Satellite Land Surface Temperature Research Articles

Comparison of in-situ, aircraft, and satellite land surface temperature measurements over a NOAA Climate Reference Network site

Land surface temperature (LST) is a key variable for studying the energy and water vapor exchange at the biosphere–atmosphere interface. In an effort to better quantify the spatial variability and overall representativeness of single-point LST measurements being recorded at NOAA's Climate Reference Network (CRN) sites and to improve the accuracy of satellite LST measurements, airborne flight campaigns were conducted over a CRN site in Crossville, Tennessee, USA during 2010 to 2011. Multiple measurements of LST were made using infrared temperature sensors at micrometeorological tower sites and onboard an instrumented Piper Navajo airborne research aircraft. In addition to this, coincident LST products from the moderate resolution imaging spectroradiometer (MODIS) instruments (Collection 5), onboard NASA Terra and Aqua Earth Observing System satellites were used. In this paper the comparison of LST measurements made from multiple platforms are presented. Our study showed that the temporal and spatial variability of surface temperature as indicated by the standard deviation of the brightness temperature (Tb) during the flight periods were <1.7°C. Aircraft and tower-based Tb during the flight periods agreed well with a root mean square error (RMSE) of <1.3°C. The daytime MODIS LST was lower than the tower and aircraft-based LST, but higher than the daytime near surface air temperature (Ta). MODIS LST showed a positive and lower bias with the nighttime tower-based LST, but with slightly higher RMSE than the daytime dataset. The MODIS LST showed better correlation with the tower-based LST than Ta during clear sky conditions due to the complex relationship between air and surface temperature. Including both day and nighttime data the MODIS LST showed a bias of −0.56°C, RMSE of 2.84°C, and standard deviations of the difference of 2.79°C when compared to the mean tower-based LST at the site. Tower-based Ta explained 98% of the variance in LST during nighttime conditions with a bias of ~0.8°C and RMSE of 0.86°C.

Modeling insect population fluctuations with satellite land surface temperature

The simulation of insect pest populations in agricultural and forest ecosystems is an important and useful tool for integrated pest management (IPM). Insect population models are mainly driven by environmental temperature data, which are usually collected from agrometeorological stations or derived from geographic statistical extrapolations. The present study describes the modeling of olive fly (Bactrocera oleae) populations in the Eastern Mediterranean region using the MODIS (Moderate Resolution Image Spectro Radiometer) land surface temperature (LST) product from NASA TERRA satellite. These data, together with in situ temperature data, were used to estimate the tree-canopy temperatures at the pixel resolution (1km). The estimated canopy temperature was used as input for the olive fly population model. Our main aim was to demonstrate the use of satellite-acquired information for modelling biological and ecological phenomena. Eleven years (2001–2012) of olive fly population fluctuations were simulated for three different geographic locations, representing different geo-climatic conditions. The model successfully simulated the seasonal population fluctuations throughout the 11-year period and did a good job of connecting all of the life stages of the insect. To evaluate the validity of these findings, we compared them with adult olive-fly trapping data. We observed a high degree of correlation between the trapping data and our model's predictions. Here, we demonstrate that satellite thermal data can be used to predict insect pest population fluctuations for IPM purposes. The study also advances some new modelling concepts, such as the “window of opportunity” which links physiological development with chronological age.

Correction of real-time satellite precipitation with multi-sensor satellite observations of land surface variables

Precipitation is an important hydro-meteorological variable, and is a primary driver of the water cycle. In large parts of the world, real-time ground-based observations of precipitation are sparse and satellite-derived precipitation products are the only information source.We used changes in satellite-derived soil moisture (SM) and land surface temperature (LST) to reduce uncertainties in the real-time TRMM Multi-satellite Precipitation Analysis product (TMPA-RT). The Variable Infiltration Capacity (VIC) model was used to model the response of LST and SM on precipitation, and a particle filter was used to update TMPA-RT. Observations from AMSR-E (LPRM and LSMEM), ASCAT, SMOS and LST from AMSR-E were assimilated to correct TMPA-RT over the continental United States.Assimilation of satellite-based SM observations alone reduced the false detection of precipitation (by 85.4%) and the uncertainty in the retrieved rainfall volumes (5%). However, a higher number of observed rainfall events were not detected after assimilation (34%), compared to the original TMPA-RT (46%). Noise in the retrieved SM changes resulted in a relatively low potential to reduce uncertainties. Assimilation of LST observations alone increased the rainfall detection rate (by 51%), and annual precipitation totals were closer to ground-based precipitation observations. Combined assimilation of both satellite SM and LST, did not significantly reduce the uncertainties compared to the original TMPA-RT, because of the influence of satellite SM over LST. However, in central United States improvements were found after combined assimilation of SM and LST observations. This study shows the potential for reducing the uncertainties in TMPA-RT estimates over sparsely gauged areas.

