Daily Land Surface Temperature Research Articles

Biophysical forcings of land‐use changes from potential forestry activities in North America

Land‐use changes through forestry and other activities alter not just carbon storage, but biophysical properties, including albedo, surface roughness, and canopy conductance, all of which affect temperature. This study assessed the biophysical forcings and climatic impact of vegetation replacement across North America by comparing satellite‐derived albedo, land surface temperature (LST), and evapotranspiration (ET) between adjacent vegetation types. We calculated radiative forcings (RF) for potential local conversions from croplands (CRO) or grasslands (GRA) to evergreen needleleaf (ENF) or deciduous broadleaf (DBF) forests. Forests generally had lower albedo than adjacent grasslands or croplands, particularly in locations with snow. They also had warmer nighttime LST, cooler daily and daytime LST in warm seasons, and smaller daily LST ranges. Darker forest surfaces induced positive RFs, dampening the cooling effect of carbon sequestration. The mean (±SD) albedo‐induced RFs for each land conversion were equivalent to carbon emissions of 2.2 ± 0.7 kg C/m2 (GRA–ENF), 2.0 ± 0.6 kg C/m2 (CRO–ENF), 0.90 ± 0.50 kg C/m2 (CRO–DBF), and 0.73 ± 0.22 kg C/m2 (GRA–DBF), suggesting that, given the same carbon sequestration potential, a larger net cooling (integrated globally) is expected for planting DBF than ENF. Both changes in LST and ET induce longwave RFs that sometimes had values comparable to or even larger than albedo‐induced shortwave RFs. Sensible heat flux, on average, increased when replacing CRO with ENF, but decreased for conversions to DBF, suggesting that DBF tends to cool near‐surface air locally while ENF tends to warm it. This local temperature effect showed some seasonal variation and spatial dependence, but did not differ strongly by latitude. Overall, our results show that a carbon‐centric accounting is, in many cases, insufficient for climate mitigation policies. Where afforestation or reforestation occurs, however, deciduous broadleaf trees are likely to produce stronger cooling benefits than evergreen needleleaf trees provide.

Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data

Land surface temperature (LST) is a crucial parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. These studies require thermal infrared (TIR) images at both high temporal and spatial resolution to retrieve LST. However, currently, no single satellite sensors can deliver TIR data at both high temporal and spatial resolution. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of TIR data, but rare of those can enhance both spatial and temporal details. This paper presents a new data fusion algorithm for producing Landsat-like LST data by blending daily MODIS and periodic Landsat TM datasets. The original Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was improved and modified for predicting thermal radiance and LST data by considering annual temperature cycle (ATC) and urban thermal landscape heterogeneity. The technique of linear spectral mixture analysis was employed to relate the Landsat radiance with the MODIS one, so that the temporal changes in radiance can be incorporated in the fusion model. This paper details the theoretical basis and the implementation procedures of the proposed data fusion algorithm, Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT). A case study was conducted that predicted LSTs of five dates in 2005 from July to October in Los Angeles County, California. The results indicate that the prediction accuracy for the whole study area ranged from 1.3K to 2K. Like existing spatio-temporal data fusion models, the SADFAT method has a limitation in predicting LST changes that were not recorded in the MODIS and/or Landsat pixels due to the model assumption.

Application of a Land Surface Model Using Remote Sensing Data for High Resolution Simulations of Terrestrial Processes

Most current land surface models (LSMs) coupled to regional climate models (RCMs) have been implemented at the several tens of kilometer spatial scales. Modeling land surface processes in LSMs at a finer resolution is necessary for improvements in terrestrial water and energy predictions especially for small catchments. This study has therefore assessed the applicability of high-resolution simulations for terrestrial processes to a small study basin from the Common Land Model (CoLM) using 1-km surface boundary conditions (SBCs) based on remote sensing products. The performance of the CoLM simulations at finer (1-km) and coarser (30-km) resolutions are evaluated for daily runoff and land surface temperature results which have a significant influence on the terrestrial water and energy cycles. The daily stream water temperature is also estimated by a linear regression function of the 1-km daily land surface temperature prediction. The daily stream runoff and temperature results are compared with observations from a stream gauge station, and the daily land surface temperature prediction is compared with the 1-km remote sensing product. It is observed that the high-resolution CoLM results can reasonably capture seasonal variations in both daily runoff and temperatures crucial to the terrestrial water and energy budget.

