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Land Surface Temperature Retrieval of Landsat-8 Data using Split Window Algorithm-A Case Study of Mosul District


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The main purpose of this paper is to present an operational algorithm to retrieve the Land Surface Temperature (LST) and Land Surface Emissivity (LSE) in Mosul District, Ninawa Province, Iraq from Landsat-8 data of September 16, 2015. The proposed algorithm is Split-Window (SW) with brightness temperature value of both band 10 (10.60 - 11.19µm) and band 11 (11.50 - 12.51 µm) of Landsat-8 in thermal infrared range. Normalized Difference Vegetation (NDVI) threshold values have been determined to separate the bare soil, and vegetated areas from each other. Fractional Vegetation Cover (FVC) was derived with the help of NDVI threshold technique. Emissivity values of bands 10 and 11 are calculated through FVC. The results showed that the spatial variation of land surface temperature was more reliable and accurate in entire Mosul District.
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Journal of American Science 2017;13(12)
Land Surface Temperature Retrieval of Landsat-8 Data Using Split Window Algorithm-A Case Study of
Mosul District
Amal Muhammad Saleh
College of Agriculture /University of Baghdad
Abstract: The main purpose of this paper is to present an operational algorithm to retrieve the Land Surface
Temperature (LST) and Land Surface Emissivity (LSE) in Mosul District, Ninawa Province, Iraq from Landsat-8
data of September 16, 2015. The proposed algorithm is Split-Window (SW) with brightness temperature value of
both band 10 (10.60 - 11.19µm) and band 11 (11.50 - 12.51 µ m) of Landsat-8 in thermal infrared range. Normalized
Difference Vegetation (NDVI) threshold values have been determined to separate the bare soil, and vegetated areas
from each other. Fractional Vegetation Cover (FVC) was derived with the help of NDVI threshold technique.
Emissivity values of bands 10 and 11 are calculated through FVC. The results showed that the spatial variation of
land surface temperature was more reliable and accurate in entire Mosul District.
[Amal Muhammad Saleh. Land Surface Temperature Retrieval of Landsat-8 Data Using Split Window
Algorithm-A Case Study of Mosul District. J Am Sci 2017;13(12):62-75]. ISSN 1545-1003 (print); ISSN 2375-
7264 (online). 8. doi:10.7537/marsjas131217.08.
Keywords: Land Surface Temperature; Land Surface Emissivity; Fractional Vegetation Cover; Split-Window;
1. Introduction
Land surface temperature (LST) is one of the
most important parameters in the physical processes
of surface energy and water balance at local through
global scales (Karnieli et al., 2010). Knowledge of the
LST provides information on the temporal and spatial
variations of the surface equilibrium state and is of
fundamental importance in many applications (Kerr et
al., 2000). As such, the LST is widely used in a
variety of fields including evapotranspiration, climate
change, hydrological cycle, vegetation monitoring,
urban climate and environmental studies, among
others (Hansen et al., 2010). and has been recognized
as one of the high-priority parameters of the
International Geosphere and Biosphere Program
(IGBP) (Townshend et al., 1994). Due to the strong
heterogeneity of land surface characteristics such as
vegetation, topography, and soil, LST changes rapidly
in space as well as in time and an adequate
characterization of LST distribution and its temporal
evolution, therefore, requires measurements with
detailed spatial and temporal sampling (Neteler,
Given the complexity of surface temperature
over land, ground measurements cannot practically
provide values over wide areas. With the development
of remote sensing from space, satellite data offer the
only possibility for measuring LST over the entire
globe with sufficiently high temporal resolution and
with complete spatially averaged rather than point
values. (Li et al., 2013).
The major objectives of the study are to find the
brightness temperature using band 10 and band 11 of
TIR, calculate the LSE using NDVI threshold
technique and estimate the LST of Mosul District
using Split-Window (SW) algorithm.