Can satellite land surface temperature data be used similarly to river discharge measurements for distributed hydrological model calibration?

A new methodology is proposed for the calibration of distributed hydrological models at the basin scale by constraining an internal model variable using satellite data of land surface temperature (LST). The model algorithm solves the system of energy and mass balances in terms of a representative equilibrium temperature that governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is compared to operational satellite LST, while calibrating soil hydraulic parameters and vegetation variables differently in each pixel, minimizing the errors. This procedure is compared to the traditional calibration using only discharge measurements. The distributed energy water balance model, Flash-flood Event-based Spatially-distributed rainfall–runoff Transformation – Energy Water Balance model (FEST-EWB), is used to test this approach. This methodology is applied to the Upper Yangtze River basin (China) using MODIS LST retrieved from satellite data in the framework of the NRSCC-ESA DRAGON-2 Programme. The calibration procedure based on LST seems to outperform the calibration based on discharge, with lower relative error and higher Nash-Sutcliffe efficiency index on cumulated volume.Editor D. Koutsoyiannis; Associate editor C. Perrin

Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature

Land surface temperature (LST) and its diurnal variation are important when evaluating climate change, the land–atmosphere energy budget, and the global hydrological cycle. However, the available satellite LST products have either a coarse spatial resolution or a low temporal resolution, which constrains their potential applications. This paper proposes a spatio-temporal integrated temperature fusion model (STITFM) for the retrieval of high temporal and spatial resolution LST from multi-scale polar-orbiting and geostationary satellite observations. Compared with the traditional fusion methods for LST with two different sensors, the proposed method is able to fuse the LST from arbitrary sensors in a unified framework. The model was tested using LST with fine, moderate, and coarse-resolutions. Data from the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS), the Geostationary Operational Environmental Satellite (GOES) Imager, and the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) were used. The fused LST values were evaluated with in situ LST obtained from the Surface Radiation Budget Network (SURFRAD) and the Land Surface Analysis Satellite Application Facility (LSA SAF) project. The final validation results indicate that the STITFM is accurate to within about 2.5K.

Errata: Soil moisture derived using two apparent thermal inertia functions over Canterbury, New Zealand

The near-surface soil moisture (SM) is an important property of the soil that can be studied from satellite remote sensing observations over a large spatial domain. This research provides an estimate on the accuracy of SM retrieved from satellite land surface temperature (LST) observations over the Canterbury Plains, New Zealand. The apparent thermal inertia (ATI) method with two approaches (ATI1 and ATI2) was applied to derive the near-surface SM from the moderate resolution imaging spectroradiometer (MODIS) LST product. The in-situ measurements of SM and rainfall data at six sites across the study area were used as reference. The analysis was conducted over two periods, a short period of four months and a longer period of three years. SM simulations by the weather research and forecasting (WRF) model were used in the analysis for the shorter period. Overall, SM based on ATI2 showed a slightly higher correlation with the in-situ measurements (ρ¯=0.66) than ATI1 (ρ¯=0.63). The correlation, in general, was higher for the WRF simulations (ρ¯=0.81). Both functions performed better during summer compared to winter, but overall, ATI2 showed lower mean errors (ME≈−15 m3·m−3 volumetric SM) compared to ATI1 (ME≈−20 m3·m−3) at most of the sites. Additionally, seasonal variations of SM were better detected by ATI2 than ATI1, and the effects of precipitation were detected on more occasions by the ATI2 function. We conclude that ATI2 function can be used to estimate the near-surface SM over a large area from the MODIS LST time series if a few representative reference stations are available.

