Cloud and Cloud Shadow Detection for
Landsat Images: The Fundamental Basis
for Analyzing Landsat Time Series
Zhe Zhu, Shi Qiu, Binbin He, and Chengbin Deng
Brief Summary ......................................................................................................... 4
1.1 Introduction .................................................................................................... 4
1.2 Landsat Data and Reference Masks ............................................................ 5
1.2.1 Landsat Data .......................................................................................5
1.2.2 Manual Masks of Landsat Cloud and Cloud Shadow ..................7
1.3 Cloud and Cloud Shadow Detection Based on a Single-Date
Landsat Image ................................................................................................ 8
1.3.1 Physical-Rules-Based Cloud and Cloud Shadow Detection ........ 8
220.127.116.11 Physical-Rules-Based Cloud Detection Algorithms ...... 8
18.104.22.168 Physical-Rules-Based Cloud Shadow Detection
Algorithms ......................................................................... 12
1.3.2 Machine-Learning-Based Cloud and Cloud Shadow
Detection ........................................................................................... 14
1.4 Cloud and Cloud Shadow Detection Based on Multitemporal
Landsat Images ............................................................................................ 14
1.4.1 Cloud Detection Based on Multitemporal Landsat Images ...... 15
1.4.2 Cloud Shadow Detection Based on Multitemporal Landsat
1.5 Discussions ................................................................................................... 17
1.5.1 Comparison of Different Algorithms ........................................... 17
1.5.2 Challenges ......................................................................................... 17
1.5.3 Future Development ........................................................................ 18
22.214.171.124 Spatial Information ........................................................... 18
126.96.36.199 Temporal Frequency ......................................................... 18
188.8.131.52 Haze/Thin Cloud Removal ............................................. 18
1.6 Conclusion ....................................................................................................19
References .............................................................................................................. 19
4Remote Sensing Time Series Image Processing
Cloud and cloud shadow detection is the fundamental basis for analyzing
Landsat time series. This chapter provides a comprehensive review of all the
cloud and cloud shadow detection algorithms designed explicitly for Landsat
images. This review provides guidance on the selection of cloud and cloud
shadow detection algorithms for various applications using Landsat time
Landsat satellites have been widely used for a variety of remote sensing
applications, such as change detection (Collins and Woodcock, 1996; Xian
etal., 2009), land cover classication (Homer etal., 2004; Yuan etal., 2005),
biomass estimation (Zheng etal., 2004; Lu, 2005), and leaf area index retrieval
(Chen and Cihlar, 1996; Fassnacht etal., 1997). Nevertheless, for decades, most
of the analyses were based on a single or a few cloud free Landsat images
acquired at different dates, due to the high cost of Landsat images prior to
2008 (Loveland and Dwyer, 2012). Free and open access to the entire Landsat
archive in 2008 has changed the story entirely (Woodcock etal., 2008; Wulder
etal., 2012). Landsat data are being downloaded for an unprecedented variety
of applications. Many of them require frequent Landsat observations for
the same location – Landsat Time Series (LTS). The Landsat Global Archive
Consolidation (LGAC) initiative has added 3.2 million Landsat images to the
U.S. Geological Survey (USGS) Earth Resources Observation and Science
(EROS) Center (Wulder etal., 2016), which has made time series analysis
with LTS even more popular. Decreasing data storage costs and increasing
computing power have further stimulated the use of LTS.
Though time series analysis based on LTS has attracted much attention,
automated cloud and cloud shadow detection has been and remains a major
obstacle. The presence of clouds and cloud shadows reduces the usability of
the Landsat image which makes it difcult for any kind of remote sensing
applications. For coarse resolution images, such as from the Advanced Very
High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging
Spectroradiometer (MODIS), there are many mature operational algorithms
for detecting clouds and cloud shadows (Derrien etal., 1993; Ackerman
etal., 1998). However, for moderate resolution satellites, like Landsat, there
were no algorithms that could provide cloud and cloud shadow masks at
the pixel level. This is not surprising because Landsat images were not
affordable, each of which previously cost more than 400 U.S. dollars per
image. Even when cloudy Landsat images are used, most of the time only
5Cloud and Cloud Shadow Detection for Landsat Images
a small number of images are needed, and manual interpretation of clouds
and their shadows in the images is feasible. However, when these nancial
constraints were lifted (Woodcock etal., 2008), an unprecedented demand
arose for automatically processing a massive number of Landsat images for
time series analysis. Manual interpretation of cloud and cloud shadow was
no longer acceptable.
1.2 Landsat Data and Reference Masks
1.2.1 Landsat Data
Since 1972, Landsat satellites have provided a continuous Earth observation
data record. Landsats 1–5 carried the Multispectral Scanner System (MSS)
sensor with 60-meter spatial resolution. The MSS only collected images with
four spectral bands, including green, red, and two Near InfraRed (NIR)
bands (Ta ble 1.1). Note that the Landsat 3 MSS also included a Thermal
Infrared (TIR) band, but failed shortly after launch. The fewer bands result
in known difculties in detecting clouds and cloud shadows (Braaten etal.,
2015). However, the MSS images are still crucial for LTS related analyses
(Pugmacher etal., 2012). Since the launch of Landsat 4 in 1982, the Thematic
Mapper (TM) has provided more spectral information at 30-meter spatial
resolution (Table 1.1). The TM sensor was also carried on Landsat 5, which
was launched on March 1, 1984, and functioned for over 28 years until 2012.
Landsat 7, carrying the Enhanced Thematic Mapper Plus (ETM+), was
launched on April 15, 1999 (Table 1.1). This instrument also has a 30-meter
spatial resolution and improved radiometric and geometric calibration
accuracies, but the Scan Line Corrector (SLC) has failed since May 31, 2003.
Both TM and ETM+ have a TIR band at a spatial resolution of 120-meter
and 60-meter, respectively. Landsat 8 was launched on February 11, 2013.
It has two sensors: Operational Land Imager (OLI) and Thermal Infrared
Sensor (TIRS) (Tabl e 1.1). The OLI instrument provides 30-meter resolution
optical data, while TIRS provides 100-meter resolution TIR data. Note that
the TIRS has a shorter design life compared to the OLI. Additionally, the new
OLI added the new blue band (Band 1: 0.435–0.451 µm) and the cirrus band
(Band9: 1.363–1.384 µm) with 30-meter spatial resolution.
Although each Landsat satellite can cover global land every 16 days, many
of the observations are inevitably impacted by clouds and cloud shadows.