2. Material and Methods
Mosul is a city in northern Iraq Located some
400 km (250 mi) north of Baghdad, the original city
stands on the west bank of the Tigris River, opposite
the ancient Assyrian city of Nineveh on the east bank
(Figure 1a). It is geographically situated on latitude
35º 33 54.67-36º 3203.79 North and longitude 42º
4312.46 - 43º 0321.78 East. Mosul experiences a
hot semi-arid climate (ppen climate classification
BSh) (Peel et al., 2007). The winter is mild, but
certainly it's not tropical; the January average is 7 °C.
The summer in Mosul is very hot, with a relentless
sun, and with daytime temperatures of 43°C in July
and August, but with peaks of 47/48°C; however, the
relative humidity is between 25% - 79% (Brugge,
2014). Mosul is at an altitude of about 228 metres
(748ft) above sea level. Throughout the year, in
Mosul, 365 mm of rain fall: they are not many, but
they are concentrated between November and April,
with very few rains in May and October, while
between June and September it almost never rains
(Dessler and Parson, 2006). The average annual wind
speed of Mosul is 1.26 m/s, (Hassoon, 2013).
Landsat 8 is one of the Landsat series of NASA.
The data of Landsat 8 is available in Earth Explorer
website at free of cost. In the present study, the TIR
bands 10 and 11 were used to estimate brightness
temperature and OLI spectral bands 2, 3, 4, and 5
were used to generate NDVI of the study area (Figure
Journal of American Science 2017;13(12)
1b), (Table 1). Landsat 8 provides metadata of the
bands such as thermal constant, rescaling factor value
etc., which can be used for calculating various
algorithms like LST.
Figure 1. (a) Location map of the study area, (b) The Satellite Landsat OLI and TIRS scene.
The value of Top of Atmospheric (TOA) spectral
radiance (L) was determined by multiplying
multiplicative rescaling factor (0.0003342) of TIR
bands with its corresponding TIR bands and adding
additive rescaling factor (0.1) with it (Table 2).
Lλ =  (1)
Lλ - Top of Atmospheric Radiance in watts/(m2 *
srad * m).
ML - Band specific multiplicative rescaling factor
Qcal - band 10/ 11 image.
AL - Band specific additive rescaling factor
Estimation of Brightness Temperature (TB) of
Band 10 and 11 is the electromagnetic radiation
traveling upward from the top of the Earths
atmosphere. Thermal calibration process has been
done by converting thermal DN values of raw thermal
bands of TIR sensor into Top of Atmospheric (TOA)
Spectral Radiance.
Using ERDAS IMAGINE 9.2 Modeler we
implement algorithm of equation-2.
TB = 
󰇢 (2)
TB - Brightness Temperature.
Lλ - Top of Atmospheric spectral radiance in
(m2 * srad * m).
K1 and K2 - Band-specific thermal conversion
constant from the metadata image file and it varies for
both TIR bands (Table 3).
Journal of American Science 2017;13(12)
OLI bands 2, 3, 4 and 5 were layer stacked and
NDVI was calculated using algorithm shown in
equation-3 (Rouse et al., 1974).
Range: -1 < NDVI < + 1
NDVI - Normalized Difference Vegetation
BAND4 - TOA planetary reflectance values from
Red band.
BAND5 - TOA planetary reflectance values from
Near Infrared band.
The relationship between LST and NDVI takes
into account that vegetation and soils are the main
surface cover for the terrestrial component (Snyder et
al., 1998).
Table 1. Metadata of Satellite Image.
No. of Bands
Resolution (m)
Path/ Row
Date of Acquisition
Panchromatic (1)
170 / 035
16th September
Reflective (8)
170 / 035
Thermal (2)
170 / 035
Table 2. Radiometric rescaling Factor.
Rescaling Factor
Band 10
Band 11
Table 3. Thermal constant values.
Estimation of Fractional Vegetation Cover
(FVC) was implemented using NDVI image.
Fractional Vegetation Cover estimate the fraction of
an area under vegetation. Split-Window algorithm
utilize FVC to estimate Land Surface Emissivity
(LSE). Using ARC MAP 10.3 we reclassify the NDVI
layer into soil and vegetation and calculate NDVI for
Soil and Vegetation (Table 4).
Table 4. NDVI for Soil and Vegetation.
