on the surface reflectance of spectral bands.
Recommended citation: Shi Qiu, Zhe Zhu, and Curtis E Woodcock (2020). "Cirrus clouds that adversely affect Landsat 8 images: What are they and how to detect them?." Remote Sensing of Environment. 111884.
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Can Landsat 7 preserve its science capability with a drifting orbit?
Published in Science of Remote Sensing, 2021
This paper is the first to report the orbital drift of the Landsat 7 satellite and its impact on reflectance. The findings summarized in this study currently serve as foundational information about Landsat 7, as featured in Google Earth Engine.
Recommended citation: Shi Qiu, Zhe Zhu, Rong Shang, and Christopher J Crawford (2021). "Can Landsat 7 preserve its science capability with a drifting orbit?." Science of Remote Sensing. 4.
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Evaluation of Landsat image compositing algorithms
Published in Remote Sensing of Environment, 2023
This paper introduces a new image compositing algorithm (MAX-RNB) based on the maximum ratio of Near Infrared (NIR) to Blue band (RNB), and evaluated it together with nine other compositing algorithms: MAX-NDVI (maximum Normalized Difference Vegetation Index), MED-NIR (median NIR band), WELD (conterminous United States Web-Enabled Landsat Data), BAP (Best Available Pixel), PAC (Phenology Adaptive Composite), WPS (Weighted Parametric Scoring), MEDOID (medoid measurement), COSSIM (cosine similarity), and NLCD (National Land Cover Database). The method is one of the benchmarks compared with the Google AlphaEarth Satellite Embedding Dataset (see the paper)
Recommended citation: Shi Qiu, Zhe Zhu, Pontus Olofsson, Curtis E Woodcock, and Suming Jin (2023). "Evaluation of Landsat image compositing algorithms." Remote Sensing of Environment. 285.
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Physics-informed machine learning for cloud detection
Published in Remote Sensing of Environment, 2025
This paper introduces a novel Physics-Informed Machine Learning (PIML) framework for better cloud detection for Landsat and Sentinel-2 imagery.
Recommended citation: Shi Qiu, Zhe Zhu, Xiucheng Yang, Junchang Ju, Qiang Zhou, and Christopher S.R. Neigh (2025). "Physics-informed machine learning for cloud detection." Remote Sensing of Environment. in Revise.
A shift from human-directed to wild-undirected land disturbances in the US
Published in Nature Geoscience, 2025
This paper produces the first-ever 30m US land disturbance dataset, and documents land disturbance regime shift 1988-2022.
Recommended citation: Qiu S., Zhu Z., Yang X., Woodcock C., Fahey R., Stehman S., Zhang Y., Cullerton M., Grinstead A., Hong F., Song K., Suh J., Li T., Ren W., Nemani R. (2025). "A shift from human-directed to wild-undirected land disturbances in the US." Nature Geoscience. in Press.
talks
Comparison of machine learning models for mapping medium-resolution land cover and land change
Published:
teaching
Remote Sensing of Environment
Graduate course, University of Connecticut, Department of Natural Resources and the Environment, 2022
This introductory course provides a solid foundation in remote sensing science and technology. Students will examine a range of remote sensing sensors and their applications in environmental studies, while gaining essential skills in data analysis using specialized software. Topics include the principles of electromagnetic radiation, spectral reflectance, Earth observation platforms and sensors, image processing methods, and multidisciplinary applications. The course combines theory and practice to prepare students for effectively applying remote sensing in real-world problem solving.
Remote Sensing Image Processing
Graduate course, University of Connecticut, Department of Natural Resources and the Environment, 2025
Remote sensing image processing will focus on various kinds of remote sensing image processing techniques, primarily using optical satellite imagery. The course covers a variety of related topics that include the physical processes involved in remote sensing and various kinds of image-processing methods. The labs will be primarily focused on how to use the image processing software (e.g., ENVI) to analyze remotely sensed imagery.