Figure 1.1 illustrates mean global cloud cover calculated based on all available
Landsat 8 daytime images acquired between September 2013 and August
2017. The cloud cover information for each Landsat Path/Row is calculated
based on the metadata of Landsat 8 images downloaded from the USGS
Landsat Bulk Metadata Service (https://landsat.usgs.gov/landsat-bulk-
metadata-service), which is derived based on an algorithm called Fmask
6Remote Sensing Time Series Image Processing
(Zhuand Woodcock, 2012; Zhu etal., 2015). Extremely high cloud cover is
observed in tropical rainforest regions, while for arid places, such as desert
or dryland regions, cloud cover is relatively low. The mean global cloud cover
contained in the Landsat images is approximately 41.59%, which means that
clouds impact almost half of the Landsat observations.
Landsat 1–5 MSS, Landsat 4–5 TM, Landsat 7 ETM+ and Landsat 8 OLI Sensor
MSS Bands (µm)
TM Bands (µm)
ETM+ Bands (µm)
OLI/TIRS Bands (µm)
Band 1 (0.45–0.52) Band 1 (0.45–0.52) Band 1 (0.435–0.451)
Band 4 (0.50–0.60) Band 2 (0.52–0.60) Band 2 (0.52–0.60) Band 2 (0.452–0.512)
Band 5 (0.60–0.70) Band 3 (0.63–0.69) Band 3 (0.63–0.69) Band 3 (0.533–0.590)
Band 6 (0.70–0.80)
Band 7 (0.80–1.10)
Band 4 (0.76–0.90) Band 4 (0.77–0.90) Band 4 (0.636–0.673)
Band 5 (1.55–1.75) Band 5 (1.55–1.75) Band 5 (0.851–0.879)
Band 8 (10.40–12.50)
Landsat 3 onlya
Band 6 (10.40–12.50) Band 6 (10.40–12.50) Band 6 (1.566–1.651)
Band 7 (2.08–2.35) Band 7 (2.09–2.35) Band 7 (2.107–2.294)
Band 8 (0.52–0.90) Band 8 (0.503–0.676)
Band 9 (1.363–1.384)
Band 10 (10.60–11.19)
Band 11 (11.50–12.51)
a Indicates that the thermal band of the Landsat 3 MSS was unsuccessful and not available.
Mean cloud cover percentage for each scene (%)
0–20 21–40 41–60 61–80 81–100 No data
Mean global cloud cover percentage calcu lated based on all available La ndsat 8 images acquired
between September 2013 and August 2017. A total of 966,708 Landsat 8 images are used. The
mean global cloud cover percentage from all Landsat 8 observations is 41.59%.
7Cloud and Cloud Shadow Detection for Landsat Images
1.2.2 Manual Masks of Landsat Cloud and Cloud Shadow
Manually interpreted cloud and cloud shadow masks are the most important
data source for developing and/or validating the cloud and cloud shadow
detection algorithms (Irish et al., 2006; Zhu and Woodcock, 2012; Hughes
and Hayes, 2014; Foga etal., 2017; Qiu etal., 2017). At present, there are
three publicly available, manually interpreted cloud and cloud shadow
masks derived from Landsat images (Tabl e 1. 2), including “L7 Irish” masks
for Landsat 7 data (USGS, 2016a), “L8 SPARCS” masks for Landsat 8 data
(USGS, 2016b), and “L8 Biome” masks for Landsat 8 data (USGS, 2016c). These
masks are manually interpreted based on Landsat images randomly selected
from different locations, which cover a variety of land cover types, and the
cloud cover percentage within each manual mask also varies substantially.
The “L7 Irish” manual masks were rst created to systematically coverthe
global environments and different cloud conditions (Irish etal., 2006). The
“L7 Irish” masks were produced based on Landsat 7 ETM+ images by
visual interpretation of full resolution images with different Landsat band
combinations, and their average error was estimated at approximately 7%
(Oreopoulos et al., 2011). The “L8 SPARCS” manual masks were created
manually from Landsat 8 OLI images by Hughes and Hayes (2014), which
was used to validate Spatial Procedures for Automated Removal of Cloud
and Shadow (SPARCS) algorithm. Note that those manual cloud and cloud
shadow masks are provided at a 3 km by 3 km Landsat subset (1000 × 1000
30-meter pixels), with around 4% of pixels being ambiguous (Foga etal., 2017).
The manual cloud and cloud shadow masks in “L8 Biome” are designed for
Landsat 8 OLI/TIRS images, which were randomly selected from different
locations around the world using a biome-based stratied sampling approach.
Their corresponding cloud and cloud shadow masks were produced by
multiple visual criteria (such as brightness, shape, and texture) with various
band combinations by a single analyst (Foga etal., 2017). This new dataset
achieved better accuracy than the “L7 Irish,” due to the multiple visual
criteria it used (Foga etal., 2017).
Manual Cloud and Cloud Shadow Masks Derived from Landsat Images
Error ReferenceStart End
L7 Irish ETM+
206 (45) 06/06/2000 12/30/2001 7.00% USGS (2016a)
L8 SPARCS OLI 80 (80) 05/12/2013 11/02/2014 4.00% USGS (2016b)
L8 Biome OLI 96 (33) 04/13/2013 11/05/2014 Less than
Note that the all images contain manual cloud masks. The numbers in the brackets indicate the
number of cloud shadow masks for each dataset.
8Remote Sensing Time Series Image Processing
1.3 Cloud and Cloud Shadow Detection Based
on a Single-Date Landsat Image
Recently, many cloud and cloud shadow detection algorithms have been
developed for Landsat images (Table 1. 3). Among them, some were proposed
by using a single-date Landsat image (hereafter single-date algorithms), and
we can classify these single-date algorithms into two categories: physical-
rules-based and machine-learning-based algorithms (Table 1.3).
1.3.1 Physical-Rules-Based Cloud and Cloud Shadow Detection
184.108.40.206 Physical-Rules-Based Cloud Detection Algorithms
The physical-rules-based algorithms detect clouds by identifying their
physical characteristics of clouds, that are “bright”, “white”, “cold”, and
“high” (Irish, 2000; Zhu etal., 2015). Compared to other land cover types, the
reectance of cloud is much higher in almost all wavelengths, which makes
clouds look “bright”. Therefore, we can use some simple thresholds in the
spectral bands to exclude clear sky pixels that are not bright enough. Clouds
are “white” due to the similar reectance in all wavelengths, particularly
in the visible bands. In this case, some indices such as “whiteness” (Zhu
and Woodcock, 2012), Normalized Difference Vegetation Index (NDVI), and
Normalized Difference Snow Index (NDSI) can be used to separate clouds
from clear sky pixels that are not white enough. Moreover, clouds are “cold”
because they are usually high in the air, and the temperature of clouds
follows the environmental lapse rate—the higher the clouds, the colder the
temperature. This characteristic can be successfully captured by the thermal
band from Landsat TM, ETM+, and TIRS instruments, which can further
separate clouds from similar bright and white land surfaces (e.g., barren sand,
soil, rock, snow/ice, etc.). Additionally, as clouds are usually “high” in the
sky, the path for water vapor over clouds is much shorter than that for places
without clouds. Therefore, the water vapor absorption band (or the cirrus
band) is especially helpful in identifying higher altitude clouds.