NDVI for Soil
NDVI for Vegetation
Using ERDAS IMAGINE 9.2 Modeler we
calculated the algorithm of FVC of equation-4
(Carlson and Ripley, 1997; Gutman and Ignatov,
FVC - Fractional Vegetation Cover.
NDVIs - NDVI reclassified for soil.
NDVIv - NDVI reclassified for vegetation.
The correct determination of the surface
temperature is constrained to an accurate knowledge
of surface emissivity. The emissivity of a surface is
controlled by such factors as water content, chemical
composition, structure and roughness. It can be
determined as the contribution of the different
components that belong to the pixels according to
their proportions (Snyder et al., 1998).
Estimation of Land Surface Emissivity (LSE)
was obtained from FVC layer. Land Surface
Emissivity measure the inherent characteristic of earth
surface. LSE estimation required emissivity of soil
and vegetation of both Band 10 and 11 (Table 5).
Table 5. Emissivity values.
Band 10
Band 11
[Source: Skokovet al., 2014]
LSE of Band 10 and 11 were individually
calculated as shown in equation-5.
LSE = Ɛs * (1 - FVC) + Ɛv * FVC (5)
LSE - Land Surface Emissivity.
Ɛs - Emissivity for soil.
Ɛv - Emissivity for vegetation.
FVC - Fractional Vegetation Cover.
Combination of LSE of Band 10 and LSE of
Band 11 was obtained through Mean and Difference
Journal of American Science 2017;13(12)
between them as shown in equation-6 & 7 (Sobrino et
al., 1996).
m = LSE10 LSE11 (7)
m - Mean of LSE.
LSE10 - LSE of band 10.
LSE11 - LSE of band 11.
m - Difference of LSE
Land Surface Temperature (LST) was calculated
by applying a structured mathematical algorithm viz.,
Split-Window (SW) algorithm. It uses brightness
temperature of two bands of TIR, mean and difference
in land surface emissivity for estimating LST of an
area. Using ERDAS IMAGINE 9.2 Modeler we
implement the algorithm of equation-8.
LST = TB10 + C1 (TB10 -TB11) +C2 (TB10 -TB11)2 +
C0 + (C3+C4W) (1-m) + (C5+C6W)m (8)
LST - Land Surface Temperature ().
TB10 and TB11 - Brightness Temperature of band
10 and band 11 (Kº).
C0 To C6 - Split-Window Coefficient values
(Table 6).
m - mean LSE of TIR bands.
m - Difference of LSE.
W- Atmospheric water-vapour content =14.215
g/cm2 [Source: Iraqi Meteorological Organization And
Seismology, 2015].
Table 6. Split-Window coefficient values.
[Source: Skokovet al., 2014]
3. Results
NDVI map revealed that the NDVI value varies
between -0.0923236 to 0.557708. Healthy and green
vegetation cover area had highest NDVI value
whereas area under water body had negative value
(Figure 2). The NDVI value of area under vegetation
was more than 0.060304167 and for built-up and
barren land was 0 - 0.060304167. We calculate
Fractional vegetation cover (FVC) using equation-4 as
shown in Figure (3) and LSE using equation-5.
We implement algorithm in ERDAS IMAGINE
9.2 Modeler to calculate difference and mean LSE
Layer shown in Figure (4) and Figure (5). Mean LSE
Layer of Mosul Province ranged between 0.969 -
0.988. Highly elevated regions in the Province had
more vegetative cover, hence LSE was high in these
We take TIRS band 10 and 11 to estimate
Brightness Temperature (TB) in Kelvin using the
algorithm of equation-2 shown in Figure (6) and
Figure (7). From Figure (8) and Figure (9) of
statistical graph, we observe that class 5 exhibit
28.90% of the total area at a temperature between
302.28 -315.60 Kº from TB of Band 10 and also class
5 of TB of Band 11 exhibit maximum of 27.68% of
total area at a temperature between 298.52-311.11Kº.
