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Landsat-8: Science and product vision for terrestrial global
change research
D.P. Roy
a,
⁎, M.A. Wulder
b
,T.R.Loveland
c
, C.E.Woodcock
d
,R.G.Allen
e
, M.C. Anderson
f
,D.Helder
g
,J.R.Irons
h
,
D.M. Johnson
i
,R.Kennedy
d
, T.A. Scambos
j
,C.B.Schaaf
k
,J.R.Schott
l
, Y. Sheng
m
,E.F.Vermote
n
,A.S.Belward
o
,
R. Bindschadler
p
,W.B.Cohen
q
,F.Gao
r
,J.D.Hipple
s
, P. Hostert
t
, J. Huntington
u
, C.O. Justice
v
, A. Kilic
w
,
V. Kovalskyy
a
,Z.P.Lee
k
,L.Lymburner
x
,J.G.Masek
y
,J.McCorkel
y
,Y.Shuai
z
, R. Trezza
e
,J.Vogelmann
c
,
R.H. Wynne
aa
,Z.Zhu
d
a
Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD57007, USA
b
Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, British Columbia, V8Z 1M5, Canada
c
U.S. Geological Survey Earth Resources Observation and Science (EROS) Center 47914 252nd Street, Sioux Falls, SD 57198, USA
d
Department of Earth and Environment, Boston University, MA 02215, USA
e
University ofIdaho Research and Extension Center, Kimberly, ID 83341, USA
f
United States Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
g
College of Engineering, South Dakota State University Brookings, SD 57007, USA
h
Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
i
United States Department of Agriculture, National Agricultural Statistics Service, 3251 Old Lee Highway, suite 305, Fairfax, VA 22030, USA
j
National Snow and Ice Data Center, University of Colorado, 1540 30th Street, Boulder CO 80303, USA
k
School for the Environment, University of Massachusetts Boston, Boston, MA 02125, USA
l
Rochester Institute of Technology, Chester F. Carlson Center for Imaging Science, Rochester, NY 14623, USA
m
Department of Geography, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
n
Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
o
European Commission, Joint Research Centre, Institute for Environment and Sustainability, 20133 VA, Italy
p
Hydrospheric and Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
q
USDA Forest Service, PNW Research Station, Corvallis, OR 97331, USA
r
USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
s
United States Department of Agriculture, Risk Management Agency, Washington, DC 20250, USA
t
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
u
Desert Research Institute, Reno, NV, 89501, USA
v
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
w
Dept. of Civil Engineering, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 65816, USA
x
Geoscience Australia, GPO Box 378 Canberra ACT 2601, Australia
y
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
z
ERT Inc. at the Biospheric Sciences Laboratory of NASA's Goddard Space Flight Center, Greenbelt, MD 20771, USA
aa
Virginia Tech, Forest Resources and Environmental Conservation, 310 West Campus Dr, Blacksburg, VA 24061, USA
abstractarticle info
Article history:
Received 9 October 2013
Received in revised form 28 January 2014
Accepted 1 February 2014
Available online xxxx
Keywords:
Landsat 8
OLI
TIRS
Landsat Science Team
Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's ter-
restrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the
remarkable 40 year Landsat record and has enhanced capabilities including new spectral bands in the blue and
cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance
and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a
significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science
Team's efforts to establishan initial understandingof Landsat 8 capabilitiesand the steps ahead in support of pri-
orities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science
and applications opportunitiesare described withrespect to calibration and radiometric characterization; surface
reflectance; surface albedo; surface temperature,evapotranspiration and drought;agriculture; land cover, condi-
tion, disturbance and change; fresh and coastal water; and snowand ice. Insights into thedevelopment of derived
‘higher-level’Landsat products are provided inrecognition of the growing need forconsistently processed, mod-
erate spatial resolution, large area, long-term terrestrial data records for resource management and for climate
and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land
Remote Sensing of Environment 145 (2014) 154–172
⁎Corresponding author.
Contents lists available at ScienceDirect
Remote Sensing of Environment
journal homepage: www.elsevier.com/locate/rse
http://dx.doi.org/10.1016/j.rse.2014.02.001
0034-4257 © 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-SA license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
imaging constellations by combining Landsat data with data collected from other international sensing systems,
and consideration of successor Landsat mission requirements.
© 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-SA license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
1. Introduction
At over 40 years, the Landsat series of satellites provides thelongest
temporal record of space-based surface observations. Landsat 1 was
launched in 1972 and was followed by a series of consecutive, tempo-
rally overlapping, Landsat observatories (Landsat 2, 3, 4, 5 and 7) that
have provided near-global coverage reflective and thermal wavelength
observations with increasing spectral and spatial fidelity (Lauer,
Morain, & Salomonson, 1997; Loveland & Dwyer, 2012; Williams,
Goward, & Arvidson, 2006). Remarkably, the Landsat record is unbro-
ken, with most land locations acquired at least once per year since
1972, capturing a period when the global human population has more
than doubled (United Nations Population Division, 2011) and evidence
for climate change has become discernible (Hansen, Sato, & Ruedy,
2012; IPCC, 2013). Landsat data offer a unique record of the land surface
and its modification over time. The Landsat moderate spatial resolution
is sufficiently resolved to enable chronicling of anthropogenic and nat-
ural change at local to global scale (Gutman et al., 2008; Townshend &
Justice, 1988) and the data time series are calibrated to provide a char-
acterized consistent record (Markham & Helder, 2012) that is needed to
enable discrimination between data artifacts and actual land surface
temporal changes (Roy et al., 2002). Landsat data have demonstrated
capabilities for mapping and monitoring of land cover and land surface
biophysical and geophysical properties (Hansen & Loveland, 2012;
Wulder, Masek, Cohen, Loveland, & Woodcock, 2012) and potential
utility for terrestrial assimilation and biogeochemical cycling and land
use forecasting applications (Lewis et al., 2012; Nemani et al., 2009;
Sleeter et al., 2012). Applications addressed with Landsat data involve
both scientific discovery and managing and monitoring resources for eco-
nomic and environmental quality, public health and human well-being,
and national security. Analyses of the economic benefits of Landsat vary
from $935 million/year (ASPRS, 2006) to $2.19 billion/year (Miller,
Richardson, Koontz, Loomis, & Koontz, 2013) in support of applications
including water resource analysis and management, agriculture and for-
est analysis and management, homeland security, infrastructure analysis,
disaster management, climate change science, wetland protection, and
monitoring land cover change.
The 40+ year Landsat record was continued with the successful
February 11th 2013 launch of Landsat 8 from Vandenburg Air Force
Base, California. This new Landsat observatory was developed through
an interagency partnership between the National Aeronautics and
Space Administration (NASA) and the Department of the Interior U.S.
Geological Survey (USGS) (Irons & Loveland, 2013). NASA led the mis-
sion and wasresponsible for system engineering and design, developing
the flight segment, securing launch services, flight ground systems inte-
gration, and conducting on-orbit initialization and verification. NASA
referred to the effort as the Landsat Data Continuity Mission (LDCM)
during the development, launch, and on-orbit commissioning. USGS
led the ground system development and the LDCM was renamed
Landsat 8 on May 30th 2013 when the USGS formally took responsibil-
ity for mission operations, including collecting, archiving, processing,
and distributing Landsat 8 data. Landsat 8 carries two sensors, the Oper-
ational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS), and
over 500 image scenes per day are ingested into the U.S. Landsat data
archive at the USGS Earth Resource Observation and Science (EROS)
Center, South Dakota. The new Landsat 8 scenes complement the now
more than four million scenes acquired by previous Landsat missions
that are stored in the U.S. Landsat archive and are freely available via
the internet (Woodcock et al., 2008).
This paperintroduces the current (2012–2017) USGS–NASA Landsat
Science Team (LST) efforts to establish an initial understanding of
Landsat 8 capabilities and the steps ahead in support of science team
identified priorities. These priorities and the purpose and focus of the
current LST are first introduced. This is followed by an overview of the
Landsat 8 mission objectives, sensors, orbit, data acquisition, and stan-
dard data products to provide context for the subsequent sections. Pre-
liminary evaluation of Landsat 8 capabilities and identification of new
science and applications opportunities are highlighted, followed by in-
sights into the development of derived ‘higher-level’Landsat products,
international synergies between Landsat and other moderate resolution
remote sensing satellites, and a conclusion that includes consideration
of successor Landsat mission requirements.
2. Landsat 8 Science Team
This paper is authored by members and affiliates of the current LST.
There have been several LSTs, each selected through a competitive
proposal review process to serve a five-year term funded by the USGS
and/or NASA. The science teams were charged to provide feedback on
critical design issues, including functional performance specifications
of the Landsat instruments, data systems and data formats that affect
Landsat data users, and to consider interoperability of Landsat with
other planned and in orbit remote sensing systems, and to provide in-
sights on future missions. The previous LST (2005–2011) provided jus-
tification for making the U.S. Landsat data archive available at no cost,
recommended strategies for the effective expansion and use of the ar-
chived Landsat data, and investigated the requirements for Landsat 8
to meet the needs of users including policy makers (Woodcock et al.,
2008; Wulder & Masek, 2012). The LST prior to that (1996–2001) was
formulated as part of the Landsat 7 development phase in a period
when Landsat 5 was the only operating Landsat due to the 1993 Landsat
6failure(Goward et al., 2006; Irons & Masek, 2006). It developed a
Landsat 7 long-term data acquisition plan, undertook research to devel-
op methods to analyze Landsat data for global change studies, and
evaluated the data quality acquired by Landsat 7 after it was launched
in April 1999 (Arvidson, Gasch, & Goward, 2001; Goward, Masek,
Williams, Irons, & Thompson, 2001).