Most of the algorithms are developed for Landsat TM and ETM+ images.
Historically, the Automated Cloud Cover Assessment (ACCA) was used to
provide cloud cover percentage in Landsat TM and ETM+ images (Irish, 2000;
Irish etal., 2006). With several spectral lters, ACCA works well for estimating
a cloud cover score for each image but is not sufciently precise in identifying
the locations and boundaries of clouds (Zhu and Woodcock, 2012). Besides,
ACCA fails to identify warm cirrus clouds and may misidentify snow/ice
as clouds, mainly because the static thresholds in ACCA are insufcient to
capture the various kinds of clouds and the variety of land surface types.
To better distinguish cloud from snow/ice, Choi and Bindschadler (2004)
used the cloud and cloud shadow geometry matching approach iteratively
9Cloud and Cloud Shadow Detection for Landsat Images
Characteristics of Different Cloud and Cloud Shadow Detection Algorithms for Landsat Images
Name Landsat Sensor
Shadow Ancillary Data
MFmask TM ETM+ OLI/TIRS Both DEM 96% Qiu etal. (2017)
LSR 8 OLI/TIRS Both N/A N/A Vermote etal. (2016)
UDTCDA OLI/TIRS Cloud MOD09A1 N/A Sun etal. (2016)
MSScvm MSS Both DEM 84% Braaten etal. (2015)
ELTK OLI/TIRS Cloud N/A N/A Wilson and Oreopoulos
Fmask TM ETM+ OLI/TIRS Both N/A 96% Zhu and Woodcock (2012),
Zhu etal. (2015)
N/A TM ETM+Both DEM 88%∼99% Huang etal. (2010)
LTK TM ETM+ OLI/TIRS Cloud N/A 93% Oreopoulos etal. (2011)
LEDAPS TM ETM+Both Air temperature
N/A Vermote and Saleous (2007)
CDSM/ANTD ETM+Both N/A N/A Choi and Bindschadler (2004)
ACCA TM ETM+ OLI/TIRS Cloud N/A N/A Irish (2000), Irish etal. (2006)
N/A OLI Cloud N/A N/A Zhou etal. (2016)
SPARCS ETM+Both N/A 99% Hughes and Hayes (2014)
See5 OLI Cloud N/A 89% Scaramuzza etal. (2012)
AT-ACCA OLI Cloud N/A 90% Scaramuzza etal. (2012)
N/A ETM+Both N/A N/A Potapov etal. (2011)
N/A ETM+Cloud N/A N/A Roy etal. (2010)
N/A MSS Cloud N/A 93% Lee etal. (1990)
10 Remote Sensing Time Series Image Processing
TABLE 1.3 (Continued)
Characteristics of Different Cloud and Cloud Shadow Detection Algorithms for Landsat Images
Name Landsat Sensor
Shadow Ancillary Data
Multi-date IHOT MSS TM ETM+Cloud N/A N/A Chen etal. (2015)
TmaskaTM ETM+ OLI/TIRS Both N/A N/A Zhu and Woodcock (2014)
N/AaTM ETM+Both N/A 97% Goodwin etal. (2013)
N/A TM ETM+Both N/A N/A Jin etal. (2013)
MTCDaTM ETM+Both Sentinel-2 data N/A Hagolle etal. (2010)
N/A TM Both N/A N/A Wang etal. (1999)
Note: MFmask: Mountainous Fmask; LSR 8: Landsat 8 Surface Reectance product; UDTCDA: Universal Dynamic Threshold Cloud Detection Algorithm;
MSScvm: MSS clear-view-mask; ELTK: Enhanced LTK; Fmask: Function of mask; LTK: Luo Trishchenko Khlopenkov; LEDAPS: Landsat Ecosystem
Disturbance Adaptive Processing System; CDSM/ANTD: Cloud Detection using Shadow Matching/Automatic NDSI Threshold Decision; ACCA:
Automatic Cloud Cover Assessment; SPARCS: Spatial Procedures for Automated Removal of Cloud and Shadow; See5: C5.0 algorithm used to
generate a decision tree. AT-ACCA: Articial Thermal-Automated Cloud Cover Algorithm; IHOT: Iterative Haze Optimized Transformation;
Tmask: multiTemporal mask; MTCD: Multi-Temporal Cloud Detection; TM: Thematic Mapper; ETM+: Enhanced Thematic Mapper Plus; OLI:
Operational Land Imager; TIRS: Thermal Infrared Sensor; DEM: Digital Elevation Model; MOD09A1: MODerate resolution Imaging
Spectroradiometer surface reectance product; NCEP: National Centers for Environmental Prediction.
a Indicates the algorithms based on Landsat time series.
11Cloud and Cloud Shadow Detection for Landsat Images
to determine the optimal threshold of NDSI for each Landsat image in cloud
detection. This approach works well over ice sheets, but it is time-consuming
and only works on the surface of ice sheets. Vermote and Saleous (2007)
proposed a cloud detection algorithm for Landsat TM and ETM+ images,
and the detection results are provided as one of the internal products in
the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS)
atmosphere correction software. This algorithm needs surface temperature
from the National Centers for Environmental Prediction (NCEP) as ancillary
data to generate a surface temperature reference layer for cloud detection.
Huang et al. (2010) constructed a spectral temperature space to identify
clouds in Landsat image using clear sky forest pixels as a reference. This
method works well over forest areas but has not been fully tested for non-
forest areas. By revisiting the Luo Trishchenko Khlopenkov (LTK) scene
identication algorithm initially developed for the MODIS image (Luo etal.,
2008), Oreopoulos etal. (2011) modied this algorithm to detect clouds in
Landsat 7 ETM+ data using simple thresholds derived for the blue, red, NIR,
and Short-Wave Infrared (SWIR) bands (no thermal band). Recently, Zhu
and Woodcock (2012) developed the Fmask (Function of mask) algorithm
that detects cloud by using a scene-based threshold. This method is suitable
for the Landsats 4–8 data and can generate a cloud probability layer. Users
can adjust the threshold of cloud probability to determine cloud masks. The
default threshold (global optimal) is 22.5%. If large omissions are found, a
smaller threshold (e.g., 12.5%) is recommended, and if large commissions are
observed, a higher threshold (e.g., 50%) is recommended. This method has
also been successfully integrated into the Landsat surface reectance Climate
Data Record (CDR) and Collection 1 Quality Assessment (QA) band provided
by the USGS Earth Resources Observation and Science (EROS) Center. In
the Fmask algorithm, the thermal band is one of the important inputs, as
it can capture the “cold” character of clouds (Zhu etal., 2015). However, the
temperature for clear sky pixels can also vary widely due to substantial
changes in elevation, and this will lead to commission and omission errors
in cloud detection in mountainous areas. To reduce this issue, Qiu etal. (2017)
provided a Mountainous Fmask (MFmask) algorithm that normalizes the
thermal band with Digital Elevation Models (DEMs) based on a simple linear
There are also algorithms explicitly designed for Landsat 8 images, many of
which take advantage of the new blue and cirrus bands equipped in Landsat
OLI. Wilson and Oreopoulos (2013) further modied the aforementioned LTK
algorithm by including the cirrus band to detect cloud better. Zhu etal. (2015)
also designed a cloud detection algorithm for Landsat 8 images by calculating
a thin cloud probability layer from the cirrus band, and achieved better
accuracy than the Fmask algorithm designed initially for TM and ETM+
images. Vermote etal. (2016) proposed a new cloud detection algorithm for
Landsat 8, which used the inversion “residual” from the two blue bands and
the cirrus band reectance. To minimize the inuences of cloud detection
12 Remote Sensing Time Series Image Processing
from mixed pixels, complex surface structures, and atmospheric factors,
Sun etal. (2016) presented a Universal Dynamic Threshold Cloud Detection
Algorithm (UDTCDA) for Landsat 8 OLI images, but only the blue, green, red,
NIR, and SWIR bands were used. The dynamic threshold in this method was
determined based on MODIS monthly surface reectance database, which
was established based on the long-time series of MODIS 8-day synthetic
surface reectance products.