Figure (10) represent the final LST layer of
Mosul Province on 16th September 2015. Area
statistics graph of LST layer in Figure (11) proof that,
it divide into two major land surface temperature class
of 1 and 4 with statistics of 28.97% of an area under
foothill regions at a temperature less than 269.20 Kº
by class 1 and 30.66% of an area under healthy
vegetation land cover at a temperature in between
311.54 - 334.64 Kº by class 4. Water bodies exhibit
20.83% of the total area at a temperature in between
292.29 - 311.54 Kº by class 3. Cultivable land exhibit
17.52% of the total area at a temperature in between
269.20 - 292.29 Kº by class 2. Remain 2.02% of an
area under barren land and wasteland exhibit an LST
more than 334.64 Kº by class 5.
Validation of LST is of crucial importance for
estimating the accuracy of the products and
understanding the potential and limitations of satellite
observations of LST. Validation of LST was carried
out using a direct comparison of satellite-derived LST
with collocated and simultaneously acquired LST
from ground-based on 16th September 2015 (Iraqi
Meteorological Organization And Seismology, 2015).
Figure (12) shows a Linear regression of estimated vs.
actual LST, the small bias & RMSE and the high
coefficient of determination R2 demonstrate the
excellent quality of the Satellite Application Facility.
The cold outliers are assumed to be caused by
undetected cloud contamination or instrument -related
4. Discussions
LST and LSE are two significant parameters in
global change studies, in estimating radiation budgets
in heat balance studies and as a control index for
climate models. Emissivity, is an indicator of land-
cover type and resources, and also a necessary
element in the calculation of Land surface temperature
(LST) from remotely sensed data. For LSE mapping,
Fractional Vegetation Cover (FVC) of optical bands
of OLI sensor of Landsat 8 had been derived. Split-
Window algorithm (SW) is a dynamic mathematical
Journal of American Science 2017;13(12)
tool, provide the (LST) information using brightness
temperature of thermal bands of TIRS sensor and
Land surface emissivity (LSE). The study clearly
revealed that 30.66% of the total area are under
vegetation land cover at a temperature of 311.54 -
334.64 Kº and other 28.97% of the total area are under
hilly regions at a temperature less than 269.20 Kº.
Therefore the results indicate that the proposed (SW)
algorithm can be a suitable and robust method to
retrieve the LST map from Landsat-8 satellite data.
Figure 2. NDVI Layer of Mosul District on 16th September 2015.
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Figure 3. FVC Layer of Mosul District on 16th September 2015.
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Figure 4. Difference LSE layer between Band 10 and 11.
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Figure 5. Mean of LSE layer between band 10 and 11.
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Figure 6. TB of Band 10 with label of Temperature intervals.
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Figure 7. TB of Band 11 with label of Temperature intervals.
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Figure 8. Area statistics of classified TB layer of Band 10.
Figure 9. Area statistics of classified TB layer of Band 11.
Journal of American Science 2017;13(12)
Figure 10. Land Surface Temperature Layer of Mosul District on 16th September 2015.
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Figure 11. Graph of Area occupied (%).
Figure 12. Scatter plot of estimated vs. actual LST.
Journal of American Science 2017;13(12)
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... LST has information that correlates with most parameters such as land surface interaction with the atmosphere as well as temporal and spatial information [3][4]. The importance of LST being able to be used to determine land conditions such as land cover changes, evapotranspiration, monitoring vegetation, climate change, and urban climate [5][6][7]. It requires a quick and broad measurement to determine the rate of LST changes. ...
... FVC has correlated with climate change and affects the land surface temperature condition emitted by objects [19,21]. The FVC effects on land surface temperatures are not [7][8][9][10][11][12][13]. The SWA-S method of Sobrino et al [14,15] used the water vapor value from the extraction of remote sensing image. ...
... That result gave raising accuracy for land surface temperature although used a different method for extraction land surface temperature. LSE method has dominated value ≥ 0.96 for each processing data [7,[22][23], but the emissivity value can know from each object with those conditions for estimation of land surface temperature [12,39]. ...