Reconstruction of Time-Series MODIS LST in Central Qinghai-Tibet Plateau Using Geostatistical Approach

We present an approach for reconstructing land surface temperature (LST) time series over mountainous areas based on Regression Kriging (RK) technique and a data processing scheme for filtering out LST noise and artifacts. A total of 1462 eight-day composite Moderate Resolution Imaging Spectroradiometer LST images over central Qinghai-Tibet Plateau over 2003-2010 are reconstructed. The regression model includes four auxiliary predictors-latitude, longitude, elevation, and NDVI-which are proven to be a good estimator for the 8-day LST. Comparison of ground surface temperature (GST) measurements at eight meteorological stations with the raw and reconstructed LST series shows that the reconstruction strategy can effectively recover complete high-quality over-land LST maps and significantly improve the consistency between LST and GST.

Reconstruction of the land surface temperature time series using harmonic analysis

Satellite remote sensing is an important approach for obtaining land surface temperature (LST) over wide temporal and spatial ranges. However, the presence of clouds generates numerous missing and abnormal values that affect the application of LST data. To fill data gaps and improve data quality, the Harmonic ANalysis of Time Series (HANTS) algorithm was employed to remove cloud-affected observations and reconstruct the Moderate Resolution Imaging Spectroradiometer (MODIS) LST data taken in the year 2005 for the Yangtze River Delta region of China. Analysis of MODIS data quality indicated that the yearly proportion of high-quality LST data in this region was less than 50% with numerous missing and low-quality data points. To reconstruct 8-day LST via the removal of cloud-contaminated observations, we applied pixel-by-pixel harmonic fitting to the time series and used fitted values to replace the missing and abnormal values in the original LST data. To evaluate the reconstruction performance, a simulated dataset was generated according to the percentage of cloud coverage in each 8-day period. Satisfactory validation results indicate that the harmonic method can effectively fit the NA Values caused by cloud cover and fill data gaps in the LST data, which can significantly improve the practical value of the MODIS LST dataset.

Mapping air temperature using time series analysis of LST: the SINTESI approach

Abstract. This paper presents a new procedure to map time series of air temperature (Ta) at fine spatial resolution using time series analysis of satellite-derived land surface temperature (LST) observations. The method assumes that air temperature is known at a single (reference) location such as in gridded climate data with grid size of the order of 35 km × 35 km. The LST spatial and temporal pattern within a grid cell has been modelled by the pixel-wise ratios r (x,y,t) of the LST at any location to the LST at a reference location. A preliminary analysis of these patterns over a decade has demonstrated that their intra-annual variability is not negligible, with significant seasonality, even if it is stable throughout the years. The intra-annual variability has been modeled using Fourier series. We have evaluated the intra-annual variability by theoretically calculating the yearly evolution of LST (t) for a range of cases as a function of terrain, land cover and hydrological conditions. These calculations are used to interpret the observed LST (x,y,t) and r (x,y,t). The inter-annual variability has been evaluated by modeling each year of observations using Fourier series and evaluating the interannual variability of Fourier coefficients. Because of the negligible interannual variability of r (x,y,t), LST (x,y,t) can be reconstructed in periods of time different from the ones when LST observations are available. Time series of Ta are generated using the ratio r (x,y,t) and a linear regression between LST and Ta. Such linear regression is applied in two ways: (a) to estimate LST at any time from observations or forecasts of Ta at the reference location; (b) to estimate Ta from LST at any location. The results presented in this paper are based on the analysis of daily MODIS LST observations over the period 2001–2010. The Ta at the reference location was gridded data at a node of a 35 km × 35 km grid. Only one node was close to our study area and was used for the work presented here. The regression of Ta on LST was determined using concurrent observations of Ta at the four available weather stations in the Valle Telesina (Italy), our study area. The accuracy of our estimates is consistent with literature and with the combined accuracy of LST and Ta. We obtained comparable error statistics when applying our method to LST data during periods different but adjacent to the periods used to model of r (x,y,t). The method has also been evaluated against Ta observations for earlier periods of time (1984–1988), although available data are rather sparse in space and time. Slightly larger deviation were obtained. In all cases five days of averages from estimated and observed Ta were compared, giving a better accuracy.