Very few algorithms have been designed for the Landsat MSS image, due to
the limited number of spectral bands within the MSS sensor. To address this
issue, Braaten etal. (2015) proposed a simple and automated cloud detection
algorithm relying on green band brightness and the normalized difference
between the green and red bands and achieved comparable accuracies to the
220.127.116.11 Physical-Rules-Based Cloud Shadow Detection Algorithms
Detecting cloud shadows for Landsat images is more difcult than detecting
clouds due to the spectral similarity of cloud shadows and dark surfaces.
Cloud shadows are usually detected based on physical rules derived from
the cloud shadow geometry.
Previously, the cloud shadow detection algorithms were developed
based on simple spectral tests according to the dark features. However, it
is difcult to directly use thresholds to determine cloud shadows because
their spectral signatures are very similar to other dark surfaces (e.g., terrain
shadows, wetlands, dark urban, etc.). Fortunately, the geometry-based cloud
shadow detection has shown relatively good results. The geometry-based
cloud shadow detection approach is based on the projection of cloud object
onto the local plane of the Earth with respect to a direction of incoming
solar radiation (Berendes et al., 1992; Le Hégarat-Mascle and André,
2009; Simpson etal., 2000). The relative positions of the sun, the satellite,
and the cloud can be used to predict the cloud shadow observed in the
satellite images (Fig u re 1.2). Methods for detecting cloud shadow based on
geometry can be grouped into two categories: shape-similarity-match and
The shape-similarity-match approach detects cloud shadow by matching
cloud shadows with cloud objects, assuming that cloud and cloud shadow
shape are similar (Gurney, 1982; Berendes etal., 1992). Gurney (1982) assumed
that a cumulus cloud is approximated in shape by its associated shadow and
matched cloud shadows with clouds. Berendes etal. (1992) developed a semi-
automated methodology for estimating cumulus cloud base height using
Landsat data by matching cloud edges with their corresponding shadow
edges. Due to the absence of the thermal band, Braaten etal. (2015) used cloud
projection to identify cloud shadow in Landsat MSS image based on their
geometry information. Although the computation of cloud and cloud shadow
match is time-consuming and may result in some mismatches, this approach
13Cloud and Cloud Shadow Detection for Landsat Images
is still an attractive routine, especially for images without the thermal band
(e.g., Landsat MSS).
The cloud-height-estimation method uses a constant lapse rate to estimate
cloud top height based on the Brightness Temperature (BT) difference between
cloud top and the land surface. When the cloud height is known, the clouds
can be easily projected to predict their associate shadows on the ground
based on their geometry relationship (Vermote and Saleous, 2007; Huang
etal., 2010). Vermote and Saleous (2007) detected cloud shadow for Landsat
data using a geometric determination of shadow based on the cloud mask and
the estimated altitude of cloud derived from the BT and a conversion factor
range. Huang etal. (2010) identied cloud shadow based on the dark spectral
features, the temperature-based cloud height estimation with a constant lapse
rate, and the solar illumination geometry. Those methods can work well for
thick clouds but are less ideal for the semitransparent clouds, of which the BT
will be a mixture of thin cloud and the surface underneath.
Zhu and Woodcock (2012) and Zhu etal. (2015) calculated cloud shadows
by combining the previous shape-similarity-matching and the cloud-height-
estimation methods, and treated the cloud as a 3D object. This algorithm works
well for most cases, but it may fail to detect the correct cloud shadows for places
of large topographic change and terrain shadows. To address those issues, Qiu
etal. (2017) applied a double-projection method to calculate cloud shadow
shape and removed terrain shadows using a topographic correction model
with the aid of DEMs. In addition, this improved Fmask algorithm can estimate
a more accurate cloud height based on heights estimated from neighboring
clouds, that also improves the detection of cloud shadow for Landsat images.
of the sensor
Sun/cloud/shadow geometry in Landsat image. Note that θs is the solar zenith angle, ϕs is the
solar azimuth angle, θv is the satellite view zenith angle, and h is the cloud height. The position
of the real cloud may shift from the cloud directly observed by the Landsat sensor.
14 Remote Sensing Time Series Image Processing
1.3.2 Machine-Learning-Based Cloud and Cloud Shadow Detection
Regarding the machine-learning-based algorithms, clouds and/or cloud
shadows are generally treated as a cover type and are identied using
a certain classier trained by previously collected training dataset –
supervised classication. Lee etal. (1990) combined texture-based features
in a network to discriminate clouds in Landsat MSS images and achieved an
overall cloud identication accuracy of 93%. Recognizing that omission and
commission errors for cloud detection will always occur in large datasets
for ACCA, Roy etal. (2010) implemented both the ACCA algorithm and a
classication tree approach to detect clouds using a large number of training
pixels from a global Landsat Level 1G database. Potapov etal. (2011) also
manually selected lots of cloud pixels as training data based on 21 Landsat
images from different years and different regions and built a single tree
model for cloud type to classify the clouds. Due to the possible loss of the
thermal band on Landsat 8, Scaramuzza etal. (2012) expanded the ACCA
pass-1 algorithm without the use of the thermal band, identifying clouds for
Landsat 8 through a statistical classier C5.0 (a classication tree) based on
many randomly sampled pixels from a series of training images. Hughes and
Hayes (2014) also explored the inclusion of spatial information as an input to
a neural network classier on identifying and classifying clouds for Landsat
images. Zhou etal. (2016) utilized the traditional threshold to obtain a coarse
cloud mask and then used the Support Vector Machine (SVM) classier to
detect clouds in Landsat 8 images. Though all these investigations pointed
out the usefulness of machine-learning-based methods in cloud detection,
most require a certain level of knowledge of cloud or surface conditions
within the images (as training data) and commonly fail to detect clouds for
certain unique conditions (Huang etal. 2010). Additionally, cloud shadow
in Landsat image can also be detected using the machine-learning-based
methods (Potapov et al., 2011; Hughes and Hayes, 2014). This method,
however, heavily relies on the training dataset and has substantial omission
or commission errors (Hughes and Hayes, 2014).