Soil moisture derived using two apparent thermal inertia functions over Canterbury, New Zealand

The near-surface soil moisture (SM) is an important property of the soil that can be studied from satellite remote sensing observations over a large spatial domain. This research provides an estimate on the accuracy of SM retrieved from satellite land surface temperature (LST) observations over the Canterbury Plains, New Zealand. The apparent thermal inertia (ATI) method with two approaches (ATI1 and ATI2) was applied to derive the near-surface SM from the moderate resolution imaging spectroradiometer (MODIS) LST product. The in-situ measurements of SM and rainfall data at six sites across the study area were used as reference. The analysis was conducted over two periods, a short period of four months and a longer period of three years. SM simulations by the weather research and forecasting (WRF) model were used in the analysis for the shorter period. Overall, SM based on ATI2 showed a slightly higher correlation with the in-situ measurements (ρ¯=0.66) than ATI1 (ρ¯=0.63). The correlation, in general, was higher for the WRF simulations (ρ¯=0.81). Both functions performed better during summer compared to winter, but overall, ATI2 showed lower mean errors (ME≈−15 m3·m−3 volumetric SM) compared to ATI1 (ME≈−20 m3·m−3) at most of the sites. Additionally, seasonal variations of SM were better detected by ATI2 than ATI1, and the effects of precipitation were detected on more occasions by the ATI2 function. We conclude that ATI2 function can be used to estimate the near-surface SM over a large area from the MODIS LST time series if a few representative reference stations are available.

수도권 AWS 기온을 이용한 MODIS, Landsat 위성의 지표면 온도 분석

서울을 포함한 수도권의 지표면 온도를 분석하기 위하여 Landsat과 MODIS의 지표면 온도, AWS의 기온, 지표면 고도 및 토지이용도를 이용하였다. Landsat과 MODIS 위성의 지표면 온도와 AWS 기온의 분석은 상관계수, 평방근 오차(Root Mean Squared Error, RMSE), 선형회귀분석 등의 통계분석방법을 적용하였다. Landsat과 MODIS 지표면 온도의 상관계수는 0.32이고 RMSE는 4.61 K였다. 그리고 Landsat과 MODIS 지표면 온도와 AWS 기온의 상관성은 각각 0.83과 0.96이며 RMSE는 3.28 K, 2.25 K이었다. Landsat과 MODIS 지표면 온도는 비교적 높은 상관성을 보였으나 각각의 선형회귀의 기울기는 0.45와 1.02이었다. Landsat 5의 경우 전체 관측소에 대하여 0.5이하의 낮은 상관성을 보였고 Landsat 8의 경우는 일치되는 지점이 다른 위성에 비하여 적었으나 0.5이상의 상관성을 나타냈다. Landsat 7은 대부분 0.8이상의 높은 상관성을 보였고 대체적으로 서울중심부에서 높은 상관성이 나타났다. 위성의 지표면 온도와 지표유형에 따른 AWS 기온사이의 상관성은 0.8이상의 높은 상관성을 보였다. Landsat 위성의 지표면 온도의 상관성은 0.84이었고 RMSE는 3.1 K이상이었으며 MODIS 위성의 상관계수는 0.96이상이고 RMSE는 2.6 K이하였다. 결과적으로 두 위성의 지표온도의 차이는 관측시각 차이에 의한 것으로 위성의 해상도에 따라 복사량을 탐지하는 지표면의 면적 차이에 의하여 발생되는 것으로 사료된다. In order to analyze the Land Surface Temperature (LST) in metropolitan area including Seoul, Landsat and MODIS land surface temperature, Automatic Weather Station (AWS) temperature, digital elevation model and landuse are used. Analysis method among the Landsat and MODIS LST and AWS temperature is basic statistics using by correlation coefficient, root-mean-square error and linear regression etc. Statistics of Landsat and MODIS LST are a correlation coefficient of 0.32 and Root Mean Squared Error (RMSE) of 4.61 K, respectively. And statistics of Landsat and MODIS LST and AWS temperature have the correlations of 0.83 and 0.96 and the RMSE of 3.28 K and 2.25 K, respectively. Landsat and MODIS LST have relatively high correlation with AWS temperature, and the slope of the linear regression function have 0.45 (Landsat) and 1.02 (MODIS), respectively. Especially, Landsat 5 has lower correlation about 0.5 or less in entire station, but Landsat 8 have a higher correlation of 0.5 or more despite of lower match point than other satellites. Landsat 7 have highly correlation of more than 0.8 in the center of Seoul. Correlation between satellite LSTs and AWS temperature with landuse (urban and rural) have 0.8 or higher. Landsat LST have correlation of 0.84 and RMSE of more than 3.1 K, while MODIS LST have correlation of more than 0.96 and RMSE of 2.6 K. Consequently, the difference between the LSTs by two satellites have due to the difference in the optical observation and detection the radiation generated by the difference in the area resolution.