The current LST (2012–2017) was selected with an aim to represent
the breadth of Landsat user perspectives and their requirements. The
LST is comprised of 21 principal investigator-lead teams of scientists
and engineers drawn from academia, U.S. Federal science and mission
agencies, and includes representation from non-U.S. institutions to en-
sure an international perspective. The majority of the science team
members have expertise in processing and characterizing Landsat data
and/or expertise using Landsat data for a specific application domain.
The LST met prior to and shortly after Landsat 8 launch and established
the following four core priorities for the next five years:
(1) evaluation of Landsat 8 capabilities and identification of new sci-
ence and applications opportunities,
(2) development of strategies and prototype approaches for the de-
velopment of ‘higher-level’derived Landsat science products
needed in support of global change research,
(3) identification of international land imaging constellation
opportunities,
(4) definition of science and applications requirements for
succeeding Landsat missions for operational long-term obser-
vational continuity.
155D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
Section 4 of this paper focuses on the first priority, which is most im-
mediate and is described in the hierarchy of processing required to
transform Landsat 8 data into derived products and provide core appli-
cations. Sections 5 and 6 reflect current LST perspectives on the second
and third prioritiesrespectively as they are evolving. The conclusion in-
cludes consideration of successor Landsat mission requirements.
3. Landsat 8 overview
3.1. Mission objectives
The primary Landsat 8 mission objective is to extend the Landsat re-
cord into the future and maintain continuity of observations so that
Landsat 8 data are consistent and comparable with those from the pre-
vious Landsat systems. Landsat supports the global data and informa-
tion needs of the NASA Earth Science program, which seeks to develop
ascientific understanding of the Earth system and its response to natu-
ral and human-induced changes to enable improved prediction of
climate, weather, and natural hazards (Irons, Dwyer, & Barsi, 2012;
NRC, 2007). The Landsat legacy has been relatively consistent in mission
objectives, with capabilities modified by incremental improvements in
satellite, sensor, transmission, reception, data processing, and data dis-
tribution technologies. Landsat currently provides an integral role in
NASA's multi-scale global observing strategy. Notably, the NASA Terra
satellite was placed in the same morning orbit as Landsat 7 to provide
opportunities for multi-scale global land surface change monitoring
(Skole, Justice, Janetos, & Townshend, 1997), particularly using data
from the Moderate Resolution Imaging Spectroradiometer (MODIS)
and the Advanced Spaceborne Thermal Emission and Reflection Radi-
ometer (ASTER) that include Landsat heritage spectral bands (Justice
et al., 1998; Yamaguchi, Kahle, Tsu, Kawakami, & Pniel, 1998). Landsat
also supports the national data and information needs of the USGS na-
tional science strategy (USGS, 2007) and USGS initiatives such as the
U.S. National Land Cover Database (Fry et al., 2011) that are reliant on
the U.S. Landsat data coverage.
Landsat 8 is a science mission, and as for the previous Landsat sys-
tems, has no operational mandate (Wulder, White, Masek, Dwyer, &
Roy, 2011). Specifically, this means that if the current Landsat 8 system
fails there will be no quick replacement with another Landsat. The de-
velopment of the Landsat 8 mission is reviewed in Irons et al. (2012).
In 2005 NASA began planning for a free-flyer government-only Landsat
7 successor mission with the following objectives (Irons et al., 2012):
(a) collect and archive moderate-resolution, reflective multispectral
image data affording seasonal coverage of the global land mass
for a period of no less than five years;
(b) collect and archive moderate-resolution, thermal multispectral
image data affording seasonal coverage of the global land mass
for a period of no less than three years;
(c) ensure that the data are sufficiently consistent with data from
the earlier Landsat missions, in terms of acquisition geometry,
calibration, coverage characteristics, spectral and spatial charac-
teristics, output product quality, and data availability, to permit
studies of land cover and land use change over multi-decadal
periods;
(d) distribute standard data products on a nondiscriminatory basis
and at no cost to users.
These four objectives, the lessons learned from the Earth Observing-
1 mission that demonstrated the advantages and challenges associated
with a multispectral optical wavelength pushbroom sensor (Ungar,
Pearlman, Mendenhall, & Reuter, 2003), and recommendations from
the previous Landsat Science Teams provided the basis for the Landsat
8 capabilities and performance specifications that are summarized in
the remainder of this Section.
3.2. Landsat 8 sensor overview
The Landsat 8 satellite carries a two-sensor payload, the Operational
Land Imager (OLI) and the Thermal Infrared Sensor (TIRS), which are
described in detail in Irons et al. (2012) and are summarized in
Table 1. The OLI and TIRS spectral bands remain broadly comparable
to the Landsat 7 Enhanced Thematic Mapper plus (ETM+) bands. Com-
pared to the ETM+, the OLI has two additional reflective wavelength
bands: a new shorter wavelength blue band (0.43–0.45 μm) intended
for improved sensitivity to chlorophyll and other suspended materials
in coastal waters and for retrieving atmospheric aerosol properties,
and a new shortwave infrared band (1.36–1.39μm) for cirrus cloud de-
tection. The other OLI bands are spectrally narrower in most cases than
the corresponding ETM+ bands. In particular, the OLI near-infrared
(NIR) band is closer in width to the MODIS NIR band and avoids the
0.825 μm water vapor absorption feature that occurs in the ETM+ NIR
band. The TIRS senses emitted radiance in two 100 m thermal infrared
bands, compared to the high and low gain single thermal infrared 60
m ETM+ band. The reduced TIRS spatial resolution is not optimal but
was necessitated by engineering cost restrictions. However, the two
thermal TIRS bands enable thermal wavelength atmospheric correction
and more reliable retrieval of surface temperature and emissivity.
The OLI and TIRS designs incorporate technical advancements that
improve their performance over the previous Landsat sensors. Signifi-
cantly, like the Advanced Land Imager (ALI) on EO-1, both the OLI and
TIRS are pushbroom sensors with focal planes aligninglong arrays of de-
tectors across-track. This provides improved geometric fidelity, radio-
metric resolution and signal-to-noise characteristics (Irons et al.,
2012) compared to the whisk-broom sensor technology used by previ-
ous Landsat instruments (Lee, Storey, Choate, & Hayes, 2004) and by
MODIS (Wolfe et al., 2002). The OLI band signal-to-noise ratios exceed
those achieved by the Landsat ETM + by a factor of at least eight
(Irons et al., 2012). These improvements enable the OLI and TIRS
analog-to-digital converters to quantize the sensed radiance into 12
bits (4096 levels) of meaningful data, rather than the 8 bits (256 levels)
used by Landsat ETM +. The greater 12-bit quantization permits im-
proved measurement of subtle variability in surface conditions. Calibra-
tion coefficients for all Landsat sensors are configured to globally
maximize the range of land surface radiance in each spectral band
(Markham, Goward, Arvidson, Barsi, & Scaramuzza, 2006). The dynamic
range of the OLI is improved compared to previous Landsat sensors, re-
ducing band saturation over highly reflective surfaces such as snow or
cloud.
3.3. Landsat 8 orbit and data acquisition
The Landsat 8 satellite is in the same near-polar, sun-synchronous,
705 km circular orbit and position as the recently decommissioned
Landsat 5 satellite. Landsat 8 data are acquired in 185 km swaths and
segmented into 185 km × 180 km scenes defined in the second
World-wide Reference System (WRS-2) of path (groundtrack parallel)
and row (latitude parallel) coordinates also used by the Landsat 4, 5,
and 7 satellites (Arvidson et al., 2001). Landsat 8 has a 16 day repeat
cycle; eachWRS-2 path/row is overpassedevery 16 days and may be ac-
quired a maximum of 22 or 23 times per year, as for Landsat 4, 5 and 7.
Combined, the Landsat 8 and 7 sensors provide the capability to acquire
any WRS-2 path/row every 8 days at the Equator and more frequent
coverage at higher latitudes due to the poleward convergence of the
Landsat orbits (Kovalskyy & Roy, 2013).
The amount of Landsat data in the U.S. Landsat archive has not been
constant among Landsat sensors, from year to year, or geographically,
because of differing Landsat data acquisition strategies, data reception
capabilities, and system health issues (Goward et al., 2006; Loveland &
Dwyer, 2012; Markham, Storey, Williams, & Irons, 2004). Landsat 7
was the first Landsat mission that adopted a systematic acquisition
plan in 1999, and Landsat 7 data continue to be acquired systematically
156 D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
in an attempt to refresh annually the U.S. Landsat archive with sunlit,
substantially cloud-free acquisitions that capture seasonal land surface
dynamics (Arvidson, Goward, Gasch, & Williams, 2006). Globally, out-
side of the conterminous United States, however, only a fraction of the
potential Landsat 7 WRS-2 path/rows are acquired (Arvidson et al.,
2006; Ju & Roy, 2008).
The Landsat 8 data acquisition plan seeks to directly benefit global
studies by acquiring the majority of the land WRS-2 paths/rows
overpassed each day. The Landsat8 satellite has improved high capacity
onboard recording and satellite to ground transmission capabilities
compared to previous Landsat systems. The data are transmitted via
X-band to three primary ground receiving stations located at Gilmore
Creek in Alaska, Svalbard in Norway, and at the USGS Earth Resource
Observation and Science (EROS) Center in theU.S. International cooper-
ator receiving stations, typically national space and mapping agencies,
may receive real time Landsat 8 data transmissions within line-of
sight of the satellite.Unlike previous Landsat missions, all of the Landsat
8 data available to the international cooperator receiving stations are
stored and transmitteddirectly to the primary ground receiving stations
and added to the U.S Landsat archive (Loveland & Dwyer, 2012). Ap-
proximately 60% more Landsat 8 scenes are acquired per day compared
to Landsat 7. This improved data acquisition provides near-global sea-
sonal coverage and the possibility to generate global Landsat data sets
with adjacent cloud-free path/rows acquired only months apart, partic-
ularly if combined with data from other contemporaneous Landsat and
Landsat-like sensors (Kovalskyy & Roy, 2013).