1.4 Cloud and Cloud Shadow Detection Based
on Multitemporal Landsat Images
In addition to the single-date algorithm, cloud and cloud shadow detection
algorithms based on multitemporal Landsat images have also been developed
(hereafter multitemporal algorithm). Compared to the spectral or spatial
features derived from a single-date Landsat image, multitemporal Landsat
images can provide extra-temporal information in cloud and cloud shadow
detection, and are reported to produce better cloud and cloud shadow masks
15Cloud and Cloud Shadow Detection for Landsat Images
(Table 1.3). The basic idea of these algorithms is that clouds and cloud shadows
can be easily detected by comparing an observed image with a clear sky
reference (image differencing), as the presence of clouds and cloud shadows
will result in sudden changes of Landsat reectance (Wang etal., 1999; Jin
1.4.1 Cloud Detection Based on Multitemporal Landsat Images
For a long time, multitemporal cloud detection algorithms were only based on
two-date or multi-date Landsat images. Early on, Wang etal. (1999) proposed
the use of two-date Landsat TM images to nd clouds by image differencing.
This proposed method rst coarsely nds the clouds for the two Landsat TM
images by setting a histogram-derived threshold for the brightness values,
and then uses another static threshold for the absolute brightness difference
between the two images to further ensure reliable cloud detection. Jin etal.
(2013) identied clouds by incorporating Landsat blue and thermal bands
from two-date images. Based on two Landsat images that have no overlapping
clouds, this method rst selected the relaxed clouds by differencing the blue
bands from the two images and then produced the restricted clouds by
eliminating some commission pixels with relatively low spectral values in
the SWIR band and low temperature in the thermal band. The thresholds
used in this approach were determined by measuring spectral deviation from
the mean value of the input images. These methods can accurately detect
cloud for the reported images, but the thresholds may not be transferable to
other images. To avoid confusion between bright surfaces and haze/cloud,
Chen et al. (2015) proposed an Iterative Haze Optimized Transformation
(IHOT) for improving haze/clouds detection for Landsat images with the
help of a corresponding clear image. By integrating an iterative procedure
of regressions into the HOT (Zhang etal., 2002), the reectance difference
between hazy and clear images, and reectance of hazy and clear images,
the land surface information can be removed. The IHOT result is derived
to characterize the haze contamination on Landsat images spatially. These
proposed approaches are practical and straightforward only using two-date
or multi-date Landsat images but heavily dependent on the quality and
availability of reference images. Besides, these approaches may not work well
if extensive land cover changes occurred between the acquisition dates of the
reference and cloudy images.
With free and open access to the Landsat archive, time series analysis
with Landsat images became possible, providing a new way to detect clouds
based on higher frequency Landsat observations. The LTS itself can be
used for detecting clouds. Goodwin etal. (2013) used LTS from TM/ETM+
to detect clouds. By using the minimum and median values of the blue
band as a reference, this algorithm can produce better cloud masks across
Queensland compared to Fmask (Zhu and Woodcock, 2012). However, it
has not yet been tested in environments with different soils, vegetation
16 Remote Sensing Time Series Image Processing
cover, and structure or areas with snow/ice cover (Goodwin et al., 2013).
Specically designed for monitoring land cover change, an algorithm called
Tmask (multitemporal mask) has been developed for automated masking of
cloud and snow for LTS (Zhu and Woodcock, 2014). This method estimated
time series models for each pixel based on “clear-sky LTS” previously
ltered by the Fmask algorithm. By using a robust tting approach, the
cloud observations that are missed by Fmask will have minimal impacts on
the estimation of the time series models. By comparing the model estimates
with actual Landsat observations for the green, NIR, and SWIR bands,
we will be able to detect any remaining cloud observations for the entire
stack of Landsat images. In addition to the Landsat images, there are also
algorithms developed for Landsat-like data, such as VENµS and Sentinel-2.
Hagolle etal. (2010) developed the Multi-Temporal Cloud Detection (MTCD)
method that detects sudden increases of reectance in the blue band on a
pixel-by-pixel basis using time series observations and tested the linear
correlation of pixel neighborhoods taken from pairs of images acquired
successively. The MTCD method provides better discrimination of cloudy
and clear sky pixels than the ACCA method for Landsat images. However, it
requires satellite data with high revisit frequency and sequential processing
of the data.
1.4.2 Cloud Shadow Detection Based on Multitemporal Landsat Images
Most cloud shadow detection algorithms using multitemporal Landsat
images assume that the presence of cloud shadows will lead to darker, colder,
and smoother features than the regular land surface (Irish, 2000; Le Hégarat-
Mascle and André, 2009). Wang etal. (1999) presented a wavelet transform
approach to detect cloud shadows for two Landsat TM images automatically.
Considering that the brightness changes of the cloud shadow-obscured
regions are much smoother than the regions with no shadows, the absolute
wavelet coefcients corresponding to cloud shadows decrease much greater
amount than those of other regions. Thus, a relative contrast difference for
the added result of the wavelet transform outputs was directly used to detect
cloud shadow for the two Landsat images with a static threshold. Different
from this complicated approach, Jin etal. (2013) detected the cloud shadows
simply by differencing the SWIR and the thermal bands from two-date
Landsat images and employed the geometric relationship between clouds
and their corresponding shadows to reduce false positive errors. Zhu and
Woodcock (2014) also identied cloud shadows for LTS by image differencing.
The reference values were predicted using a time series model for each pixel.
Though there are only a few cloud shadow detection approaches using
multitemporal Landsat images, these methods can provide better results than
the approaches based on a single-date Landsat image, especially for shadows
from thin clouds (Zhu and Woodcock, 2014).
17Cloud and Cloud Shadow Detection for Landsat Images
1.5.1 Comparison of Different Algorithms
With so many different cloud and cloud shadow detection algorithms
available in the literature, it is essential to compare those approaches
and to provide further guidance on the selection of algorithms for those
interested in using LTS. A list of most of the automated cloud and cloud
shadow detection algorithms can be found in Table 1.3. We observe that the
most widely used detection algorithms are based on a single-date Landsat
image, probably due to the ease of implementation. Recently, Foga etal.
