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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

A radiative transfer model-based method for the estimation of grassland aboveground biomass

TL;DR: The RTM-based method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology.
Journal ArticleDOI

The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

Tangao Hu, +1 more
- 25 May 2018 - 
TL;DR: Multitemporal Landsat 8 OLI and Sentinel-2 data are used to investigate vegetation damage on Dominica and Puerto Rico by Hurricane Maria, and related influencing factors are analyzed, providing insight into the sensitivity and recovery of vegetation after a major land-falling hurricane.
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An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions

TL;DR: A new Automatic Time-Series Analysis method to screen clouds and cloud shadows in multi-temporal optical images, which needs fewer predefined parameters, does not require a thermal infrared band, and is more suitable for areas with persistent clouds is presented.
Journal ArticleDOI

Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification

TL;DR: It is demonstrated and concluded that error quantification is an essential step in land cover classification and land cover change detection and presents a path for improving present assessments for sustainable supply chain analyses and associated recommendations.
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An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations

TL;DR: In this paper, the spectral reflectance of cirrus clouds in the underflight data that were omitted from Landsat 7 cloud masks but were detected in Landsat 8's cloud mask due to the use of the newly added cirrus band.
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.
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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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