3.4. Landsat 8 Level 1 data product
Landsat 8 data are nominally processed into 185 km × 180 km Level
1 terrain-corrected (L1T) products that have a typical 950 MB com-
pressed GeoTiff file size —more than twice that of previous Landsat sen-
sor L1T products. All the OLI and TIRS spectral bands are stored as
geolocated 16-bit digital numbers in the same L1T file. The 100 m TIRS
bands are resampled by cubic convolution to 30 m and co-registered
with the 30 m OLI spectral bands. An associated metadata file stores
spectral band gain and offset numbers that can be used to linearly con-
vert the digital numbers to at-sensor radiance (W m
−2
sr
−1
μm
−1
)and
to convert the OLI digital numbers to at-sensor reflectance (unitless). In
this way users do not need to perform the non-linear transformation
from radiance to reflectance which can be challenging for less experi-
enced users (Roy et al., 2010). The Landsat 8 L1T product also provides
a spatially explicit data quality assessment file that indicates the proba-
bility of clouds defined using a supervised classification algorithm
(Scaramuzza, Bouchard, & Dwyer, 2012), terrain occlusion, and the
presence of dropped data for each 30 m pixel location. The L1T products
are defined in the Universal Transverse Mercator (UTM) map projection
with World Geodetic System 84 (WGS84) datum to be compatible with
heritage Landsat data, including Landsat 1–5 Multi-Spectral Scanner
(MSS) data (Tucker, Grant, & Dykstra, 2004). The L1T data for
Antarctica are defined in the Polar Stereographic projection to reduce
polar map projection distortions.
The Landsat 8 L1T data processing includes radiometric calibration,
systematic geometric correction, precision correction assisted by
ground control chips, and the use of a digital elevation model to correct
parallax error due to local topographic relief (Lee et al., 2004; Storey,
Lee, & Choate, 2008). The radiometric calibration approach is described
in Section 4. The Landsat 8 L1T products have improved geometric fidel-
ity because of the Landsat 8 pushbroom sensor design and because the
satellite has a fully operation al onboard global positioning sy stem (GPS)
to measure the exterior orientation directly, rather than inferring it
from ground control chips as with previous Landsat geolocation algo-
rithms. Geolocation accuracy is improved, particularly in regions of un-
structured and featureless terrain and over cloudy WRS-2 path/rows
where ground control chip availability is normally reduced (Roy et al.,
2010; Wolfe et al., 2002). The Landsat 8 L1T product has a 90% confi-
dence level OLI to TIRS co-registration uncertainty requirement of
b30 m, which is needed for applications that use both the reflective
and emitted radiance, and a circular geolocation error uncertainty re-
quirement of b12 m (Irons et al., 2012). These geometry improvements
will enable more accurate mapping and monitoring applications, the
generation of less smoothed temporally composited Landsat 8 data
products (Roy, 2000), and more accurate multi-temporal change detec-
tion (Townshend, Justice, Gurney, & McManus, 1992).
4. Preliminary Landsat science team evaluation of Landsat 8
capabilities and identification of new science and
applications opportunities
4.1. Calibration and radiometric characterization
Prior to launch, the OLI and TIRS were subject to rigorous pre-launch
testing and measurements to characterize their radiometric, spectral,
spatial and environmental parameters (Markham et al., 2008; Thome
et al., 2011). This knowledge is used to provide the relationship
between the at-sensor radiance (W m
−2
sr
−1
μm
−1
) and the digital
numbers output for each pixel and each spectral band. Also pre-
launch, NASA and the European Space Agency (ESA) performed a
national laboratory traceable cross-calibration comparison of the OLI
and the Landsat-like multi-spectral instrument (MSI) that will be on
the Sentinel-2 satellites (ESA, 2013) to ensure that their data will be
cross-calibrated.
Calibration and radiometric characterization is included as a LST ac-
tivity because of the post-launch degradation that can occur in the rela-
tionship between the at-sensor radiance and the recorded digital
numbers, and the need to ensure consistency with archived Landsat
data. On-board Landsat 8, calibration is undertaken every orbit; the
TIRS radiometric response is examined by consideration of deep space
Table 1
Comparison of Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) bands with the Landsat 7 Enhanced Thematic Mapper Plus (ETM+)bands.
Landsat 8 Landsat 7
Band description (30 m native resolution unless
otherwise denoted)
Wavelength
(μm)
Band description (30 m native resolution unless
otherwise denoted)
Wavelength
(μm)
Band 1 —blue 0.43–0.45
Band 2 —blue 0.45–0.51 Band 1 —blue 0.45–0.52
Band 3 —green 0.53–0.59 Band 2 —green 0.52–0.60
Band 4 —red 0.64–0.67 Band 3 —red 0.63–0.69
Band 5 —near infrared 0.85–0.88 Band 4 —near infrared 0.77–0.90
Band 6 —shortwave infrared 1.57–1.65 Band 5 —shortwave infrared 1.55–1.75
Band 7 —shortwave infrared 2.11–2.29 Band 7 —shortwave infrared 2.09–2.35
Band 8 —panchromatic (15 m) 0.50–0.68 Band 8 —panchromatic (15 m) 0.52–0.90
Band 9 —cirrus 1.36–1.38
Band 10 —thermal Infrared (100 m) 10.60–11.19 Band 61 —thermal Infrared (60 m) 10.40–12.50 (high gain)
Band 11 —thermal Infrared (100 m) 11.50–12.51 Band 62 —thermal Infrared (60 m) 10.40–12.50 (low gain)
157D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
and on-board blackbody observations, and the OLI response is exam-
ined by observations of on-board solar diffuser panels. Due to the rigors
of launch and the harsh space environment, on-board calibration is sup-
plemented with vicarious techniques that use ground-based measure-
ments to predict sensor outputs (Schott et al., 2012; Slater et al.,
1987). For Landsat 8 a suite of globally distributed well-characterized
sites based on previous Landsat vicarious calibration studies are being
used (Helder et al., 2013). Other sensors will be cross-calibrated with
Landsat 8 by comparison of near-simultaneous sensor observations
over these sites. In addition, Landsat 8 vicarious calibration is being un-
dertaken by sensing the moon, which is a known target with minimal
atmospheric contamination (Kieffer et al., 2003), and will be used to de-
tect long-term sensor degradations.
Shortly after launch the Landsat 8 orbit was configured to under-fly
Landsat 7 to provide a vicarious cross-calibration opportunity. The
Landsat 7 ETM+ calibration is well defined, with 5% absolute reflective
band calibration uncertainty (Markham & Helder, 2012) and thermal
band uncertainties of approximately 0.6 K when expressed as a change
in apparent temperature of a 300 K surface (Schott et al., 2012). During
the under-flight, contemporaneous ground-based measurements were
made continuously for four days. In addition, an airborne sensor package
consisting of a hyperspectral imager, lidar, and thermal camera made
measurements over several sites (McCorkel, Thome, & Lockwood,
2013).
Together, these pre-launch, onboard and vicarious calibration tech-
niques established confidence in Landsat 8 calibration accuracy and
continuity with predecessor Landsats. Initial results indicate absolute
calibration within the pre-launch specification OLI requirements of 3%
reflectance and 5% radiance (Czapla-Myers, Anderson, & Biggar, 2013;
Markham, Irons, & Storey, 2013). Currently the TIRS data show an ap-
proximate 2% (band 10) and 4% (band 11) bias in absolute radiance
when compared to vicarious measurements from buoys in Lake Tahoe,
the Salton Sea, and deep oceans.This bias translatesto an approximately
2 K to 4 K over estimate when expressed as a change in the apparent
temperature of a 300 K surface and exceeds the 2% accuracy require-
ments for TIRS. Stray light from beyond the nominal 15° TIRS field of
view has been identified as the cause of the bias and a correction ap-
proach is beingstudied. Until these studies produce a more comprehen-
sive correction, the USGS plans to reprocess Landsat 8 data and subtract
0.29 W/m
2
/sr/um from TIRS band 10 and 0.51 W/m
2
/sr/um from TIRS
band 11 to improve the accuracy of the data products for surface tem-
peratures typical of mid-latitudes during the growing season, that is,
temperatures near the 280 K to 300 K range of the buoy observations.
On-orbitassessments alsoindicate that OLI andTIRS meet their geomet-
ric performance requirements with wide margins, giving confidence
that their data can be readily incorporated into the Landsat time series
(Markham et al., 2013).
4.2. Surface reflectance
The spectral bidirectional surface reflectance, i.e., derived top of
atmosphere (TOA) reflectance corrected for the varying scattering and
absorbing effects of atmospheric gases and aerosols is needed to moni-
tor the surface reliably (Kaufman, 1989). Although the OLI's spectral
bands are narrow and chosen to avoid atmospheric absorption features
(Section 3.2), atmospheric effects are still challengingto correct. In par-
ticular, the impact of aerosols can be difficult to correct because of their
complex scattering and absorbing properties that vary spectrally and
with the aerosol size, shape, chemistry and density (Dubovik et al.,
2002). A number of atmospheric correction methodologies have been
developed, but those using radiative transfer algorithms and atmo-
spheric characterization data provide the most potential for automated
large-area application (Vermote, El Saleous, & Justice, 2002; Roy et al.,
2014). For example, the MODIS reflective wavelength bands have
been atmospherically corrected using the 6SV radiative transfer code
to generate global daily and 8-day surface reflectance products since
2000 (Vermote et al., 2002). The 6SV code has also been implemented
for correction of Landsat TM and ETM+ data (Masek et al., 2006). The
approach relies on inverting the aerosol effect, using the bands centered
at the shortest (blue) wavelengths where the surface reflectance is gen-
erally small and the aerosol signal strong. Consequently, the accuracy
of the blue and green surface reflectance is lower than in the longer
wavelengths, and these bands should be used with caution (Vermote
& Kotchenova, 2008; Ju, Roy, Vermote, Masek, & Kovalskyy, 2012).