(2017) compared the performances of several popular algorithms using
278 unique cloud validation masks over the entire globe and found that
the CFmask (Fmask algorithm programmed in C) has the best overall
accuracy for Landsat data. It should be noted that the methods based on
multitemporal Landsat images can provide more accurate detection of cloud
and cloud shadow, which is especially important for time series analysis
(e.g., forest disturbance, land cover change, etc.) (Goodwin etal., 2013; Zhu
and Woodcock, 2014).
Clouds are easily confused with snow/ice, especially for mountaintop snow/
ice (Selkowitz and Forster, 2015). These kinds of commissions can be reduced
by the NDSI threshold (Zhu and Woodcock, 2012), verication of clouds with
their corresponding shadows (Choi and Bindschadler, 2004), temperature
normalization (Qiu etal., 2017), or composition of temporal pixels in summer
season (Selkowitz and Forster, 2015). However, it is still difcult to separate
clouds from snow in some circumstances (e.g., icy clouds).
The cloud shadow detection accuracy is still relatively low. The geometry
projection of cloud is a good way to detect cloud shadow, but relies heavily on
the previously identied cloud masks, which have commission or omission
errors and subsequently result in inaccurate cloud shadows. In addition, the
cloud shadows are commonly confused with other dark features, such as
wetlands, dark urban, and terrain shadows. Terrain shadows can be removed
using the topographic correction model with the aid of DEMs (Jin etal., 2013;
Braaten etal., 2015; Qiu etal., 2017). The misidentication of cloud shadow
contributed from other dark features can also be corrected based on the
contextual information from the clouds’ heights estimated from neighboring
clouds (Qiu etal., 2017).
The use of multitemporal Landsat images can produce better cloud and
cloud shadow masks by differencing new observations with reference
observations. However, this kind of approach may not work well due to the
range of non-cloud related variations in reectance, such as the illumination
18 Remote Sensing Time Series Image Processing
geometry, land surface change, geometric misregistration, and variation
in radiometry or atmospheric composition (Hagolle et al., 2010; Goodwin
etal., 2013; Zhu and Woodcock, 2014). Furthermore, these algorithms are
computationally expensive compared to cloud and cloud shadow detection
algorithms based on a single-date Landsat image.
1.5.3 Future Development
1.5. 3.1 Spatial Information
When designing cloud and cloud shadow detection algorithms for Landsat
images, the spectral information and the temporal information have been
explored extensively, but the information contained in the spatial domain is
less studied (Gurney, 1982; Martins etal., 2002). We expect that more cloud and
cloud shadow detection algorithms will focus on the spatial characteristics of
clouds and their shadows and provide masks at higher accuracies.
18.104.22.168 Temporal Frequency
The approaches based on multitemporal Landsat images can provide more
accurate cloud and cloud shadow masks, when compared to the single-date
approaches (Goodwin et al., 2013; Zhu and Woodcock, 2014). In addition to
Landsat data, other Landsat-like satellites have also been launched, such
as Sentinel-2A/2B. The integration of multi-source images will allow more
frequent observations and further improve the detection accuracy. One major
restriction of the multitemporal cloud and cloud shadow detection algorithms
is that these algorithms require large amounts of data and computation time.
However, this will be less an issue with the rapid development of computation
tec h nolog y.
22.214.171.124 Haze/Thin Cloud Removal
Compared with thick clouds, thin clouds are transparent, and images
covered by thin clouds include information from both the atmospheric and
the ground underneath (Li et al., 2012). This gives us the opportunity to
remove the impacts of haze/thin clouds. If the satellite sensor prole and
the atmospheric properties are known, haze/thin clouds’ impacts can be
reduced by atmospheric correction (Vermote and Saleous, 2007). However, it
is difcult to acquire all the atmospheric properties (Liang etal., 2001), and
atmospheric correction may fail in handling the locally concentrated thin
clouds (Shen etal., 2014). Methods based on multispectral transformation,
such as Tasseled Cap (TC) transformation (Richter, 1996), HOT (Zhang
etal., 2002), and Advanced HOT (AHOT) (Liu etal., 2011), can remove haze/
thin clouds’impacts effectively. Besides, haze/thin clouds are generally
distributed in the low frequency parts of the image, which can be removed
by using alow-pass lter (Shen etal., 2014), such as Wavelet Analysis (WA)
19Cloud and Cloud Shadow Detection for Landsat Images
(Du et al., 2002) and Homomorphic Filter (HF) (Fan and Zhang, 2011).
While many haze/thin cloud removal methods are available, there are still
difculties in automated identication of haze/thin clouds using current
cloud detection algorithms. This will hamper the broad applications of haze/
thin cloud removal approaches.
Clouds and cloud shadows are a pervasive, dynamic, and unavoidable issue
in Landsat images, and their accurate detection is the fundamental basis
for analyzing LTS. Many cloud and/or cloud shadow detection algorithms
have been proposed in the literature. For cloud detection, most approaches
are based on a single-date Landsat image, which rely on physical-rules or
machine-learning techniques. With the policy of free and open Landsat
data, some automated cloud detection methods were developed based on
multitemporal Landsat images and can achieve better results. For cloud
shadow detection, the geometry-based approach is widely used in the single-
date algorithms. Meanwhile, by using multitemporal Landsat images, some
researchers used the image differencing method to better identify cloud
shadow. In this chapter, we reviewed many automated cloud and cloud
shadow detection algorithms, which can provide guidance on the selection
of algorithms for those interested in using LTS.
Ackerman, S. A., Strabala, K.I., Menzel, W. P., Frey, R. A., Moeller, C.C., and Gumley, L.E.
1998. Discriminating clear sky from clouds with MODIS. Journal of Geophysical
Research 103(D24): 32141–32157. doi:10.1029/1998J D200032 .
Berendes, T., Sengupta, S. K., Welch, R. M., Wielicki, B. A., and Navar, M. 1992.
Cumulus cloud base height estimation from high spatial resolution Landsat
data: A Hough transform approach. IEEE Transactions on Geoscience and Remote
Sensing 30(3): 430 – 443. doi:10.1109/36.142921.
Braaten, J. D., Cohen, W. B., and Yang, Z. 2015. Automated cloud and cloud shadow
identication in Landsat MSS imagery for temperate ecosystems. Remote Sensing
of Environment 169: 128–138. doi:10.1016/j.rse.2015.08.006.
Chen, J. M. and Cihlar, J. 1996. Retrieving leaf area index of boreal conifer forests
using Landsat TM images. Remote Sensing of Environment 55(2): 153–162.
doi:10.1016/003 4-4257(95)00195- 6.
Chen, S., Chen, X., Chen, J., and Jia, P. 2015. An iterative haze optimized transformation
for automatic cloud/haze detection of Landsat imagery. IEEE Transactions on
Geoscience and Remote Sensing 54(5): 2682–2694. doi:10.1109/TGRS.2015.2504369.
