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Journal ArticleDOI

Object-based cloud and cloud shadow detection in Landsat imagery

Zhe Zhu, +1 more
- 15 Mar 2012 - 
- Vol. 118, pp 83-94
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TLDR
The goal is development of a cloud and cloud shadow detection algorithm suitable for routine usage with Landsat images and as high as 96.4%.
About
This article is published in Remote Sensing of Environment.The article was published on 2012-03-15. It has received 1620 citations till now. The article focuses on the topics: Cloud top & Cloud fraction.

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Citations
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Journal ArticleDOI

Declining greenness in Arctic-boreal lakes

TL;DR: In this paper, the authors analyzed time series of 30m Landsat growing season composites to quantify trends in lake greenness for >4 × 105 waterbodies in boreal and Arctic western North America.
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Monitoring Irrigation Using Landsat Observations and Climate Data over Regional Scales in the Murray-Darling Basin

TL;DR: In this article, the authors used three vegetation indices derived from Landsat images to calculate crop coefficients (K c ) based on multiple published relationships, which were combined through the FAO56 methodology using gridded rainfall and two reference evapotranspiration (E T o ) products to find actual evapOTranspiration as A E T = E T o × K c, providing six E t o - K c combinations.
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A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images

TL;DR: The results show that the method proposed not only has high accuracy, but also has advantages in terms of time consumption, which could serve as a reference for those who are interested in the cloud detection of remote sensing images.
Journal ArticleDOI

Comparison of Three Landsat TM Compositing Methods: A Case Study Using Modeled Tree Canopy Cover

TL;DR: In this paper, the authors evaluated the effectiveness of using more easily automated image composites techniques, such as median Landsat-5 image composite or maximum NDVI Landsat 5 image composite, as alternatives to model II regression mosaics for the modeling of PTCC.
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Development of Geospatial and Temporal Characteristics for Hispaniola’s Lake Azuei and Enriquillo Using Landsat Imagery

TL;DR: This paper used Landsat imagery for water body identification to create a novel 36-year surface area extent time series for lakes Azuei and Enriquillo aimed at illuminating the dramatic temporal changes of these two lakes not just at yearly but at monthly or even sub-monthly scales.
References
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Book

Morphological Image Analysis: Principles and Applications

Pierre Soille
TL;DR: This self-contained volume will be valuable to all engineers, scientists, and practitioners interested in the analysis and processing of digital images.
Journal ArticleDOI

A Landsat surface reflectance dataset for North America, 1990-2000

TL;DR: Initial comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat surface reflectance compared to the standard MODIS reflectance product.
Journal ArticleDOI

Discriminating clear sky from clouds with MODIS

TL;DR: The MODIS cloud mask algorithm as discussed by the authors uses several cloud detection tests to indicate a level of confidence that the MEDIS is observing clear skies, which is ancillary input to MEDIS land, ocean, and atmosphere science algorithms to suggest processing options.
Journal ArticleDOI

Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data

TL;DR: Zhang et al. as discussed by the authors used a more advanced NASA Goddard Institute for Space Studies (GISS) radiative transfer model and improved ISCCP cloud climatology and ancillary data sets.
Journal ArticleDOI

Spectral signature of alpine snow cover from the Landsat Thematic Mapper.

TL;DR: In this article, the spectral signatures of the Landsat TM images of the Sierra Nevada were analyzed to distinguish several classes of snow from other surface covers, and a number of TM images were used for automatic analysis of alpine snow cover.
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