Daily MODIS Land Surface Temperature Data for the Analysis of the Heat Requirements of Grapevine Varieties

Heat requirements for grapevine varieties have been widely used to characterize potential growing regions for viticulture. One of the most common landscape classifications is based on the Winkler Index (WI) values, defined as the total summation of daily average air temperature above 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> C from April 1 to October 31 in the Northern Hemisphere. Mapping WI is commonly performed by spatial interpolation of temperature data collected with weather station (WS) networks. However, in complex terrain such as the European Alps, these are usually irregularly and sparsely distributed or unavailable. This renders traditional geospatial interpolation approaches unreliable. As an alternative, thermal remote sensing data, which are intrinsically spatialized, can be used. The aims of this paper are the following: (1) to provide time series of WI-value maps from 2003 to 2010, by means of the Moderate-resolution Imaging Spectroradiometer (MODIS) land surface temperature (MOD11A1 and MYD11A1) daily data and (2) to calibrate them using ground truth data, collected by two WS networks, (3) leading to a classification of the study area into WI regions. WI values from WS data and from MODIS data exhibit a strong linear regression, close to the 1 : 1 prediction with an offset of 42.02 and a slope of 0.90. These coefficients have been used to calibrate the original map, before the following classification. The study area was found to have growing degree days that correspond to four Winkler Regions. The use of MODIS data represents a robust and straightforward method for selectively detecting areas suitable for grapevine varieties, particularly in regions with sparsely distributed WSs.

Modeling diurnal variation of ground thermal radiance images using energy balance model and endmember composing technique

Modeling and analyzing dynamic changes of land thermal radiance scenes play an important role in thermal remote sensing. In this paper, the diurnal variation of ground surface thermal scene is mainly discussed. Firstly, based on the land surface energy balance equation, the diurnal variation of land surface temperatures (LSTs) over bare land covers were simulated by an analytical thermal model with second harmonic terms, and the diurnal LST variation of vegetation canopy was simulated using the Cupid model. Secondly, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and ratio resident-area index (RRI) were used to evaluate the endmember abundance of four land cover types including vegetation, bare soil, impervious and water area, which were calculated from IKONOS visible and near infrared (VNIR) bands. Finally, the thermal radiance scenes at various times and view angles were modeled based on the linear-energy-mixing hypothesis. The results showed that the simulated daily LST variations for vegetated and bare surfaces are correlated with the measured values with a maximum standard deviation of 2.7°C, that land thermal radiant textures with high-resolution are restored from the linear-energy-mixing method, and that the information abundance of the scene are related to the distribution of land cover, the imaging time, and the view angle.

Heat waves measured with MODIS land surface temperature data predict changes in avian community structure

Heat waves are expected to become more frequent and severe as climate changes, with unknown consequences for biodiversity. We sought to identify ecologically-relevant broad-scale indicators of heat waves based on MODIS land surface temperature (LST) and interpolated air temperature data and assess their associations with avian community structure. Specifically, we asked which data source, time periods, and heat wave indices best predicted changes in avian abundance and species richness. Using mixed effects models, we analyzed associations between these indices and data from the North American Breeding Bird Survey in the central United States between 2000 and 2007 in four ecoregions and five migratory and nesting species groups. We then quantified avian responses to scenarios of severe, but commonly-occurring early, late, and summer-long heat waves. Indices based on MODIS LST data, rather than interpolated air temperatures, were more predictive of avian community structure. Avian communities were more related to 8-day LST exceedances (positive anomalies only); and were generally more sensitive to summer-long heat waves. Across the region, abundance, and to a lesser extent, species richness, declined following heat waves. Among the ecoregions, relationships were most consistently negative in the southern and montane ecoregions, but were positive in a more humid northern ecoregion. Among migratory groups, permanent resident species were the most sensitive, declining in abundance following a summer-long heat wave by 19% and 13% in the montane and southern ecoregions, respectively. Ground-nesting species, which declined in the south by 12% following a late summer heat wave, were more sensitive than avifauna overall. These results demonstrate the value of MODIS LST data for measuring ecologically-relevant heat waves across large regions. Ecologically, these findings highlight the importance of extreme events for avian biodiversity and the considerable variation in response to environmental change associated with different functional groups and geographic regions. The magnitude of the relationships between avian abundance and heat waves reported here raises concerns about the impacts of more frequent and severe heat waves in a warming climate.