A Landsat 8 OLI land surface atmospheric correction algorithm is
being prototyped using the 6SV approach refined to take advantage of
the narrow OLI spectral bands, improved radiometric resolution and
signal to noise, and the new OLI blue band (0.43–0.45 μm). The new
blue band is particularly helpful for retrieving aerosol properties, as it
has shorter wavelength than the conventional OLI, TM and ETM +
blue bands. Fig. 1 shows results of preliminary tests applied to a Landsat
8 OLI scene acquired over Washington D.C.and Baltimore. The truecolor
surface reflectance and uncorrected TOA reflectance are shown in
Fig. 1a and b respectively, and illustrate the impact of the correction at
visible wavelengths. Fig. 1c shows the extent of cirrus cloud depicted
by the shortwave infrared band (1.36–1.39μm) sensor that was specifi-
cally added to the OLI for this purpose. This band is being evaluated as
an atmospheric correction pre-filter, as aerosol properties near clouds
can be very different than far from clouds (Tackett & Di Girolamo,
2009). Figs. 2 and 3 illustrate full resolution surface reflectance images
extracted from Fig. 1a over Dulles Airport and Baltimore Inner Harbor,
respectively. The Landsat 8 OLI 15 m panchromatic band was used to
pan sharpen (Tu, Huang, Hung, & Chang, 2004) these two images. The
improved OLI 12-bit radiometric resolution, particularly over water,
and the high level of geographic detail provided by the OLI, are clearly
apparent.
4.3. Surface albedo
The surface albedo –the proportion of solar energy that is reflected
by the Earth's surface –is an essential climate variable describing the
energy available to drive atmospheric, land, oceanic, and cryospheric
temperature and evaporation regimes as well asvegetative evapotrans-
piration, photosynthesis, and carbon assimilation (Schaaf, Cihlar,
Belward, Dutton, & Verstraete, 2009; Schaaf, Liu, Gao, & Strahler, 2011,
chap. 24). Long-term surface albedos are required by climate, biogeo-
chemical, hydrological, and weather forecast models at a range of spatial
and temporal scales. The albedo varies as a result of solar illumination,
snowfall, inundation, vegetation growth, littoral variation, and with
natural and anthropogenic disturbance and land cover and land use
change. Improving the spatial detail and precision of surface albedo
measures using Landsat is especially important in understanding the
impacts of local land cover change. Variations in surface albedo, partic-
ularly in areas of permanentand seasonal snow cover, have been shown
to play a significant role in the radiative forcing of the Earth system and
represent a significant contributor to ongoing changes in the terrestrial
energy balance (Barnes & Roy, 2010;Flanner, Shell, Barlage, Perovich, &
Tschudi, 2011; Myhre, Kvalevag, & Schaaf, 2005).
Remote sensing offers the only viable method of measuring and
monitoring the global heterogeneity of albedo (GCOS, 2004; Schaaf
et al., 2009). Surface albedo cannot be retrieved directly from Landsat
spectral bidirectional surface reflectance because of the narrow field of
view, which precludes sampling of the intrinsic reflectance anisotropy
of most land surfaces. However, 30 m surface albedo can be derived
using the coarser resolution (500 m) MODIS Bidirectional Reflectance
Distribution Function (BRDF) product (Schaaf et al., 2002, 2011, chap.
24) to capture the generalized surface anisotropy of different surface
land covers, and then coupling these with concurrent 30 m Landsat
spectral bidirectional surface reflectance (Shuai, Masek, Gao, & Schaaf,
2011). This approach is beingused to develop a Landsat 8 OLI surface al-
bedo algorithm to derive spectral and broadband intrinsic albedo values
(white-sky albedo and black-sky albedo). The improved OLI radiometry
158 D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
and atmospheric correction will provide more reliable spectral albedo,
and the greater number of OLI spectral bands will contribute to im-
proved broadband albedo values. In addition, the new OLI blue band
may improve albedo retrieval over littoral areas, to help characterize es-
tuaries, mangroves and coral reefs.
4.4. Surface temperature, evapotranspiration and drought
Land surface temperature is one of the key variables needed to de-
scribe surface states and processes critical in studies of climate, hydrol-
ogy, ecology, biogeochemistry and human health (Kalma, McVicar, &
McCabe, 2008; Quattrochi & Luvall, 2004). Prior to Landsat 8, the land
surface temperature could not be derived reliably without use of ancil-
lary data because of the availability of only a single Landsat thermal
wavelength band. The two thermal TIRS bands are spectrally similar to
two of the 1 km MODIS thermal bands and enable, for the first time, at-
mospheric correction of Landsat thermal imagery using split-window
techniques. This will provide simpler andmore accurate retrieval of sur-
face temperature and emissivity than was possible with previous
Landsat sensor data.
Landsat provides the spatial resolution and continuous record
needed to capture time histories of water consumption at the scale of
human use, critically, at the scale of typical agricultural fields. Landsat-
based field-aggregated evapotranspiration (ET) is currently being used
in a number of U.S states for water rights management (Sullivan,
Huntington, & Morton, 2011) and for conjunctive management of
surface and ground-water (Anderson, Allen, Morse, & Kustas, 2011;
Burkhalter et al., 2013), and in other countries to estimate non-
sustainable ground-water depletion (Santos, Lorite, Allen, & Tasumi,
2012). Water budgets, groundwater modeling, and expert evidence
submitted for water rights hearings have relied on Landsat to identify
areas and quantities of groundwater discharge consumed by natural veg-
etation and to define the perennial water yield (Allen, Tasumi, Morse,
et al., 2007; Burns & Drici, 2011; NSEO, 2012). Many groundwater dis-
charge areas are small or narrow and have high spatial variability, requir-
ing Landsat's spatial resolution to estimate spatially representative and
accurate ET fluxes. Fig. 4 shows a map of ET produced from Landsat 8
for a coastal area of California about 150 km south of where Landsat 8
was launched. The center image is relative ET produced by the METRIC
surface energy balance application (Allen, Tasumi, & Trezza, 2007),
expressed as a fraction of maximum, weather-based reference ET
(ETrF) for the day, shown at the native 100 m TIRS resolution. The
right image is relative ET produced by METRIC following a ‘sharpening’
of the thermal data to the 30 m OLI resolution, achieved by associating
variations in surface temperature and shortwave OLI vegetation indices
(Trezza et al., 2008). Outlines of areas of water consumption associated
with individual fields become clearer after thermal sharpening, and the
consistency of ET between field edges and field centers supports the use-
fulness of the sharpening to better define total water consumption asso-
ciated with individual fields and water rights.
Continental-scale thermal-based ETrF anomaly maps generated using
coarse-scale geostationary satellite thermal data have been shown to
provide a robust and high-resolution alternative to precipitation-based
indices for drought monitoring (Anderson, Hain, Wardlow, Mecikalski,
& Kustas, 2011; Anderson et al., 2013). Landsat scale ET time-series pro-
vide a unique opportunity to investigate field-scale vegetation stress and
to study how drought vulnerability varies spatially with plant-functional
type, land/water management and edaphic condition, and to enhance
drought early warning systems for targeted mitigation efforts and im-
proved yield estimation (Anderson, Kustas, et al., 2011). Fusion of ET
data-streams from Landsat and daily MODIS data provide the
Fig. 1. a. Prototype Landsat 8 OLI surface reflectance product derived from a 185 km ×
180 km L1T image acquired over Chesapeake Bay, MD, USA, April 21 2013. True-color
composite of the OLI red (0.630–0.680 μm), green (0.525–0.600 μm) and blue (0.450–
0.515 μm) bands. b. Top of atmosphere true-color composite for the same scene and OLI
bands shown in Fig. 1a. Illustrated using the same contrast stretch as for Fig. 1a. c. Cirrus,
and also other clouds , apparent in the new OLI cirrus band (1.360–1.390 μm) for the
scene shown in Fig. 1a and b.
159D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
opportunity to enable more timely detection of incipient crop stress
(Cammalleri, Anderson, Gao, Hain, & Kustas, 2013). Landsat 8 will contin-
ue to serve as the primary data source for the development of technolo-
gies for estimating cumulative water consumption over growing-season
timescales, for estimating groundwater discharge, perennial water yield,
and for monitoring changes in water use and availability as populations
increase and pressures to develop new freshwater resources rise.
4.5. Agriculture
Landsat-based agricultural applications were developed shortly
after the launch of Landsat 1 and have been subject to multi-agency
funded support through initiatives including the Large Area Crop Inven-
tory Experiment (LACIE) (MacDonald, Hall, & Erb, 1976)andtheAgri-
culture and Resources Inventory Surveys through Aerospace Remote
Sensing (AgRISTARS) Program (AgRISTARS Program Support Staff,
1981). Landsat 8 will build upon this rich history, adding to the tempo-
ral archive of agricultural change information and allowing for contin-
ued monitoring.
The U.S. Department of Agricultural (USDA) uses Landsat and
Landsat-like satellite data to monitor cropping systems domestically
and abroad. A flagship example is the Cropland Data Layer (CDL)
which is annually generated to define over 100 land cover and crop
type classes at 30 m for all the conterminous U.S. It is used primarily
to help estimate crop area of dominant commodities and supplements
information from ongoing ground-based surveys (USDA, 2013).