Validation of remotely sensed surface temperature over an oak woodland landscape — The problem of viewing and illumination geometries

Satellite retrieved values of Land Surface Temperature (LST) over structured heterogeneous pixels generally depend on viewing and illumination angles as well as on the characteristics of the land cover. Here we present a method to quantify such dependencies on land surface characteristics, sun illumination and satellite position. The method uses a geometric model to describe the surface elements viewed by an air-borne sensor and relies on parallel-ray geometry to calculate the projections of tree canopies and sunlit and shaded ground: these are considered as basic surface elements responsible for most of the spatial variability of LST. For a woodland landscape we demonstrate that modeling the fractions of these basic surface elements within the sensor field-of-view allows us to quantify the directional effects observed on satellite LST with sufficient accuracy.Geometric models are an effective tool to upscale in situ measurements for the validation of LST over discontinuous canopies (e.g. forests). Here we present the application of a model to observations of brightness temperature from the LSA-SAF validation site in Évora (Portugal), an area of oak woodland, over the one-year period from October 2011 to September 2012. The resulting composite temperature is compared against LSA SAF LST products from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat as well as against MYD11A1/MOD11A1 (collection 5) products from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard AQUA and TERRA. Comparisons with modeled ground LST show that SEVIRI LST has a bias of 0.26°C and a RMSE of 1.34°C, whereas MODIS LST (MYD11A1 and MOD11A1, collection 5) has a bias of −1.54°C and a RMSE of 2.37°C. Both MODIS and SEVIRI LST are closer to in situ values obtained with the geometric model than to those obtained when disregarding the effects of viewing and illumination geometry. These results demonstrate the need to consider the directional character of LST products, especially for validation purposes over heterogeneous land covers. For the new MODIS LST product (MOD21), which is based on the Temperature-Emissivity Separation (TES) algorithm, comparisons with in-situ LST show an improved bias of −0.81°C and a RMSE of 1.48°C (daytime values only). The TES based product presents lower emissivity values than those used for retrieving MYD11A1/MOD11A1 LST, which may partially explain the improved match with in-situ LST.Discrepancies between LST retrievals obtained from different sensors, especially those on different orbits can also be partly explained by their viewing/illumination geometries. In this study the geometric model is used to correct LST deviations between simultaneous MODIS and SEVIRI LST estimations related to those effects. When the model is used to correct the variable MODIS viewing geometry there is a reduction in standard deviation of about 0.5°C.

How can we use MODIS land surface temperature to validate long-term urban model simulations?

High spatial resolution urban climate modeling is essential for understanding urban climatology and predicting the human health impacts under climate change. Satellite thermal remote-sensing data are potential observational sources for urban climate model validation with comparable spatial scales, temporal consistency, broad coverage, and long-term archives. However, sensor view angle, cloud distribution, and cloud-contaminated pixels can confound comparisons between satellite land surface temperature (LST) and modeled surface radiometric temperature. The impacts of sensor view angles on urban LST values are investigated and addressed. Three methods to minimize the confounding factors of clouds are proposed and evaluated using 10 years of Moderate Resolution Imaging Spectroradiometer (MODIS) data and simulations from the High-Resolution Land Data Assimilation System (HRLDAS) over Greater Houston, Texas, U.S. For the satellite cloud mask (SCM) method, prior to comparison, the cloud mask for each MODIS scene is applied to its concurrent HRLDAS simulation. For the max/min temperature (MMT) method, the 50 warmest days and coolest nights for each data set are selected and compared to avoid cloud impacts. For the high clear-sky fraction (HCF) method, only those MODIS scenes that have a high percentage of clear-sky pixels are compared. The SCM method is recommended for validation of long-term simulations because it provides the largest sample size as well as temporal consistency with the simulations. The MMT method is best for comparison at the extremes. And the HCF method gives the best absolute temperature comparison due to the spatial and temporal consistency between simulations and observations.