Surface temperature spatial and temporal variations in North America from homogenized satellite SMMR‐SSM/I microwave measurements and reanalysis for 1979–2008

We have developed procedures for deriving land surface temperature and homogenizing 30 years of daily microwave brightness temperatures from the NOAA/NASA Nimbus 7 scanning multichannel microwave radiometer (SMMR) and Defense Meteorological Satellite Program Special Sensor Microwave/Imager (DMSP SSM/I) Pathfinder EASE‐Grid database. Processing includes normalization of variable acquisition overpass time, removing the effects of changing satellite orbits, intercalibration of sensors, and filling gaps between missing data. The derived new database over North America (above 45°N), limited to snow‐free periods, provides the first estimate of the trends of consistent mean daily summer land surface temperature over the last three decades. By comparison with near‐surface air temperatures derived from the European Centre for Medium‐Range Weather Forecasts 40 year Reanalysis (ERA‐40), the National Centers for Environmental Prediction North American Regional Reanalysis (NARR), and the exhaustive ground‐based meteorological measurements across Canada, we highlighted significant systematic biases in the satellite‐derived surface temperatures for the 1983–1991 period, spanning from the second half of Nimbus 7 SMMR lifetime (1983–1987; mean bias of −0.87°C compared to ERA‐40) and the entire period of DMSP‐F8 SSM/I lifetime (1987–1991; mean bias of −1.56°C compared to ERA‐40). The biased data were corrected with use of a relative offset derived from ERA‐40 and DMSP‐F11/F13 mean difference over the 1992–2002 period. The comparison of corrected data from the 9 day overlap between SMMR and DMSP‐F8 SSM/I and from ground‐based meteorological measurements over the 1983–1991 biased period shows the usefulness of the correction method. The mean difference between corrected satellite‐derived surface temperature and in situ meteorological air temperature is +0.05°C ± 1.85°C for the entire period (1979–2008). The NARR data appear, in general, to be too warm by about 1°C. The satellite‐derived homogenized database gives new observational evidence for global warming over North American regions (mean summer temperature trend of +0.018°C/a throughout the studied period) with regional variable trends, in agreement with reanalysis and in situ measurement trends. However, over the Canadian Arctic tundra, the increase in observed land surface temperatures appears slightly less than the estimates based on near‐surface air temperature.

Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data

Continuous monitoring of extreme environments, such as the European Alps, is hampered by the sparse and/or irregular distribution of meteorological stations, the difficulties in performing ground surveys and the complexity of interpolating existing station data. Remotely sensed Land Surface Temperature (LST) is therefore of major interest for a variety of environmental and ecological applications. But while MODIS LST data from the Terra and Aqua satellites are aimed at closing the gap between data demand and availability, clouds and other atmospheric disturbances often obscure parts or even the entirety of these satellite images. A novel algorithm is presented in this paper, which is able to reconstruct incomplete MODIS LST maps. All nine years of the available daily LST data (2000–2008) have been processed, allowing the original LST map resolution of 1,000 m to be improved to 200 m, which means the resulting LST maps can be applied at a regional level. Extracted time series and aggregated data are shown as examples and are compared to meteorological station time series as an indication of the quality obtained.