The CDL is generated using a supervised classification approach with
extensive agricultural ground truth (Boryan, Yang, Mueller, & Craig,
2011; Johnson & Mueller, 2010) applied to a variety of sensor data in-
cluding from Landsat 5 TM, Landsat 7 ETM+, Indian Remote Sensing
Resourcesat-1 Advanced Wide Field Sensor (AWiFS), and Disaster
Monitoring Constellation (DMC) Demios-1 and UK-2 (Johnson, 2008).
Non-U.S. satellite data have been utilized to complement Landsat data
because they have a more frequent revisit rate (less than 16 days) and
do not suffer from the Landsat 7 ETM+ scan line corrector problem
(Markham et al., 2004). The USDA has already begun integrating
Landsat 8 data into the CDL generation to benefit from the greater
data coverage and the improved OLIradiometric resolution and spectral
band locations. Fig. 5 shows CDL for 2012 and 2013made with DMC and
with Landsat 8 OLI data respectively. It is anticipated that because of the
improved radiometric resolution and spectral characteristics combined
with the greater number of images collected, Landsat 8 will be a key
data source for USDA programs and reduce organizational reliance on
non-US satellite assets that have incremental costs and processing
requirements.
Satellite-based crop yield estimation is also of interest to the USDA
and others because in situ surveys are expensive and often do not
capture rapid within season changes. Furthermore, in many regions
Fig. 2. Full resolution depiction of Dulles Airport, in Virginia just west of Washington, DC, extracted from the OLI surface reflectance image (Fig. 1a). The OLI panchromatic band
(0.500–0.680 μm) has been used to sharpen the resolution of thered, green and blue bands. A region of 9 km × 9 km is shown.
160 D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
there are no reliable crop condition statistics (Becker-Reshef et al.,
2010), which is of concern because crop supply uncertainty often in-
creases commodity price volatility (Food & Agriculture Organization,
FAO of the United Nations, 2011). To date, coarse spatial resolution sat-
ellite data have been used predominantly, and satellite-derived yield
products remain in the research domain (Bolton & Friedl, 2013;
Doraiswamy et al., 2005). Landsat provides the appropriate field scale
spatial resolution and continuous record needed to capture crop growth
in many regions of the world (Doraiswamy et al., 2004; Gitelson et al.,
2012; Lobell, Ortiz-Monasterio, Asner, Naylor, & Falcon, 2005), particu-
larly in regions with smaller field sizes or mosaic plantings. However,
the 16-day Landsat revisit combined with cloud cover can limit the reli-
able documentation of phenology compared to coarse resolution near
daily coverage polar orbiting systems such as MODIS (Kovalskyy, Roy,
Zhang, & Ju, 2011), which is critical for monitoring growing season
crop productivity.
Field-level, and within-field, monitoring is important for the early
detection of plant disease and weed infestation, to assess the efficacy
of agricultural management practices, and to monitor the impacts of
ephemeral meteorological events. Although conventionally undertaken
with airborne and high resolution satellite data, the improved geomet-
ric and radiometric fidelity of Landsat 8 OLI dataare expected to provide
new opportunities in this domain. Certainly the water consumption as-
sociated with individual fields (Fig. 4) and the use of pan sharpening
with the 15 m panchromatic band (Figs. 2 and 3) indicate potential for
within field monitoring, particularly in large scale mechanized agricul-
tural areas.
Landsat 8 data are now part of the satellite data collections used by
the USDA, and are likely to be used by other international governmental
agencies, agribusiness, and individual agricultural producers. Integra-
tion with Landsat 7, alongside other sensors, such as AWiFS, DMC and
Sentinel-2, will allow for the generation of denser temporal profiles of
croplands to enhance agricultural monitoring efforts. However, the
biggest improvement from Landsat 8 will most likely come from the im-
proved global collection capacity, allowing improved ability to map
global agricultural regions consistently and in greater detail.
4.6. Land cover, condition, disturbance and change
Landsat data have been used since the beginning of the Landsat pro-
gram to monitor the status and changes of the Earth's land cover and
condition and have become the core of many institutional large-area
land cover mapping and monitoring initiatives. Additionally, the combi-
nation of free Landsat data and theincreasing affordability of computer
Fig. 3. Full resolution depiction of Baltimore, Maryland, Inner Harbor extracted from the OLI surface reflectance image (Fig. 1a). The OLI panchromatic band (0.500–0.680 μm) has been
used to sharpen the resolution of the red, green and blue bands. A region of 9 km × 9 km is shown.
161D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
processing and storage hardware have catalyzed a blossoming of novel
research in both mapping and change detection. Landsat 8 continuity
extends the record that is the foundation for land change investigations,
and preserves the institutional investments made by land change sci-
ence and applications programs.
4.6.1. Systematic institutional large-area land cover mapping and
monitoring initiatives
US Federal agencies have relied on Landsat data for operational land
cover mapping since the 1990's. The United States Multi-Resolution
Land Characteristics (MRLC) Consortium was initiated during the Landsat
commercial in 1994 to cost-share the compilation of a national dataset of
processed Landsat scenes for use in various agency land cover projects.
The USGS National Land Cover Dataset (Fry et al., 2011), the USGS Gap
Analysis Project natural vegetation mapping project (Jennings, 2000),
and the NOAA Coastal Change Analysis Program (C-CAP) coastal zone
land cover mapping (Dobson et al., 1995) were the initial products of
the joint data buy. These initiatives continue, and now include the Land-
scape Fire and Resource Management Planning Tools Project (LANDFIRE)
(Rollins, 2009; Vogelmann et al., 2011) and the USDA Cropland
Data Layer (see Section 4.5). Other countries have adopted Landsat for
national mapping activities. Canada used circa 2000 Landsat TM and
ETM+ data to produce the Earth Observation for Sustainable Develop-
ment map of forests (EOSD) (Wulder et al., 2008) and this detailed prod-
uct plays a role in a variety of Canadian applications including the
National Forest Inventory, forest fragmentation assessment (Soverel,
Coops, White, & Wulder, 2010), and conservation protection (Andrew,
Wulder, & Coops, 2012; Cardille, White, Wulder, & Holland, 2012). The
Australian Landsat archive currently underpins natural resource manage-
ment at state levels (Armston, Denham, Danaher, Scarth, & Moffiet, 2009)
and supports national scale carbon inventories (Lehmann, Wallace,
Caccetta, Furby, & Zdunic, 2013) using Geoscience Australia surface re-
flectance Landsat 5 and Landsat 7 data (Li et al., 2010).
Landsat data anchor international tropical forest monitoring
efforts. NASA's Landsat Pathfinder Program laid the foundation for
large-area Landsat mapping of tropical regions (Justice et al., 1995;
Skole & Tucker, 1993). The operational PRODES Project (Projeto de
Monitoramento do Desflorestamento na Amazonia Legal), conducted
by Brazil's National Institute for Space Research (INPE), has been using
Landsat data to monitor deforestation rates across the Brazilian Amazon
annually since 1988 (INPE, 2013; Shimabukuro, Batista, Mello, Moreira,
& Duarte, 1998). The Observatoire Satellital des Forêts d'Afrique
Centrale (OSFAC) initiative is using Landsat and other data to monitor
Congo Basin forests (Hansen et al., 2008). The European Commission's
Joint Research Centre TREES-III project uses a pan-tropical grid sam-
pling approach to characterize tropical forest loss for 20 by 20 km sam-
ple blocks of Landsat TM data from 2000 to 2010 (Achard et al., 2002;
Beuchle et al., 2011; Mayaux et al., 2013). Hansen et al. (2013) recently
documented a decade of global forest gains and losses using Landsat
ETM+ data. As a result of the success of these and other tropical forest
monitoring investigations,Landsat data are a key input to the UN-REDD
(Reducing Emissions from Deforestation and forest Degradation) Pro-
gramme that was launched in 2008 to support the measurement,
reporting and verification of forest cover and carbon stocks in develop-
ing countries (GOFC-GOLD, 2012).
Landsat 8 data will enable these initiatives to continue, thereby
ensuring relevance to their resource management, policy, research
and application user communities. In particular, the continuity of the in-
formation content of the Landsat TM and ETM+ reflective wavelength
bands, including the short-wave infrared bands to improve discrimina-
tion of bare soil from non-photosynthetic vegetation (Ustin, Roberts,
Gamon, Asner, & Green, 2004) and to distinguish broadleaf and ever-
green forest types (Horler & Ahern, 1986), is critical. In a classification
Fig. 4. A 11 km × 22 km coastal area near Ventura, California from a Landsat 8 L1T image acquired May 4 2013. Left: false color image of OLI short-wave infrared (1.57–1.65 μm), near-
infrared (0.85–0.88 μm) and red (0.64–0.67 μm) expressed as red, green, blue, Center: relative ET produced by METRIC surface energy balance application using native resolution 100
m TIRS thermal bands, Right: relative ET produced by METRIC following ‘sharpening’of the TIRS thermal data.
162 D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
context, the improved OLI signal-to-noise ratio promises to enable bet-
ter discrimination of low reflectance targets, and to improve discrimina-
tion among various soil and non-photosynthetic vegetation targets. The
new shortwave infrared band will improve detection of cirrus clouds
(Fig. 1c) and facilitate more reliable land cover mapping. This is partic-
ularly important in persistently cloudy areas, where less reliable cloud
detection has necessitated aggressive cloud masking and reliance on
temporal compositing of Landsat data from very different time periods
(Lindquist, Hansen, Roy, & Justice, 2008). International studies, particu-
larly in the tropics and high latitudes, will also benefit from the in-
creased daily image acquisition capacity and improved image geodetic
properties, the latter should enable more accurate image geometry in
cloudy regions. In addition, the Landsat 8 OLI panchromatic band has a
narrower bandpass compared to previous Landsat sensors to provide
greater contrast between vegetated and bare surfaces, and will enhance
classification training and validation data collection.