Calibration and Validation of a Distributed Energy–Water Balance Model Using Satellite Data of Land Surface Temperature and Ground Discharge Measurements

Abstract Distributed hydrological models of energy and mass balance need as inputs many soil and vegetation parameters, which are usually difficult to define. This paper will try to approach this problem by performing a pixel to pixel calibration procedure of soil hydraulic and vegetation parameters based on satellite land surface temperature data as a complementary method to the traditional calibration with ground discharge measurements at river control cross sections. These analyses are performed for the upper Po River basin (Italy) closed at the river cross section of Ponte della Becca with a total catchment area of about 38 000 km2, for a calibration period from 2000 to 2003, and a validation period from 2004 to 2010. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data and a distributed hydrological model, Flash-Flood Event-Based Spatially Distributed Rainfall-Runoff Transformation Energy Water Balance model (FEST-EWB), that solves the system of energy and mass balance equations as a function of the representative equilibrium temperature will be used. This equilibrium surface temperature is comparable to the land surface temperature as retrieved from operational remote sensing data. Results suggest that a combined calibration based on satellite land surface temperature and ground discharge is needed to correctly reproduce volume discharge and also spatially distributed maps of representative equilibrium temperature and evapotranspiration. Improvements of about 10 mm/8 days are obtained on evapotranspiration from the model calibrated with Q and land surface temperature (LST) respect to the calibration based only on discharge.

Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion

Land-surface temperature (LST) is of great significance for the estimation of radiation and energy budgets associated with land-surface processes. However, the available satellite LST products have either low spatial resolution or low temporal resolution, which constrains their potential applications. This paper proposes a spatiotemporal fusion method for retrieving LST at high spatial and temporal resolutions. One important characteristic of the proposed method is the consideration of the sensor observation differences between different land-cover types. The other main contribution is that the spatial correlations between different pixels are effectively considered by the use of a variation-based model. The method was tested and assessed quantitatively using the different sensors of Landsat TM/ETM+, moderate resolution imaging spectroradiometer and the geostationary operational environmental satellite imager. The validation results indicate that the proposed multisensor fusion method is accurate to about 2.5 K.

A Comparison of Two Solar Radiation Models Using Artificial Neural Networks and Remote Sensing in Turkey

This study introduces artificial neural networks for the estimation of solar radiation using model 1 (latitude, longitude, altitude, month, and meteorological land surface temperature) and model 2 (latitude, longitude, altitude, month, and satellite land surface temperature) data in Turkey. Price method was used for the estimation of land surface temperature values. Scale conjugate gradiant learning algorithms and logistic sigmoid transfer function were used in the network. R 2 with model 1 and model 2 values have been found to be 96.93 and 97.24% (training stations), 80.41 and 82.37% (testing stations), respectively. These results are sufficient to predict the solar radiation.

Multi-pixel Calibration of a Distributed Energy Water Balance Model Using Satellite Data of Land Surface Temperature and Eddy Covariance Data

Distributed hydrological models of energy and mass balance usually need in input many soil and vegetation parameters, which are usually difficult to define. This paper will try to approach this problem performing a parameters calibration based on satellite land surface temperature data (LST) as a complementary method to the traditional calibration with ground data.A pixel to pixel calibration procedure of soil hydraulic and vegetation parameters for each pixel of the domain is proposed according to the comparison between observed and simulated land surface temperature.A distributed hydrological model, FEST-EWB, that solves the system of energy and mass balance equations as a function of the representative equilibrium temperature (RET) will be used. RET is comparable to the land surface temperature as retrieved from operational remote sensing data. LST is a critical model state variable and remote sensing LST can be effectively used, in combination with energy and mass balance modeling, to monitor latent and sensible heat fluxes. The analyses are performed over the Consorzio Muzza basin for an area that covers 74,000ha in the middle of the Po Valley, near Lodi city.The reliability of the hydrological model estimates will be evaluated against measurements of latent heat flux and soil moisture acquired by an eddy-covariance station.Moreover, distributed evapotranspiration estimates will be compared with the results obtained from a simplified version of FEST-WB model with computes crop evapotranspiration with Hargreaves equation and crop coefficient values.

Comparing night-time satellite land surface temperature from MODIS and ground measured air temperature across a conurbation

The relationship between remotely sensed land surface temperature (LST) data and ground-measured air temperature is important for a number of applications. This article details a pilot project over the summer (June, July, August) 2010 using Moderate Resolution Imaging Spectroradiometer (MODIS) LST data and air temperature data from a custom network of data loggers across the conurbation of Birmingham, UK. The results show that at night-time air temperature is consistently higher than the satellite-measured LST, but significant station-specific variability exists.