Using the MODIS land surface temperature product for mapping permafrost: an application to northern Québec and Labrador, Canada

AbstractThe Land Surface Temperature (LST) products of the Moderate Resolution Imaging Spectroradiometers (MODIS) aboard NASA's Terra and Aqua satellites were used to develop maps of annual near‐surface temperatures for comparison with the spatial distribution of permafrost and boundaries of the permafrost zones. The methodological approach involved fitting a sinusoidal model over the daily LST readings to reproduce seasonal thermal variations near the ground for each 1‐km2 pixel. Calculations of mean annual surface temperatures and of thawing and freezing indices led to the development of regional maps, in this case for northern Québec and Labrador. The maps show the expected geographic distribution of near‐surface temperatures and acceptably represent known permafrost boundaries. Ongoing efforts to incorporate snow and vegetation cover from complementary remotely sensed data should improve the ground surface temperature mapping capability based on this approach. Copyright © 2009 John Wiley &amp; Sons, Ltd.

Assessment of a distributed biosphere hydrological model against streamflow and MODIS land surface temperature in the upper Tone River Basin

Land surface temperature (LST) is a key parameter in land–atmosphere interactions. The recently released Moderate Resolution Imaging Spectroradiometers (MODIS) LST Version 5 products have provided good tools to evaluate water and energy budget modelling for river basins. In this study, a distributed biosphere hydrological model (WEB-DHM; so-called water and energy budget-based distributed hydrological model) that couples a biosphere scheme (SiB2) with a geomorphology-based hydrological model (GBHM), is applied to the upper Tone River Basin where flux observations are not available. The model facilitates a better understanding of the water and energy cycles in this region. After being calibrated with discharge data, WEB-DHM is assessed against observed streamflows at four major gauges and MODIS LST. Results show that long-term streamflows including annual largest floods are well reproduced. As well, both daytime and nighttime LSTs simulated by WEB-DHM agree well with MODIS observations for both basin-averaged values and spatial patterns. The validated model is then used to analyze water and energy cycles of the upper Tone River Basin. It was found that from May to October, with relatively large leaf area index (LAI) values, the simulated daily maximum LST is close to soil surface temperature (Tg) since Tg is much greater than canopy temperature (Tc) in their peak values; while the daily minimum LST appears similar to Tc. For other months with relatively small LAI values, the diurnal cycles of LST closely follow Tg.

Comparison of land surface temperature (LST) modeled with a spatially-distributed solar radiation model (SRAD) and remote sensing data

The solar radiation model SRAD was applied to a Mediterranean mountainous environment in southern California for estimating land surface temperature (LST). The simulated SRAD LST results were compared with high- and meso-resolution satellite-based LST data at daily, monthly and annual temporal scales to identify potential ways of improving the LST accuracy in either the SRAD or satellite-based approaches. It was found that mean monthly and annual LST from SRAD closely matched the MODIS LST observations (but for the fact they were from 0.7 to 1.5 °C lower) while the daily LST from SRAD agreed less well with ASTER observations and were 2.1 °C and 4.8 °C higher for 01/23/05 and 07/21/06, respectively. High mountainous, steep and south/north facing slopes resulted in large discrepancies in LST estimates and the winter LST estimates are more sensitive to terrain factors and their associated land use/cover characteristics than the summer estimates. It is therefore suggested that vertically variable elevation lapse rates, spatially distributed surface albedo and leaf area index for the time-specific simulations that represent the heterogeneity of land surface characteristics be used in SRAD inputs. The results also show how instantaneous model outputs rather than lumped average daily outputs offer better comparisons with satellite based data as well.

Validating MODIS land surface temperature products using long-term nighttime ground measurements