4.6.2. Emerging mapping and change detection approaches
Although spatial and spectral properties have made Landsat data the
workhorse of land surface characterization, the opening of the Landsat
archive has fostered new analytical time series approaches for describing
land surface condition and dynamics. The critical change has been a
movement from image-based to pixel-based analysis. Because Landsat
data are now free and readily available, users are able to develop ap-
proaches based upon using all the available Landsat images for a given
region and time period rather than just a select subset of cloud-free im-
ages. This has led to the development of new time-series change
characterization approaches and new cloud and shadow masking,
mosaicking, and temporal compositing approaches, whereby the highest
quality pixels from multiple acquisitions are utilized. For example, tem-
poral compositing approaches are now being used to select a best
Landsat observation from all the Landsat observations collected over
some reporting period to generate gridded weekly, monthly, seasonal
and annual composites (Roy et al., 2010) that have been used to map
conterminous U.S. 30 m percent tree cover, bare ground, and five year
tree cover loss and bare ground gain (Hansen et al., 2011; 2014). Recent
Landsat mapping and change detection approaches have focused on
quantifying land cover change over an unprecedented range of time
scales (Cohen, Zhiqiang, & Kennedy, 2010) and using novel time series
approaches (Zhu, Woodcock, & Olofsson, 2012; Brooks et al., 2013). For
example, the Vegetation Change Tracker (VCT) provides a set of auto-
mated algorithms designed to detect forest disturbance using Landsat
time-series (Huang et al., 2010)andhasbeenusedtoestimateannual
forest disturbance rates for the conterminous U.S. (Masek et al., 2013).
The LandTrendr algorithms capture abrupt disturbance events in forests
(Kennedy, Yang, & Cohen, 2010) and other land cover changes, allowing
linkage of disturbance rates with changes in policy and economic
conditions (Griffiths et al., 2012; Kennedy et al., 2012). All pixel-level
disturbance and change mapping approaches will benefitfromthe
improved quality and data coverage of Landsat 8. Landsat 8 data rep-
resent an extension of the unbroken Landsat spectral record, provid-
ing a set of relatively consistent spectral bands critical for capturing
many land cover processes and providing a baseline for temporal
comparison.
Fig. 5. Summer 2012 (left) and 2013(right) USDA/NASS Cropland Data Layer (CDL) for a 17 km × 24 km portion of southeastern South Dakota. The 2012 CDL was derived by supervised
classification of the red, green and near infrared wavelength 22 m Deimos-1 and UK-2 data. The 2013 CDL was derived by supervised classification of the 30 m Landsat 8 OLI reflective
wavelength bands. The northern part of the cityof Sioux Falls is shown as gray and islocated in the south of the image.The USGS Earth ResourcesObservation and Science (EROS) center,
home of the U.S. Landsat archive, is located in the northeast image corner.
163D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
4.7. Fresh and coastal water
Surface fresh water sources comprise a small fraction of the global
water pool, yet they are the foundation of life in terrestrial ecosystems.
Present knowledge on fresh water distributions is limited at regional
and global scales. Global databases of lakes, reservoirs and wetlands
exist, but they have not been generated in a systematic manner or
using the same data sources (Lehner & Döll, 2004). The Shuttle Radar
Topography Mission (SRTM) Water Body Dataset (SWBD) was devel-
oped to improve the quality of SRTM digital elevation products
(SWBD, 2005), and was subsequently improved by the global 250 m
MODIS water mask product (Carroll, Townshend, Dimiceli, Noojipady,
& Sohlberg, 2009). The 15 m and 30 m resolution of the Landsat 8 OLI,
combined with high global data availability, present a unique opportu-
nity to provide the first and most up-to-date global inventory of the
world's lakes at high spatial resolution and positional accuracy using re-
cent Landsat algorithms (Li & Sheng, 2012; Sheng & Li, 2011; Smith,
Sheng, MacDonald, & Hinzman, 2005). This global high-resolution lake
database is expected to contain millions of lakes with a size of one hect-
areorlarger(Downing et al., 2006; Meybeck, 1995).
In addition to measuring the extent of water bodies satellite data
have utility for water quality information retrieval. The Landsat TM
and ETM + sensors have limited capability to map water quality in
fresh and coastal waters and have been largely restricted to mapping
turbidity or single constituent variations (assuming all observed change
was due to only one constituent). (Olmanson, Bauer, & Brezonik, 2008;
Onderka & Pekárová, 2008). These limitations are due to the relatively
low signal-to-noise ratio (SNR) of previous Landsat sensors as well as
to the limited number of spectral bands in the visible region where
water quality spectral signatures are manifest.
Fig. 3 illustrates the quality of OLI data over the Baltimore Inner Har-
bor, using the heritage Landsat visible bands, illustrating qualitatively
the improved radiometric fidelity ofthese data over water. The SNR pos-
sible with OLI and the actual performance on orbit have considerably
exceeded expectations. This is illustrated in Fig. 6, comparing Landsat
8 OLI SNR values for a uniform sample of water in the Red Sea with a
similar sample taken from a Landsat 7 ETM+ image and the instrument
Fig. 6. Signal-to-Noise Ratio (SNR) for uniform water re gions extracted over a region
of uniformbrightness in theRed Sea from Landsat 8 OLI (circles) and Landsat7 ETM + (tri-
angles), along wi th the specified SNR for Landsat 8 at ty pical radiance (L) levels
(diamonds).
Fig. 7. Preliminary Landsat 8 cryospheric applications. Top: winter thermal TIRS 10.6–
11.2 m brightness temperatures (BT) over the East Antarctic Plateau near Dome
F acquired on 31 July 2013, showing low BT in clear-sky areas south of the dome summit
(elevation contours in green) and nearby Automatic Weather Stations (AWS), Middle:
glacier ice speed of a region of southeastern Alaska derived by surface feature tracking
from a pair of Landsat-8 OLI images acquired 12 July and 13 August 2013 (color bar at
right is in meters/day), Bottom: meltwaterlake depth map for a small region of northwest
Greenland from Landsat 8 OLI image acquired 18 July 2013.
164 D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
specifiedvalues at a typical radiance level. Thesehigh SNR values areex-
tremely important for water constituent mapping because the very low
signal from water causes variations in water quality to be lost in the
noise of low SNR systems (Gordon & Clark, 1981).
Atmospheric correction over coastal waters is particularly challeng-
ing because of the much lower SNR compared to land; consequently,
water-specific Landsat 8 atmospheric correction techniques are being
developed that take advantage of the new shorter wavelength blue
band (Gerace & Schott, 2012). Using simulated data and spectral
matching algorithms, Gerace, Schott, and Nevins (2013) demonstrated
that the combination of the new OLI blue band and the improved SNR
should reduce error in constituent retrieval values to about half of the
error expected from Landsat 7 ETM+, with most of this improvement
attributable to the improved SNR. These results were for the simulta-
neous retrieval of chlorophyll, colored dissolved organic material
(CDOM) and suspended material (SM) at the instrument specified
SNR. Given the SNR values observed on orbit (Fig. 6), the expected er-
rors should be again reduced by one half (total reduction to about one
fourth the ETM+ expected errors). As a result of these simulated stud-
ies and the initial observed OLI SNR values on orbit, we expect Landsat 8
to enable a new era of water quality monitoring in the critical coastal
and fresh water regions of the globe.
4.8. Snow and ice
Landsat cryospheric applications began with the earliest Landsat
missions, in particular with the extensive 1972–1975 Antarctic coverage
by Landsat 1, 2, and 3 (Swithinbank, 1988). Landsat data have remained
a central tool for documenting the profound changes throughout the
global cryosphere in the past 40 years (e.g., Bindschadler, Dowdeswell,
Hall, & Winther, 2001; Bindschadler et al., 2008; Williams, Ferrigno,
Swithinbank, Lucchitta, & Seekins, 1995). Cryospheric research will
benefit from the improved geometric and radiometric fidelity of
the Landsat 8 OLI visible bands, allowing subtle surface features of
the large ice sheets to be better mapped and tracked for flow velocity
(Bindschadler, 2003). High radiometric sensitivity in the TIRS bands
also holds promise for better mapping of summertime ocean surface
temperature in fjords adjacent to tidewater and floating-front glaciers,
providing insight into ice-ocean interactions that can dramatically
affect ice front retreat and flow speed (Mankoff, Jacobs, Tulaczyk, &
Stammerjohn, 2012; Motyka, Hunter, Echelmeyer, & Connor, 2003).
The TIRS data also have potential for mapping debris-cover thickness
over mountain glaciers through seasonal and diurnal variations in tem-
perature as thin debris cover shows a modified response to solar heating
due to the underlying ice (Bhambri, Bolch, & Chaujar, 2011; Shukla,
Arora, & Gupta, 2010).
Chief among the specific cryospheric issues that will be addressed
with Landsat-8 data is the determination of the net ice outflow of the
great ice sheets, including variation over time, and mapping the ice
flow velocity of the world's mountain glaciers. This is currently conduct-
ed by automated feature tracking in sequential time series images
(Berthier et al., 2005; Debella-Gilo & Kääb, 2011; Scambos, Dutkiewitcz,
Wilson, & Bindschadler, 1992). The increased global OLI data acquisition
will lead to more frequent cloud-free image pairs suitable for flow map-
ping, and the improved OLI data geometry and radiometry should im-
prove tracking accuracy and the ability to track ice flow in low-
contrast areas of glaciers and ice sheets (Bindschadler, 2003).