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... Nowadays, more number of satellites especially medium-to large-resolution satellites like Landsat (TM and ETM + ), Advanced Spacebrone Thermal Emission and Reflection Radiometer (ASTER), Moderate Resolution Imaging Spectroradiometer (MODIS), NOAA's Advanced Very-High-Resolution Radiometer (AVHRR), SPOT, IKONOS and QuickBird with wide spectral resolution and medium to high spatial resolution (0.60-30.0 m) are widely used for both land use/land cover change and land surface temperature assessments. However, in recent period, Landsat OLI-TIRS data have become popular in SUHI studies (Latif 2014). ...
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This study presents a new fusion method namely supervised cross-fusion method to improve the capability of fused thermal, radar, and optical images for classification. The proposed cross-fusion method is a combination of pixel-based and supervised feature-based fusion of thermal, radar, and optical data. The pixel-based fusion was applied to fuse optical data of Sentinel-2 and Landsat 8. According to correlation coefficient (CR) and signal to noise ratio (SNR), among the used pixel-based fusion methods, wavelet obtained the best results for fusion. Considering spectral and spatial information preservation, CR of the wavelet method is 0.97 and 0.96, respectively. The supervised feature-based fusion method is a fusion of best output of pixel-based fusion level, land surface temperature (LST) data, and Sentinel-1 radar image using a supervised approach. The supervised approach is a supervised feature selection and learning of the inputs based on linear discriminant analysis and sparse regularization (LDASR) algorithm. In the present study, the non-negative matrix factorization (NMF) was utilized for feature extraction. A comparison of the obtained results with state of the art fusion method indicated a higher accuracy of our proposed method of classification. The rotation forest (RoF) classification results improvement was 25% and the support vector machine (SVM) results improvement was 31%. The results showed that the proposed method is well classified and separated four main classes of settlements, barren land, river, river bank, and even the bridges over the river. Also, a number of unclassified pixels by SVM are very low compared to other classification methods and can be neglected. The study results showed that LST calculated using thermal data has had positive effects on improving the classification results. By comparing the results of supervised cross-fusion without using LST data to the proposed method results, SVM and RoF classifiers showed 38% and 7% of classification improvement, respectively.
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Land use change is the main driving force of global environmental change and is considered as most central to various debates on sustainable development. Even though a large volume of literature materials is available on land use/land cover change for many areas, very little work has been done on land use and its implications on land surface thermal characteristics over the Sokoto area of Nigeria, despite the strategic importance of the zone, including urbanization, increased population as well as the climate in the area, which is dominated by warm harmattan wind blowing Sahara dust inland. Thus, this study aimed at investigating the implications of urban growth on temporal variations of land surface temperature (LST) using remote sensing and geographic information system (GIS) techniques over Sokoto Metropolis, Nigeria between 1986 and 2016. The change detection of each land use class was carried out for each period using Landsat images obtained from the archives of the United States Geological Survey (USGS). The results revealed that the area has undergone a drastic transformation where built-up area witnessed changes at 10.77%, farmland and vegetation increased at the rate of 0.72% and 2.15%, respectively, for the period of study (1986–2016). While bare soil and water body decreased at the rate of 0.56% and 1.11%, respectively, during the study period. This shows that there exists a transformation from bare surface (desert) to vegetated surface especially between years 2009 and 2016. The LST of Sokoto Metropolis was calculated from the satellite data, and the land surface temperature of each land use class was assessed for the study period. The maximum LST of Sokoto was 30.6°C, 32.8°C and 34.6°C for 1986, 1999 and 2016, respectively. This study has revealed the existence of a positive relationship between built-up area and LST over the area. This development might be as a result of anthropogenic activities through urban growth coupled with its potential impacts on urban climate. These are intensified by constant changes of the space, causing imbalance in the interactions between surface and atmosphere which may be extensively influenced or modified by various forms of land use.