20 Remote Sensing Time Series Image Processing
Choi, H. and Bindschadler, R. 2004. Cloud detection in Landsat imagery of ice sheets
using shadow matching technique and automatic normalized difference snow
index threshold value decision. Remote Sensing of Environment 91(2): 237–242.
Collins, J. B. and Woodcock, C. E. 1996. An assessment of several li near change detect ion
techniques for mapping forest mortality using multitemporal Landsat TM data.
Remote Sensing of Environment 56(1): 66–77. doi:10.1016/0034-4257(95)00233-2.
Derrien, M., Farki, B., Harang, L., LeGleau, H., Noyalet, A., Pochic, D., and Sairouni, A.
1993. Automatic cloud detection applied to NOAA-11 /AVHRR imagery. Remote
Sensing of Environment 46(3): 246–267. doi:10.1016/0034-4257(93)90046-Z.
Du, Y., Guindon, B., and Cihlar, J. 2002. Haze detection and removal in high resolution
satellite image with wavelet analysis. IEEE Transactions on Geoscience and Remote
Sensing 40(1): 210–217. doi:10.1109/36.981363.
Fan, C. N. and Zhang, F. Y. 2011. Homomorphic ltering based illumination
normalization method for face recognition. Pattern Recognition Letters 32(10):
Fassnacht, K. S., Gower, S. T., MacKenzie, M. D., Nordheim, E. V., and Lillesand, T. M.
1997. Estimating the leaf area index of North Central Wisconsin forests using
the Landsat thematic mapper. Remote Sensing of Environment 61(2): 2 29–24 5.
Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T., Schmidt, G.,
Dwyer, J., Hughes, M., and Laue, B. 2017. Cloud detection algorithm comparison
and validation for operational Landsat data products. Remote Sensing of
Environment 194: 379–390. doi:10.1016/j.rse.2017.03.026.
Goodwin, N. R., Collett, L. J., Denham, R. J., Flood, N., and Tindall, D. 2013. Cloud and
cloud shadow screening across Queensland, Australia: An automated method
for Landsat TM/ETM+ time series. Remote Sensing of Environment 134: 50–65.
Gurney, C. M. 1982. The use of contextual information to detect cumulus clouds and
cloud shadows in Landsat data. International Journal of Remote Sensing 3(1): 51– 62.
Hagolle, O., Huc, M., Pascual, D. V., and Dedieu, G. 2010. A multi-temporal method for
cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2
images. Remote Sensing of Environment 114(8): 1747–1755. doi:10.1016/j.
Homer, C., Huang, C., Yang, L., Wylie, B., and Coan, M. 2004. Development of a 2001
national land-cover database for the United States. Photogrammetric Engineering
and Remote Sensing 70(7): 829–840. doi:10.14358/PERS.70.7.829.
Huang, C. et al. 2010. Automated masking of cloud and cloud shadow for forest
change analysis using Landsat images. International Journal of Remote Sensing
31(20): 5449–5464. doi:10.1080/01431160903369642.
Hughes, M. J. and Hayes, D. J. 2014. Automated detection of cloud and cloud shadow in
single-date Landsat imagery using neural networks and spatial post-processing.
Remote Sensing of Environment 6(6): 4907–4926. doi:10.3390/rs6064907.
Irish, R. R. 2000. Landsat 7 automatic cloud cover assessment. Proceedings Volume 4049,
Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery, Orlando,
FL, United States, AeroSense: International Society for Optics and Photonics.
21Cloud and Cloud Shadow Detection for Landsat Images
Irish, R. R., Barker, J. L., Goward, S. N., and Arvidson, T. 2006. Characterization of
the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) algorithm.
Photogrammetric Engineering and Remote Sensing 72(10): 1179 –1188. doi :10.14358/
Jin, S., Homer, C., Yang, L., Xian, G., Fry, J., Danielson, P., and Townsend, P. A. 2013.
Automated cloud and shadow detection and lling using two-date Landsat
imagery in the USA. International Journal of Remote Sensing 34(5): 1540–1560.
Lee, J., Weger, R. C., Sengupta, S. K., and Welch, R. M. 1990. A neural network approach
to cloud classication. IEEE Transactions on Geoscience and Remote Sensing 28(5):
Le Hégarat-Mascle, S. and André, C. 2009. Use of Markov random elds for automatic
cloud/shadow detection on high resolution optical images. ISPRS Journal of
Photogrammetry and Remote Sensing 64(4): 3 51–36 6. do i:10.1016/j.i spr sjpr s.20 08.12.00 7.
Li, Q., Lu, W., Yang, J., and Wang, J. Z. 2012. Thin cloud detection of all-sky images
using Markov random elds. IEEE Geoscience and Remote Sensing Letters 9(3):
Liang, S., Fang, H., and Chen, M. 2001. Atmospheric correction of Landsat ETM+ land
surface imagery. I. Methods. IEEE Transactions on Geoscience and Remote Sensing
39(11): 2490–2498. doi:10.1109/36.964986.
Liu, C. B., Hu, J. B., Lin, Y., Wu, S. H., and Huang, W. 2011. Haze detection, perfection
and removal for high spatial resolution satellite imagery. International Journal of
Remote Sensing 32(23): 8685 – 8697. doi:10.1080/01431161.2010. 547884.
Loveland, T. R. and Dwyer, J. L. 2012. Landsat: Building a strong future. Remote
Sensing of Environment 122: 22–29. doi:10.1016/j.rse.2011.09.022.
Lu, D. 2005. Aboveground biomass estimation using Landsat TM data in the
Brazilian Amazon. International Journal of Remote Sensing 26(12): 2509–2525.
doi:10.1080/014311605 0 0142145.
Luo, Y., Trishchenko, A. P., and Khlopenkov, K. V. 2008. Developing clear-sky, cloud
and cloud shadow mask for producing clear-sky composites at 250-meter spatial
resolution for the seven MODIS land bands over Canada and North America.
Remote Sensing of Environment 112(12): 4167– 4185. doi:10.1016/j.rse.2008.06.010.
Martins, J. V., Tanré, D., Remer, L., Kaufman, Y., Mattoo, S., and Levy, R. 2002.
MODIS cloud screening for remote sensing of aerosols over oceans using
spatial variability. Geophysical Research Letters 29(12): MOD4-1–MOD4-4.
Oreopoulos, L., Wilson, M. J., and Várnai, T. 2011. Implementation on Landsat data of a
simple cloud-mask algorithm developed for MODIS land bands. IEEE Geoscience
and Remote Sensing Letters 8(4): 597–601. doi:10.1109/LGRS.2010.2095409.
Pugmacher, D., Cohen, W. B., and Kennedy, R. E. 2012. Using Landsat-derived
disturbance history (1972–2010) to predict current forest structure. Remote
Sensing of Environment 122: 146 –165. doi:10.1016/j.rse.2011.09.025.
Potapov, P., Turubanova, S., and Hansen, M. C. 2011. Regional-scale boreal forest
cover and change mapping using Landsat data composites for European Russia.