Examples of ongoing Landsat 8 applications to polar and glacier ice
study areas demonstrate several useful techniques using the OLI and
TIRS data (Fig. 7). In Fig. 7 (top), a very low temperature calibration
test site in the high-elevation areas of East Antarctica is illustrated. In-
creased thermal sensor precision and extended calibrated temperature
range at the 100 m TIRS resolution can be used to investigate the spatial
distribution of extremely low ice sheet temperatures. In Fig. 7 (middle)
the geolocation and high radiometric precision of the OLI panchromatic
band facilitate ice velocity mapping using image cross-correlation
algorithms based on refinements to established algorithms. Landsat 8
will also contribute to detailed mapping of surface melt ponds occurring
on glaciers and ice sheets, including their extent, depth, and volume,
with enhanced precision due to the higher radiometry relative to past
Landsat studies (e.g., Tedesco & Steiner, 2011). Empirical relationships
between field-based estimates of lake-bottom albedo and the decline
in radiance in the OLI green (0.525–0.600 μm) reflectance with water
depth can be usedto define meltwater depth (Fig. 7, bottom). This capa-
bility is particularly notableas surface water on icesheets and glaciers is
difficult to monitor but can have a complex interaction with the under-
lying ice, through fracture, penetration, and sub-glacial lubrication (Das
et al., 2008; Scambos, Bohlander, Shuman, & Skvarca, 2004; Stearns,
Smith, & Hamilton, 2008; Zwally et al., 2002).
5. Higher-Level Landsat product generation needs, opportunities
and challenges
The provision of ‘higher-level’Landsat products, i.e., geographically
seamless, gridded products that have been subject to geophysical trans-
formations and processed to derive environmental variables over differ-
ent time periods (monthly, seasonal, annual), have been advocated by
the LST and by the user community. Higher-level products are needed
to meet demands for consistently processed, moderate spatial resolu-
tion, large area, long-term terrestrial data records for climate and global
change studies, to help national and international reporting linked to
multilateral environmental agreements, and for regional and national
resource management applications.
The Millennium Ecosystem Assessment (Carpenter et al., 2006)and
outcomes of the Rio +20 United NationsConference on Sustainable De-
velopment (UN, 2012) highlighted the need for transparent, systematic,
and repeatable measures of a variety of ecosystem characteristics. The
concept of a climate data record has been introduced as a data set de-
signed to enable study of long-term climate change, with ‘long-term’
meaning year-to-year and decade-to-decade change, (NRC, 2000). The
40+ year continuity of the Landsat program, with consecutive, tempo-
rally overlapping Landsat observatories and cross-sensor calibration, is
a key reason the Landsat program has value for climate studies
(Trenberth et al., 2013). The Global Climate Observing System has iden-
tified a set of Essential Climate Variables (ECVs) needed in support of
the United Nations Framework Convention on Climate Change (GCOS-
154, 2011), and listed the following terrestrial ECVs as feasible for
sustained monitoring from satellite data: snow areal extent; outlines
of glaciers and ice caps; ice sheet elevation changes; lake level and
area; surface reflectance anisotropy and black and white sky albedo;
landcover type and maps for detection of land cover change; fraction
of absorbed photosynthetically active radiation (FAPAR); leaf area
index (LAI); above-ground forest biomass; burned area and active fire
detection; soil moisture; and land surface temperature. The preliminary
LST evaluation of Landsat8 capabilities and identification of new science
and applications described in Section 4 illustrate that the majority of
these ECVs can be retrieved directly or indirectly from Landsat data. Of
the ECVs identified, only ice sheet elevation changes, soil moisture,
and above-ground forest biomass cannot be reliably retrieved from
Landsat 8 or predecessor Landsat sensor data. However, Landsat-based
forest biomass estimates can be made in certain biomes, such as boreal
forests, with accuracies comparable to estimates derived using higher
spatial resolution satellite and airborne data (Mora, Wulder, White, &
Hobart, 2013). The fAPAR and LAI can be derived by empirical biome
or land cover class specific parameterization of NDVI (Butson &
Fernandes, 2004) or by model inversion against Landsat reflectance
(Ganguly et al., 2012). Landsat burned area mapping involves a high
degree of human intervention (Bastarrika, Chuvieco, & Martin, 2011)
and large area Landsat burned area mapping initiatives have significant
reporting lags (Eidenshink et al., 2007). Landsat active fire detection
algorithms have also been developed but have limited sampling capa-
bilities (Schroeder et al., 2008).
165D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
The experience of generating and distributing global decadal scale
coarse spatial resolution land products derived from MODIS and
Advanced Very High Resolution Radiometer (AVHRR) data (Justice
et al., 1998; Tucker et al., 2005) is informative of the opportunities and
challenges to generating higher-level Landsat products. Global long-
term AVHRR land surface data records have been derived to advance
understanding of the terrestrial carbon and biogeochemical cycles and
interactions with climate (Myneni, Tucker, Asrar, & Keeling, 1998). To
166 D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
date,however,nodefinitively processed long-term AVHRR land data re-
cord has been generated and thosethat are publically available are sub-
ject to scientificdebate(Beck et al., 2011). This is because, unlike
Landsat, the AVHRR sensors have noonboard reflective wavelength cal-
ibration capability and they were not designed for landsurface monitor-
ing (Vermote, Saleous, & Holben, 1995; Wu, Sullivan, & Heidinger,
2010). MODIS combines characteristics of the AVHRR and Landsat sen-
sor designs and includes onboard reflective and thermal wavelength
calibration systems (Justice et al., 1998). A suite of higher-level MODIS
land products are being generated to meet the needs of the global
change research community, with the recognition that the products
are also used at regional scales and in support of applications (Justice
et al., 2002; Masuoka et al., 2011, chap. 22). The acceptance and utility
of the MODIS Land products by the resource management, policy, re-
search and application communities indicate the need for similar prod-
ucts and product generation approaches but at Landsat resolution.
It is feasible to process long-term and large-area Landsat data setsto
provide a medium spatial resolution analog tothe coarse spatial resolu-
tion higher-level land products generated from the MODIS and AVHRR
data streams. The Web-enabled Landsat Data (WELD) project has
started to demonstrate this capability by generating ten years of 30 m
weekly, seasonal, monthly and annual composited Landsat 7 ETM +
mosaics of the conterminous United States (CONUS) and Alaska (Roy
et al., 2010).The WELD products enable the development ofturnkey ap-
proaches to, for example, land cover and land cover change characteri-
zation (Hansen et al., 2011; 2014), by employing systematic and
automated Landsat processing, including conversion of digital numbers
to calibrated top of atmosphere reflectanceand brightness temperature,
cloud masking, and reprojection into a gridded continental map projec-
tion. The WELD processing has been adapted and applied to Landsat 8
data. Fig. 8 shows the WELD processing applied to all the Landsat 8
L1T data acquired over the CONUS in August 2013. Unlike currently
available WELD Landsat 7 ETM+ products (WELD, 2013), the reflec-
tance saturation usually seen over clouds is not evident due to the im-
proved OLI dynamic range, there are no missing data because the
improved Landsat 8 geolocation enables more images to be processed
to L1T products, and there are no missing data due to the ETM+ SLC-
off issue.
Additional steps are needed during the Landsat 8 era if the needs of
the global change research user community are to be met. Not least is
the need to generate global coverage Landsat higher-level products for
the entire Landsat sensor data record. These data should be atmospher-
ically corrected to provide an accurate and stable surface reflectance re-
cord. Algorithms to combine contemporaneous Landsat sensor data, e.g.
Landsat 5 and 7, or Landsat 7 and 8, should also be developed to provide
more frequent acquisition coverage and improved probabilities of ac-
quiring cloud-free land observations (Kovalskyy & Roy, 2013). Higher-
level product generation algorithms that are computationally efficient
and automated should be developed as approaches that require high
levels of user intervention will not be scalable. The global annual
Landsat data volume is more than an order of magnitude greater than
for MODIS, requiring processing on high performance supercomputers
with petabyte data storage solutions (Nemani, Votava, Michaelis,
Melton, & Milesi, 2011). Distribution will also need to be scaled appro-
priately. Recent developments in web services and value added web
product delivery systems have the potential to provide naturalresource
managers, policy makers and researcherswith an unprecedented capac-
ity to access, analyze and interpret higher-level products derived from
the multi-petabyte scale archive of global Landsat data. In the next de-
cade, the ability to extract Landsat 1–8 time series of derived
environmental information at any pixel location globally is desired.
Moreover, there are significant, but currently unrealized, opportunities
for fast and automated processing to systematically produce near-real
time Landsat 8 higher-level monitoring products, for example, to gener-
ate ET and drought information (Section 4.4) or disturbance informa-
tion (Section 4.6).
6. International synergies between Landsat and other moderate
resolution optical wavelength remote sensing satellites
The value of Landsat data and products is well established. A main
shortcoming, clearly articulated in the above sections, remains the
need for more frequent observations to mitigate atmospheric effects
and to monitor hightemporal frequency phenomena. More frequent ob-
servations provide (a) more opportunities for cloud-free, shadow-free,
and atmospherically uncontaminated surface observations without
missing data, within desired annual, seasonal and monthly windows,
(b) increasingly detailed descriptions of time-dependent phenomena,
and (c) more reliable time-series and change detection analyses. Im-
proving the Landsat temporal resolution can be achieved by launching
more Landsat sensors, to provide a constellation, increasing the imaging
swath width, or by combining Landsatdata with data from other compa-
rable remote sensing satellites.