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Monthly observed wind speed data at four weather stations (Baghdad, Mosul, Basra, Rutba) at 10m above surface were used to explore the temporal variations of the wind speed (1971-2000) in Iraq. There are different methods to analyze wind speed variation data, but the time series are one of the powerful analysis methods to diagnose the seasonal wind speed anomaly. The results show most high abnormal data is found in summer seasons in all the stations of study, where it concentrated at 1975, 1976, 1978,1996-1995, 2000. Rutba station is different where its high deviation about annual average at nearly all the seasons, in this station there are trends in seasonal wind towards decreases in all the seasons, for example in winter it reached to about 0.046m/s.a-1 , while in other stations Mosul and Basra there increases in annual seasonal wind speed trends in seasons spring, summer, autumn where its reached higher value at summer in Basra about 0.0482m/s.a-1. The second method to determine abnormal annual seasonal wind speed is through comparison seasonal average wind speed, where the average wind speed at the seasons summer and spring in Baghdad and Basra station have very high averages at nearly all years, this cannot see in Mosul and Rutba, in Rutba the seasonal average is intersected with each other, summer and spring is not have greater seasonal average in this station.
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Fraction of green vegetation, fg, and green leaf area index, L g , are needed as a regular space-time gridded input to evapotranspiration schemes in the two National Weather Service (NWS) numerical prediction modelsÐ regional Eta and global medium range forecast. This study explores the potential of deriving these two variables from the NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized di erence vegetation index (NDVI) data. Obviously, one NDVI measurement does not allow simultaneous derivation of both vegetation variables. Simple models of a satellite pixel are used to illustrate the ambiguity resulting from a combination of the unknown horizontal (f g) and vertical (L g) densities. We argue that for NOAA AVHRR data sets based on observations with a spatial resolution of a few kilometres the most appropriate way to resolve this ambiguity is to assume that the vegetated part of a pixel is covered by dense vegetation (i.e., its leaf area index is high), and to calculate f g = (NDVI-NDVIo)/(NDVI 2 -NDVIo), where NDVIo (bare soil) and NDVI 2 (dense vegetation) are speci® ed as global constants independent of vegetation/soil type. Global (0´15ß) 2 spatial resolution monthly maps of f g were produced from a 5-year NDVI climatology and incorporated in the NWS models. As a result, the model surface ¯ uxes were improved.
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A study has been carried out using LOWTRAN-7 simulations of the Along-Track Scanning Radiometer (ATSR) data at 11 and 12 μm wavelengths to compare the merits of the multi-angle technique with those of the currently used multi-channel technique (split-window method) to retrieve both sea surface temperature (SST) and land surface temperature (LST). To this end a simple single-channel double-angle viewing model is presented, which relates actual surface temperature to the two brightness temperatures measured from space in the two views of interest (ATSR nadir and forward). Subsequently, statistical retrieval coefficients for the double angle and split-window techniques are derived via a regression analysis of the synthetic measurements. The results show that the double angle technique is capable of producing SSTs with a standard deviation of 0.23 deg K if the satellite data are error free and, furthermore, confirm the advantage of the double-viewing angle technique in comparison with the split-window technique for LST determination in homogeneous surfaces if the emissivity's spectral variation and the emissivity's angular variation, are of the same order of magnitude. Finally we present the preliminary results obtained using the proposed model from ATSR data over a semi-arid region of New South Wales, Australia provided by Prata, and over the Pacific Ocean provided by Barton.
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A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST-NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 608N) during the summer growing season (April-September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for veg- etation growth (the typical situation for low latitudes of the study area and during the midseason), the LST- NDVI correlation is negative. However, when energy is the limiting factor for vegetation growth (in higher latitudes and elevations, especially at the beginning of the growing season), a positive correlation exists be- tween LST and NDVI. Multiple regression analysis revealed that during the beginning and the end of the growing season, solar radiation is the predominant factor driving the correlation between LST and NDVI, whereas other biophysical variables play a lesser role. Air temperature is the primary factor in midsummer. It is concluded that there is a need to use empirical LST-NDVI relationships with caution and to restrict their application to drought monitoring to areas and periods where negative correlations are observed, namely, to conditions when water—not energy—is the primary factor limiting vegetation growth.
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Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local through global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from space. However, retrieving LST is still a challenging task since the LST retrieval problemis ill-posed. This paper reviews the current status of selected remote sensing algorithms for estimating LST from thermal infrared (TIR) data. A brief theoretical background of the subject is presented along with a survey of the algorithms employed for obtaining LST from space-based TIR measurements. The discussion focuses on TIR data acquired from polar-orbiting satellites because of their widespread use, global applicability and higher spatial resolution compared to geostationary satellites. The theoretical framework and methodologies used to derive the LST from the data are reviewed followed by the methodologies for validating satellite-derived LST. Directions for future research to improve the accuracy of satellite-derived LST are then suggested.