한반도에서의 천리안 위성 지표면 온도 유용성 평가

지표면 온도는 수문기상학에서 매우 중요한 인자로써 지면-대기의 상호순환을 이해하는 데 필수적인 요소이다. 따라서 지표면 온도를 정확하게 산출하기 위한 연구가 이루어지고 있다. 그러나 국내에서는 현재까지 시 공간적으로 일정한 자료를 대상으로 한 연구가 미흡한 상황이다. 본 연구에서는 국내 최초의 정지궤도 위성인 Communication, Ocean and Meteorological Satellite (COMS)의 지표면 온도 자료를 사용하여 MODerate-resolution Imaging Spectroradiometer (MODIS) 센서를 탑재한 위성(Terra/Aqua)에서 제공되는 지표면 온도와 비교 분석을 실시하였다. 또한, COMS 자료의 정확성 확인을 위하여 지상관측지점의 자료와 비교하였다. 그 결과 MODIS와의 비교에서는 COMS가 Aqua/Terra에 비하여 전체적으로 과소 산정되었으며, Aqua보다는 Terra와 비슷한 경향을 가지고 있는 것을 확인할 수 있었다. 지상 관측 일평균자료와의 비교에서도 COMS가 과소 산정되는 것으로 나타났다. 각각의 오차는 알고리즘 사용 인자의 차이, 자료의 시간적 차이, 외부적인 요인 등으로 인해 발생하는 것으로 판단된다. 그러나 전체적인 경향성이 비슷한 것으로 나타나 COMS의 활용 가능성을 확인할 수 있었으며, 추후보다 정확한 검 보정에 대한 연구가 필요한 실정이다. The Land Surface Temperature (LST) is one of the significant factors to understand the water and energy cycles between the land surface and atmosphere. However, few previous studies for spatio-temporal variations of LST has been investigated. In this study, we conducted comparative analyses between the Communication, Ocean and Meteorological Satellite (COMS) and MOderate-Resolution Imaging Spectroradiometer (MODIS) LST data. We compared COMS data with observations to identify the accuracy and found relative underestimated patterns of the COMS data as compared to observations. We also found that COMS LST were underestimated in compare to MODIS LST. The Terra LST was verified to have more similar trends with the COMS LST rather than Aqua LST. While we identified the applicability of COMS based on the results of similar tendencies of two comparisons, more intensive validation research at a variety of field conditions should be conducted to gurantee current COMS LST.

Validation of GOES-R Satellite Land Surface Temperature Algorithm Using SURFRAD Ground Measurements and Statistical Estimates of Error Properties

Validation of satellite land surface temperature (LST) is a challenge because of spectral, spatial, and temporal variabilities of land surface emissivity. Highly accurate in situ LST measurements are required for validating satellite LST products but are very hard to obtain, except at discrete points or for very short time periods (e.g., during field campaigns). To compare these field-measured point data with moderate-resolution (1 km) satellite products requires a scaling process that can introduce errors that ultimately exceed those in the satellite-derived LST products whose validation is sought. This paper presents a new method of validating the Geostationary Operational Environmental Satellite (GOES) R-Series (GOES-R) Advanced Baseline Imager (ABI) LST algorithm. It considers the error structures of both ground and satellite data sets. The method applies a linear fitting model to the satellite data and coregistered “match-up” ground data for estimating the precisions of both data sets. In this paper, GOES-8 Imager data were used as a proxy of the GOES-R ABI data for the satellite LST derivation. The in situ data set was obtained from the National Oceanic and Atmospheric Administration's SURFace RADiation (SURFRAD) budget network using a stringent match-up process. The data cover one year of GOES-8 Imager observations over six SURFRAD sites. For each site, more than 1000 cloud-free match-up data pairs were obtained for day and night to ensure statistical significance. The average precision over all six sites was found to be 1.58 K, as compared to the GOES-R LST required precision of 2.3 K. The least precise comparison at an individual SURFRAD site was 1.8 K. The conclusion is that, for these ground truth sites, the GOES-R LST algorithm meets the specifications and that an upper boundary on the precision of the satellite LSTs can be determined.