There are a number of satellites with spatial and spectral characteris-
tics similar to those of Landsat (Stoney, 2008): at least 20 missions carry-
ing moderate spatial resolution multispectral imagers were operational
as of 30th June 2013 (Belward & Skoien, under review). In particular,
Europe's Copernicus Earth Observation program includes two planned
Sentinel-2 satellites designed to provide, under a free and open data pol-
icy, multiple global acquisitions with similar spectral and spatial charac-
teristics as Landsat. The Multi Spectral Instrument (MSI) onboard
Sentinel-2 has 13 spectral bands ranging from 0.433 μm to 2.19 μm;
four 10 m visible and near-infrared bands, six 20 m red edge, near-
infrared and SWIR bands, and three 60 m bands for characterizing aero-
sols, water vapor and cirrus clouds (Drusch et al., 2012). Landsat 8 and
Sentinel-2 pre-launch cross-calibration was undertaken (Section 4.1)
to specifically support their combined data use. Scientific and applica-
tions uses are maximized through known and communicated calibration
characteristics. Landsat is currently the only satellite program to provide
consistent, cross-calibrated data spanning more than four decades
(Chander, Markham, & Helder, 2009; Markham & Helder, 2012), which
gives the program a crucial role in the provision of terrestrial essential
climate variables and long-term climate data records. The addition of
other sensors and space agencies to implement instrument cross-
calibration will be of considerable benefit to the user community. Com-
plementary to calibration activities is a recommendation to move to-
wards increasingly standardized processing, empowering users to
integrate data from different systems into science and applications in a
seamless fashion. A central question concerns how data from additional
sensors can be integrated in a systematic and robust fashion to advance
global land monitoring capabilities, with options reviewed in Wulder
et al. (2011).
At the international level, the needs for and utility of information pro-
vided by satellites is clear: government and/or private entities in 32 sov-
ereign states/geopolitical blocs have financed at least 186 land cover
observing missions over the last four decades (Belward & Skoien, under
review; Stoney, 2008). Since the 1970s the average number of land imag-
ing satellites launched per-year has increased from two to over nine, av-
erage longevity has increased almost threefold, and costs of ‘entry-level’
systems is falling. Free and open data access stimulates new science and
Fig. 8. Conterminous United States Landsat 8 monthly WELD product browse images, August2013, equal area Albers projection. Generated from all the available (890) August 2013 OLI
scenes processed to L1T. Browse images composed of 16,500 × 11,000 300 m pixels each generated from 10 × 10 30 m OLI pixels. Top: Top of atmosphere (TOA) true color reflectance
shown with a similar stretch as Fig. 1b. Bottom: TOA Normalized Difference Vegetation Index (NDVI) displayed with the standard MODIS NDVI color palette provided by the University
of Arizona Vegetation Index and Phenology Laboratory.
167D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
applications, but alternative business models and lower costs stimulate
wider system ownership. For example, the data sharing and common
ground segment established by the Disaster Monitoring Constellation
(DMC) have enabled launches of land imaging systems by agencies in
Europe, Africa and Asia (Da Silva Curiel et al., 2005). Whilst more people
in more countries have access to global land surface observations from
space than ever before, a note of caution is needed. Smaller and lighter
satellites may be cheaper to build and launch (Xue, Li, Guang, Zhang, &
Guo, 2008), but there is an inescapable need for tightly specified, high re-
liability, high performance systems such as Landsat to maintain long–
term consistent data records and facilitate fusion of data from different
satellites. Satellite costs rise as performance specifications and reliability
rise. The use of proven technologies, for example through follow-on mis-
sions of similar sensors, such as the two Sentinel-2 sensors, or the two
MODIS sensors onboard the Terra and Aqua satellites, is one means to
control costs and shorten development timelines. In all cases, restrictive
data policies and data access limitations should be avoided. Explorative
cross-platform experiments of benefit to the wider research community
are required, and realistically only made possible by the free and open ac-
cess to analysis ready data products.
7. Conclusion
The successful launch of Landsat 8 is planned to extend the 40-year
Landsat record at least another 5 years, further advancing global change
research, while protecting and maximizing the previous investments in
Landsat. The importance of continuity of the Landsat record cannot be
underestimated. Just as the Mauna Loa, Hawaii, atmospheric CO
2
record
is now considered a vital indicator of human activity, but whose conti-
nuity was also not always guaranteed (Keeling, 1998), Landsat provides
the longest consistent satellite terrestrial record. The free availability of
all Landsat data in the U.S. archive provides an unprecedented opportu-
nity for analysis of the past and future terrestrial change. Terrestrial
changes, both gradual and sudden, may be understood in quite different
ways in the context of multi-decadal rather than decadal time series.
When considering the new observations from Landsat 8 as a continua-
tion of the complementary and calibrated previously collected Landsat
series, the importance, and value, of Landsat 8 is evident.
From this communication it should beclear that Landsat 8 is an excit-
ing collection of spatial, spectral, temporal, and radiometric (perfor-
mance, accuracy, and dynamic range) resolutions, combined with
robust pre- and post-launch calibration, high geometric and geodetic ac-
curacy, as well as a systematic global acquisition strategy. Looking for-
ward, it is critical that follow-on missions enable continuity with
previous Landsat missions, including data coverage, spatial and spectral
resolution, and rigorous calibration. Continuity of the information
content of the Landsat TM and ETM + reflective visible and short-
wave infrared bands at 30 m is needed to monitor the surface at scales
where anthropogenic change is occurring. Continuity of coincident ther-
mal band information is required to provide cloud masking capabilities
and to enable surface temperature, evapotranspiration, drought and
cryospheric monitoring. Continuity of radiometric calibration is funda-
mental to maintain a long-term consistent Landsat data record, facilitate
fusion of Landsat data with other satellite sensor data, and to enable
quantitative information extraction. Based on the insights of representa-
tives from university, government, and industry, a recent report from
the US National Research Council (NRC, 2013) describes the past history
of the Landsat program, thecore elementsof the current imaging system
that require continuity, and offers some suggestions on future potential
land imaging systems and observation capacities. Insights on how
Landsat system costs can be reduced in the future, such as purchase of
multiple spacecraft and fixed price contracting, are also described. Be-
sides reinforcing the need and demand for Landsat data, land imaging
data from Landsat increasingly appears as a necessary element of na-
tional infrastructure. In any forward looking scenario regarding Landsat,
the current capacity and the benefits of continuity must be maintained,
with science and applications information needs driving requirements.
This paper summarizes the Landsat Science Team (LST) efforts to es-
tablish an initial understanding of Landsat 8 capabilities and the steps
ahead in support of science team identified priorities. The aims of the
LST align with the current scientific and applications challenges that
are known or anticipated. The thematic sections above capture what is
known and how to build on that existing knowledge with the recent
availability of Landsat 8 data. The sensor improvements to Landsat 8
are already showing measurement and categorization improvements,
and offer refined opportunities for quantitative approaches to Landsat
information extraction. These benefits arise first through improved
pre-processing (e.g., atmospheric correction), and then by enabling
physically-based information extraction approaches (e.g., albedo,
water quality, evapotranspiration; via model inversion and data assim-
ilation). It is also evident from the thematic sections that the continued
and well understood Landsat data stream offers increased capacity for
systematic production of scientifically supported higher-level Landsat
products. The acceptance and utility of the MODIS land products by
the resource management, policy, research and application communi-
ties indicate the need for similar products and product generation ap-
proaches with Landsat. It is important that higher-level Landsat
products are community endorsed and subject to external scientific
scrutiny, quality assessed, validated, and reprocessed as needed. A
core challenge is to scale currentcapabilities to generate global coverage
Landsat higher-level products for all the Landsat data record.
Landsat-like is often used to describe data from other optical wave-
length remote sensing systems with a similar spatial resolution as
Landsat. Detection of change and monitoring of terrestrial ecosystems
using these systems is hampered by cloud cover, leading to interest in
radar data (De Sy et al., 2012). The free and open access to the Landsat
archive has mitigated this situation through a change from scene-
based to pixel-based processing, data blending, and cross-sensor data
integration. The capacity of large numbers of images to create cloud-
free composites at Landsat spatial resolution has been demonstrated.
Landsat 8 is offering continuity of such measures, plus an effective
8-day revisit in combination with Landsat 7. The planned launch of
Sentinel-2 by the European Space Agency will offer additional unique
and complementary measures. Advances made through multi-sensor
data integration may provide required measurements as well as provid-
ing insights on the development and deployment of additional, lower
cost, satellites to complement the well calibrated and systematically
collected Landsat data series.
Landsatoccupiesavaluablenicheinglobalsatelliteremotesensing;
the platform collects images at a spatial resolution indicative of human
interactions with terrestrial ecosystems yet with an image extent com-
patible with systematic analyses over large areas. A ground-segment
based on reliable science provides freeandopenaccesstoLevel1prod-
ucts of known, documented, and traceable quality. The integrity of this
production chain and the confidence users have in Landsat data has led
to a proliferation of science and applications —the Landsat program pro-
vides an example to be emulated. By expanding the Landsat archive, and
by providing improved data quality, Landsat 8 has enabled continuity
and early results have shown advances in established mapping and mon-
itoring programs, allowing for a general fostering of development of new
approaches for characterizing state, condition, and dynamics of the
Earth's surface. Building upon these successes, near-term emphasis
should be placed on ensuring operational status for the incoming Landsat
data stream and outgoing Level 1 and higher-level data products.
Acknowledgements
We are grateful to the NASA–USGS-industry Landsat Data Continuity
Mission (LDCM) development team for their efforts to meet an aggres-
sive launch schedule, and we thank the USGS Climate and Land
Use Change Mission's Land Remote Sensing Program and the Earth
168 D.P. Roy et al. / Remote Sensing of Environment 145 (2014) 154–172
Resources Observation and Science (EROS) Center for co-sponsoring
and funding the USGS–NASA Landsat Science Team.
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