The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the NASA Terra and Aqua Earth Observing System satellites, provides multiple land surface temperature (LST) products on a daily basis. However, these products have not been adequately validated. This paper presents preliminary results of validating two MODIS Terra daily LST products, MOD11_L2 (version 4) and MOD07_L2 (version 4), using the FLUXNET and Carbon Europe Integrated Project (CarboEurope-IP) long-term ground measurements over eight vegetated sites. Since ground-measured LSTs were only available over one fixed point in each validation site, the study was carefully designed to mitigate the scale mismatch issue by using nighttime ground measurements concurrent to more than 1800 MODIS Terra overpasses. The preliminary results show that MOD11_L2 LSTs have smaller absolute biases and root mean squared errors (RMSE) than those of MOD07_L2 LSTs in most cases. The match of MOD11_L2 LSTs with ground measurements in the Brookings, Audubon, Canaan Valley, and Black Hills sites is good, yielding absolute biases less than 0.8 °C and RMSEs less than 1.7 °C. In the Fort Peck, Hainich, Tharandt, and Bondville sites, MOD11_L2 LSTs were underestimated by 2–3 °C. Biases in MOD11_L2 LSTs correlate to those in MOD07_L2 LSTs. Since the MOD07_L2 LST product is one of the input parameters to the MOD11_L2 LST algorithm, biases in MOD11_L2 LSTs may be influenced by biases in MOD07_L2 LSTs. The errors in both products depend weakly on sensor view zenith angle but are independent of surface air temperature, humidity, wind speed, and soil moisture.

MODIS land surface temperature data detects thermal anomaly preceding 8 October 2005 Kashmir earthquake

During the morning (03:50:40 UTC) of 8 October 2005 a major (M w 7.6) shallow focus (26 km) earthquake struck Kashmir (Himalayan region). Its epicentre was located 10 km NNE of Muzaffarabad (USGS 2005, Magnitude 7.6—Pakistan, available online at http://earthquake.usgs.gov/eqcenter/eqinthenews/2005/usdyae/). The present manuscript is an attempt to study the development of thermal anomaly in land surface temperature (LST) preceding this earthquake. Using data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard National Aeronautics and Space Administration (NASA) Terra satellite, the daily daytime LST images have been analysed for the correlation between LST variations and Kashmir earthquakes. An evident correlation of thermal anomaly in LST that is apparently related to pre‐seismic activity has been identified. An attempt has also been made to quantify the change in LST (in °C) with reference to previous day temperature values and background data (MODIS LST data from 2000–2004). A 4–8°C rise in LST to the south of the earthquake epicentre has been observed seven days before the major event. Air temperature data from two meteorological stations (Islamabad and Srinagar) also supports the observations made through MODIS LST data. The role of terrain parameters like rock types, vegetation and topography upon the spatial and temporal variations of anomalous temperature area have been studied.

Total water vapor column retrieval from MSG-SEVIRI split window measurements exploiting the daily cycle of land surface temperatures

A physically based algorithm for the retrieval of total water vapor column (TWC) over cloud-free land surfaces proposed by Kleespies and McMillin [Kleespies, J.T., McMillin L.M. (1990). Retrieval of precipitable water from observations in the Split Window over varying surface temperatures. Journal of Applied Meteorology, 29, 851–862.] is evaluated and extended for use in atmospheric correction and surface irradiance calculation schemes. Thermal infrared split window channels at 10.8 and 12.0 μm of the MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager) instrument are used. The proposed algorithm takes advantage of the improved measurement capabilities of the MSG-SEVIRI instrument with its 15 min temporal resolution and its radiometric accuracy of 0.25 K and 0.37 K in the 10.8 and 12.0 μm channels. The temporal resolution allows exploitation of the daily land surface temperature variation. There is no further need for explicit auxiliary information on air and land surface temperatures, which is difficult to obtain on an operational basis. Updated coefficients for the split window parameterization are derived based on simulations of ‘top-of-atmosphere’ SEVIRI brightness temperatures for the globally representative Thermodynamic Initial Guess Retrieval (TIGR3) set of radiosonde profiles. It turns out that the linear dependency on the transmission ratio in both split window channels as originally proposed by Kleespies & McMillin [Kleespies, J.T., McMillin L.M. (1990). Retrieval of precipitable water from observations in the Split Window over varying surface temperatures. Journal of Applied Meteorology, 29, 851–862.] has to be extended towards a non-linear approach in order to make it applicable to the full range of global atmospheric conditions. Sensitivity studies reveal that the parameterization relies on the availability of input brightness temperatures with a variation larger than approximately 5 K during the daily cycle. The new TWC algorithm was tested with MSG-SEVIRI data for European and African regions for the period March–August 2004 and compared with radiosonde data. The results show that the algorithm is capable of producing TWC values with a mean bias of − 0.2 mm and an RMSE of 6.8 mm. From the total amount of 2583 coincidences for all viewing zenith angles both for winter and summer conditions, 82% were within a ± 5 mm and 94% were within a ± 10 mm difference interval between MSG-based and radiosonde-based TWC. A second comparison to European GPS measurements for the same period from March to August 2004 reveals a bias of − 3.0 mm and an RMSE of 6.0 mm. This result is obtained for 11 UTC GPS measurements which proved to match best the MSG-TWC values. Comparing MSG-TWC to daily cloud-free mean GPS values shows a lower bias of − 2.56 mm and an increased RMSE of 6.6 mm. These findings support the usefulness of the new MSG-based algorithm for surface irradiance calculations and atmospheric correction purposes.

Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass, location and elevation information

The MODerate Resolution Imaging Spectroradiometer (MODIS) instrument on‐board the Terra and Aqua satellites is a critical tool for providing daily estimates of land surface temperature (LST). Terra launched in late 1999 has a morning (AM) overpass, whereas Aqua launched in early 2002 has an afternoon (PM) overpass. Generally, LST is expected, under cloudless conditions, to be warmer in the early afternoon than the morning due to the link between maximum skin temperature and solar insolation peak time, therefore the Aqua PM LST is likely to be closer to the maximum daily LST than that acquired from Terra. This letter investigated differences between the Aqua MODIS PM and Terra MODIS AM LST estimates over a range of land cover classes, locations, and dates, across Canada. The aim was to develop a simple adjustment which can be applied to Terra AM LST estimates to approximate a “synthetic” Aqua PM LST product from 2000 to mid‐2002, thereby providing a seamless afternoon MODIS LST product from 2000 to 2006. Results indicate that there are statistically significant differences between the AM and PM LST ranging from 0.3°C to 3.2°C depending on cover type, and between 1.2° and 5.0° depending on time of year. On average, over 90% of the variation observed in the PM record can be explained by the AM LST, land cover types and location.

Quality assessment and validation of the MODIS global land surface temperature

This paper presents an evaluation of the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands and the status of land surface temperature (LST) version-3 standard products retrieved from Terra MODIS data. The accuracy of daily MODIS LST products has been validated in more than 20 clear-sky cases with in situ measurement data collected in field campaigns in 2000–2002. The MODIS LST accuracy is better than 1°C in the range from −10 to 50°C. Refinements and improvements were made to the new version of MODIS LST product generation executive code. Using both Terra and Aqua MODIS data for LST retrieval improves the quality of the LST product and the diurnal feature in the product due to better temporal, spatial and angular coverage of clear-sky observations.

A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas

A strong linear relationship is found between Special Sensor Microwave/Imager (SSM/I) microwave (19 and 37 GHz) surface emissivities at horizontal and vertical polarizations over snow- and ice-free land surfaces. This allows retrieving the land surface emissivity and temperature from satellite microwave brightness temperatures after atmospheric corrections. Over the Canadian sub-arctic continental area, we show that the main factor modifying the emissivity is the fraction of water surface (FWS) within a pixel. Accordingly, a map of the fraction of water surface across the Canadian landmass is derived, given a correspondence within 6% as compared to the 1 km 2 Canadian National Topographic Database of water-covered areas. The microwave-derived surface temperatures are compared to synchronous in situ air and ground surface temperatures and also with independent satellite IR measurements over areas without snow or ice. Root mean square differences range between 2° and 3.5°, with mean bias error of the order of 1–3°. Better results are always obtained with the 37 GHz channel rather than with the 19 GHz channel. Over dense vegetation, the microwave-derived surface temperature is closer to the air temperature (at surface level) than to the ground temperature. The proposed simple retrieval algorithm, not sensitive to cloud cover, appears very useful for monitoring summer interannual or seasonal trends of the fraction of surface water, as well as the daily land surface temperature variation, which are very important parameters in environmental change analysis.