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We update the Goddard Institute for Space Studies (GISS) analysis of global surface temperature change, compare alternative analyses, and address questions about perception and reality of global warming. Satellite-observed nightlights are used to identify measurement stations located in extreme darkness and adjust temperature trends of urban and peri-urban stations for non-climatic factors, verifying that urban effects on analyzed global change are small. Because the GISS analysis combines available sea surface temperature records with meteorological station measurements, we test alternative choices for the ocean data, showing that global temperature change is sensitive to estimated temperature change in polar regions where observations are limited. We use simple 12-month (and n×12) running means to improve the information content in our temperature graphs. Contrary to a popular misconception, the rate of warming has not declined. Global temperature is rising as fast in the past decade as in the prior two decades, despite year-to-year fluctuations associated with the El Nino-La Nina cycle of tropical ocean temperature. Record high global 12-month running-mean temperature for the period with instrumental data was reached in 2010.
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We use a simple radiative transfer model with vegetation, soil, and atmospheric components to illustrate how the normalized difference vegetation index (NDVI), leaf area index (LAI), and fractional vegetation cover are dependent. In particular, we suggest that LAI and fractional vegetation cover may not be independent quantitites, at least when the former is defined without regard to the presence of bare patches between plants, and that the customary variation of LAI with NDVI can be explained as resulting from a variation in fractional vegetation cover. The following points are made: i) Fractional vegetation cover and LAI are not entirely independent quantities, depending on how LAI is defined. Care must be taken in using LAI and fractional vegetation cover independently in a model because the former may partially take account of the latter; ii) A scaled NDVI taken between the limits of minimum (bare soil) and miximum fractional vegetation cover is insenstive to atmospheric correction for both clear and hazy conditions, at least for viewing angles less than about 20 degrees from nadir; iii) A simple relation between scaled NDVI and fractional vegetation cover, previously described in the literature, is further confirmed by the .simulations; iv) The sensitive dependence of LAI on NDVI when the former is below a value of about 2–4 may be viewed as being due to the variation in the bare soil component.
Examination of the scientific priorities for the International Geosphere Biosphere Programme (IGBP) reveals a requirement for global land data sets in several of its Core Projects. These data sets need to be at several space and time scales. Requirements are demonstrated for the regular acquisition of data at spatial resolutions of 1 km and finer and at high temporal frequencies. Global daily data at a resolution of approximately 1 km are sensed by the Advanced Very High Resolution Radiometer (AVHRR), but they have not been available in a single archive. It is proposed, that a global data set of the land surface is created from remotely sensed data from the AVHRR to support a number of IGBP's projects. This data set should have a spatial resolution of 1 km and should be generated at least once every 10 days for the entire globe. The minimum length of record should be a year, and ideally a system should be put in place which leads to the continuous acquisition of 1 km data to provide a base line data set prior to the Earth Observing System (EOS) towards the end of the decade. Because of the high cloud cover in many parts of the world, it is necessary to plan for the collection of data from every orbit. Substantial effort will be required in the preprocessing of the data set involving radiometric calibration, atmospheric correction, geometric correction and temporal compositing, to make it suitable for the extraction of information.
Classification-based global emissivity is needed for the National Aeronautics and Space Administration Earth Observing System Moderate Resolution Imaging Spectrometer (NASA EOS/MODIS) satellite instrument land surface temperature (LST) algorithm. It is also useful for Landsat, the Advanced Very High Resolution Radiometer (AVHRR) and other thermal infrared instruments and studies. For our approach, a pixel is classified as one of fourteen 'emissivity classes' based on the conventional land cover classification and dynamic and seasonal factors, such as snow cover and vegetation index. The emissivity models we present provide a range of values for each emissivity class by combining various spectral component measurements with structural factors. Emissivity statistics are reported for the EOS/MODIS channels 31 and 32, which are the channels that will be used in the LST split-window algorithm.