Remote Sensing of Environment 115(2): 5 48–561. doi:10.1016/j.r se.2010.10.001.
Qiu, S., He, B., Zhu, Z., Liao Z., and Quan, X. 2017. Improving Fmask cloud and cloud
shadow detection in mountainous area for Landsats 4–8 images. Remote Sensing
of Environment 199: 107–119. doi:10.1016/j.rse.2017.07.002.
22 Remote Sensing Time Series Image Processing
Richter, R. 1996. A spatially adaptive fast atmospheric corre ction algorithm. International
Journal of Remote Sensing 17(6): 1201–1214. doi:10.1080/01431169608949077.
Roy, D. P., Ju, J., Kline, K., Scaramuzza, P. L., Kovalskyy, V., Hansen, M., Loveland, T. R.,
Vermote, E., and Zhang, C. 2010. Web-enabled Landsat Data (WELD): Landsat
ETM+ composited mosaics of the conterminous United States. Remote Sensing
of Environment 114(1): 35–49. doi:10.1016/j.rse.2009.08.011.
Scaramuzza, P. L., Bouchard, M. A., and Dwyer, J. L. 2012. Development of the Landsat
data continuity mission cloud-cover assessment algorithms. IEEE Transactions
on Geoscience and Remote Sensing 50(4): 1140–115 4. do i:10.1109/TGR S.2011.2164 087.
Selkowitz, D. J. and Forster, R. R. 2015. An automated approach for mapping persistent
ice and snow cover over high latitude regions. Remote Sensing 8(1): 16. doi:10.3390/
Shen, H., Li, H., Qian, Y., Zhang, L., and Yuan, Q. 2014. An effective thin cloud removal
procedure for visible remote sensing images. ISPRS Journal of Photogrammetry
and Remote Sensing 96: 224–235. doi:10.1016/j.isprsjprs.2014.06.011.
Simpson, J. J., Jin, Z., and Stitt, J. R. 2000. Cloud shadow detection under arbitrary
viewing and illumination conditions. IEEE Transactions on Geoscience and Remote
Sensing 38(2): 972–976. doi:10.1109/36.841979.
Sun, L. et al. 2016. A universal dynamic threshold cloud detection algorithm
(UDTCDA) supported by a prior surface reectance database. Journal of
Geophysical Research: Atmospheres 121(12): 7172–7196. doi:10.1002/2015JD02472 2.
U.S. Geological Survey. 2016a. L7 Irish Cloud Validation Masks. U.S. Geological Survey
data release. doi:10.5066/F7XD0ZWC. (accessed September 26, 2017)
U.S. Geological Survey. 2016b. L8 SPARCS Cloud Validation Masks. U.S. Geological
Survey data release. doi:10.5066/F7FB5146. (accessed September 26, 2017)
U.S. Geological Survey. 2016c. L8 Biome Cloud Validation Masks. U.S. Geological
Survey data release. doi:10.5066/F7251GDH. (accessed September 26, 2017)
Vermote, E., Justice, C., Claverie, M., and Franch, B. 2016. Preliminary analysis of
the performance of the Landsat 8/OLI land surface reectance product. Remote
Sensing of Environment 185: 4 6 – 56. doi:10.1016/j.r se. 2016.04.008.
Vermote, E. and Saleous, N. 2007. LEDAPS Surface Reectance Product Description.
College Park: University of Maryland. doi:null
Wang, B., Ono, A., Muramatsu, K., and Fujiwara, N. 1999. Automated detection
and removal of clouds and their shadows from Landsat TM images. IEICE
Transactions on Information and Systems 82(2): 453–460. doi:null
Wilson, M. J. and Oreopoulos, L. 2013. Enhancing a simple MODIS CLOUD mask
algorithm for the Landsat data continuity mission. IEEE Transactions on
Geoscience and Remote Sensing 51(2): 723–731. doi:10.1109/TGRS.2012.2203823.
Woodcock, C. E. etal. 2008. Free access to Landsat imagery, Science 320: 1011–1012.
doi:10.1126/scienc e. 320.5879.1011a.
Wulder, M. A., Masek, J. G., Cohen, W. B., Loveland, T. R., and Woodcock, C. E. 2012.
Opening the archive: How free data has enabled the science and monitoring
promise of Landsat. Remote Sensing of Environment 122: 2–10. doi:10.1016/j.
Wulder, M. A., White, J. C., Loveland, T. R., Woodcock, C. E., Belward, A. S., Cohen,
W.B., Fosnight, E. A., Shaw, J., Masek, J. G., and Roy, D. P. 2016. The global Landsat
archive: Status, consolidation, and direction. Remote Sensing of Environment 185:
271–28 3. doi:10.1016/j.rse.2015.11.032 .
23Cloud and Cloud Shadow Detection for Landsat Images
Xian, G., Homer, C., and Fry, J. 2009. Updating the 2001 National Land Cover
Database land cover classication to 2006 by using Landsat imagery change
detection methods. Remote Sensing of Environment 113(6): 1133 –1147. doi :10.1016/j.
Yuan, F., Sawaya, K. E., Loeffelholz, B. C., and Bauer, M. E. 2005. Land cover
classication and change analysis of the Twin Cities (Minnesota) Metropolitan
Area by multitemporal Landsat remote sensing. Remote Sensing of Environment
98(2): 317–328. doi:10.1016/j.rse.2005.08.006.
Zhang, Y., Guindon, B., and Cihlar, J. 2002. An image transform to characterize and
compensate for spatial vari ations in t hin cloud contamination of Landsat images.
Remote Sensing of Environment 82(2): 173–187. doi:10.1016/S0034-4257(02)00034-2.
Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J., and Ryu, S. R.
2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a
managed landscape in northern Wisconsin, USA. Remote Sensing of Environment
93(3): 402–411. doi:10.1016/j.rse.2004.08.008.
Zhou, G., Zhou, X., Yue, T., and Liu, Y. 2016. An optional threshold with SVM cloud
detection algorithm and DSP implementation. ISPRS-International Archives of
the Photogrammetry, Remote Sensing and Spatial Information Sciences 41: 771–777.
Zhu, Z. and Woodcock, C. E. 2012. Object-based cloud and cloud shadow detection
in Landsat imagery. Remote Sensing of Environment 118: 83–94. doi:10.1016/j.
Zhu, Z. and Woodcock, C. E. 2014. Automated cloud, cloud shadow, and snow
detection in multitemporal Landsat data: An algorithm designed specically
for monitoring land cover change. Remote Sensing of Environment 152: 217–234.
Zhu, Z., Wang, S., and Woodcock, C. E. 2015. Improvement and expansion of the
Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4–7, 8,
and Sentinel 2 images. Remote Sensing of Environment 159: 269–27 7. doi:10.1016/j.
rs e.2014.12 .014.