Estimating basin scale evapotranspiration (ET) by water balance and remote sensing methods

AbstractEvapotranspiration (ET) is an important hydrological process that can be studied and estimated at multiple spatial scales ranging from a leaf to a river basin. We present a review of methods in estimating basin scale ET and its applications in understanding basin water balance dynamics. The review focuses on two aspects of ET: (i) how the basin scale water balance approach is used to estimate ET; and (ii) how ‘direct’ measurement and modelling approaches are used to estimate basin scale ET. Obviously, the basin water balance‐based ET requires the availability of good precipitation and discharge data to calculate ET as a residual on longer time scales (annual) where net storage changes are assumed to be negligible. ET estimated from such a basin water balance principle is generally used for validating the performance of ET models. On the other hand, many of the direct estimation methods involve the use of remotely sensed data to estimate spatially explicit ET and use basin‐wide averaging to estimate basin scale ET. The direct methods can be grouped into soil moisture balance modelling, satellite‐based vegetation index methods, and methods based on satellite land surface temperature measurements that convert potential ET into actual ET using a proportionality relationship. The review also includes the use of complementary ET estimation principles for large area applications. The review identifies the need to compare and evaluate the different ET approaches using standard data sets in basins covering different hydro‐climatic regions of the world. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Spatial and temporal variations of summer surface temperatures of wet polygonal tundra in Siberia - implications for MODIS LST based permafrost monitoring

The surface temperature of permafrost soils in remote arctic areas is accessible by satellite land surface temperature (LST) detection. However, the spatial resolution of satellite measurements such as the MODIS LST products is limited and does not detect the heterogeneities of the wet polygonal tundra landscape where surface wetness varies over distances of several meters. This paper examines the spatial and temporal variability of summer surface temperatures of a polygonal tundra site in northern Siberia using a ground based high resolution thermal imaging system. Thermal infrared images were taken of a 1000 m 2 polygonal tundra area in 10 min intervals from July to September 2008. Under clear sky conditions, the individual measurements indicate temperature differences of up to 6 K between dry and wet tundra surfaces and which can exceed 12 K when dry tundra and water surfaces are compared. These differences disappear when temperature averages are considered for intervals longer than the diurnal cycle; for weekly averages the spatial temperature variability decreases below 1 K. The exception is the free water surface of a shallow polygonal pond where weekly averaged temperature differences of 2.5 K are sustained compared to the tundra surface. The ground based thermal infrared images are upscaled to MODIS sized pixels and compared to available MODIS LST data for individual measurements and weekly averages. The comparisons show generally good agreement for the individual measurements under clear sky conditions, which exist during 20% of the studied time period. However, several erroneous measurements and large data gaps occur in the MODIS LST data during cloudy conditions, leading to biased weekly temperature averages inferred from the satellite observations. Based on these results the following recommendations are given for future permafrost temperature monitoring based on MODIS LST products: (i) high resolution surface water masks for the quality assessment in landscapes where lakes and ponds are frequent and (ii) reliable cloud cover detection in conjunction with a gap filling procedure for accurate temporal averages.

Comparison of satellite, thermochron and air temperatures at Summit, Greenland, during the winter of 2008/09

AbstractCurrent trends show a rise in Arctic surface and air temperatures, including over the Greenland ice sheet where rising temperatures will contribute to increased sea-level rise through increased melt. We aim to establish the uncertainties in using satellite-derived surface temperature for measuring Arctic surface temperature, as satellite data are increasingly being used to assess temperature trends. To accomplish this, satellite-derived surface temperature, or land-surface temperature (LST), must be validated and limitations of the satellite data must be assessed quantitatively. During the 2008/09 boreal winter at Summit, Greenland, we employed data from standard US National Oceanic and Atmospheric Administration (NOAA) air-temperature instruments, button-sized temperature sensors called thermochrons and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument to (1) assess the accuracy and utility of thermochrons in an ice-sheet environment and (2) compare MODIS-derived LSTs with thermochron-derived surface and air temperatures. The thermochron-derived air temperatures were very accurate, within 0.1 ± 0.3°C of the NOAA-derived air temperature, but thermochron-derived surface temperatures were ∼3°C higher than MODIS-derived LSTs. Though surface temperature is largely determined by air temperature, these variables can differ significantly. Furthermore, we show that the winter-time mean air temperature, adjusted to surface temperature, was ∼11°C higher than the winter-time mean MODIS-derived LST. This marked difference occurs largely because satellite-derived LSTs cannot be measured through cloud cover, so caution must be exercised in using time series of satellite LST data to study seasonal temperature trends.