Moderate Resolution Imaging Spectroradiometer Land Surface Temperature Products Research Articles

Estimation of Daily Air Temperature Based on MODIS Land Surface Temperature Products over the Corn Belt in the US

Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature), respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May–September) from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19–1.85, 0.37–1.12 and 0.26–0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc.

On the Downscaling of Actual Evapotranspiration Maps Based on Combination of MODIS and Landsat-Based Actual Evapotranspiration Estimates

Downscaling is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for downscaling MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and downscaling. The regression with intercept method does have limitations in downscaling if the slope and intercept are computed over a large area. A good agreement was obtained between downscaled monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from −16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and downscaled monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between downscaled monthly AET using LinZI method and the gridded FLUXNET dataset. The downscaled monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed downscaling method will be very useful in advancing the application of remotely sensed images in water resources planning and management.

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.

Long-term high-frequency validation of MODIS LST products in a Mediterranean mountain environment

The accuracy of Moderate-resolution Imaging Spectroradiometer (MODIS) level 3 1 km land surface temperature (LST) products was assessed through long-term validation carried out in a mountainous site in Sierra Nevada, southeast Spain. A total of 1458 day and night thermal images, acquired by Terra and Aqua satellites during 2008, were processed and compared to ground-truth data recorded at the meteorological station of Robledal de Cañar with a frequency of one measurement every 10 min. The purpose of this investigation was to understand whether MODIS LST data can be used as input for climate models to be constructed for mountainous environments. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and the overestimation of night-time values. Although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation coefficients with ground measurements, only night values maintained a relatively high accuracy of approximately 2°C of annual average error. Factors that may cause errors in the MODIS LST data, like acquisition angle, cloud, and snow cover, were analysed without conclusive results. High accuracy levels, i.e. close to 1°C, similar to other validation studies carried out over simpler and much more homogenous land-cover types such as cultivated fields, have been achieved for night images acquired during the summer months, thus making these datasets reliable for their use in climatic models over mountainous regions.

Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products

Accurate estimation of spatially distributed air temperature (Ta) is useful for a wide range of disciplines such as hydrology, meteorology and ecology. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (Ts) products hold the potential to get spatial estimates of land surface temperature at high temporal (daily) and spatial resolution (1km) across the world. In this paper, the possibility of retrieving high-resolution Ta data from MODIS Ts data on both Terra and Aqua satellites was investigated over Xiangride River basin in the north Tibetan Plateau. The results of statistical analysis show that during the night MODIS Ts data on Terra satellite provides a good estimation of daily minimum air temperature with RMSE=2.97°C, MAE=2.37°C, and r=0.94, while during the day the difference between Terra MODIS Ts data and daily maximum air temperature is large with RMSE=7.45°C, MAE=6.21°C, and r=0.83. In order to improve the accuracy of the estimation of daily maximum air temperature, the temperature-vegetation index (TVX) method was used in this paper. By lowering the threshold for the negative correlation coefficient between NDVI (Normalized Difference Vegetation Index) and Ts to −0.1, the TVX method was applied to MODIS land products to estimate daily maximum air temperature successfully for the first time. The validation results show that the TVX method has improved the accuracy of daily maximum air temperature significantly with RMSE=3.79°C, MAE=3.03°C, and r=0.83. Finally, the diurnal air temperature variation was simulated using a simple sine curve model for the period 21:00–9:00UCT. The simulation results were in good agreement with observed values with RMSE=3.43°C, MAE=2.54°C, and r=0.86. The results of this study show that daily maximum and minimum air temperature can be retrieved effectively from MODIS land surface products through the use of TVX method.

Modelling light use efficiency using vegetation index and land surface temperature from MODIS in Harvard Forest

Light use efficiency (LUE) is of great importance for carbon cycle and climate change research. This study presents a new LUE model incorporation of vegetation indices (VIs) and land surface temperature (LST) derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) in Harvard Forest. Three indices, including the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2) and the soil-adjusted vegetation index (SAVI), were selected as indicators of forest canopy greenness. A single VI provided moderate estimates of LUE with coefficients of determination (R 2) 0.6219, 0.7094 and 0.7502 for NDVI, EVI2 and SAVI, respectively. Our results demonstrated that canopy LUE was related both to the canopy photosynthesis efficiency and air temperature (R 2 = 0.5634). Therefore, the MODIS LST product was incorporated as a surrogate for monitoring of environmental stresses as the observed relationship between LST and both air temperature (R 2 = 0.8828) and vapour pressure deficit (VPD) (R 2 = 0.6887). The new model in terms of (VI) × (Scaled (LST)) provided improved estimates of LUE estimation with R 2 of 0.7349, 0.7561 and 0.7879 for NDVI, EVI2 and SAVI, respectively. The results will be useful for the development of future LUE models based entirely on remote-sensing observations.

Comparison of Bayesian land surface temperature algorithm performance with Terra MODIS observations

An approach to land surface temperature (LST) estimation that relies upon Bayesian inference has been tested against multiband infrared radiometric imagery from the Terra MODIS (Moderate Resolution Imaging Spectroradiometer) instrument. The algorithm employed requires minimal knowledge of surface emissivity, starting from a parsimoniously chosen (hence, uninformative) range of prior band emissivity knowledge. Two estimation methods have been tested. The first is the iterative contraction mapping of joint expectation values for LST and surface emissivity described in a previous paper. In the second method, the Bayesian algorithm is reformulated as a maximum a posteriori (MAP) search for the maximum joint a posteriori probability for LST, given observed sensor aperture radiances and a priori probabilities for LST and emissivity. Two MODIS data granules each for daytime and night-time were used for the comparison. The granules were chosen to be largely cloud-free, with limited vertical relief in those portions of the granules for which the sensor zenith angle . Level 1b radiances were used to obtain 500 LST estimates per granule for comparison with the Level 2 MODIS LST product. The Bayesian LST estimators accurately reproduce standard MODIS product LST values. In particular, the mean discrepancy for the MAP retrievals is , and its standard deviation does not exceed 1K. The ±68% confidence intervals for individual LST estimates associated with assumed uncertainty in surface emissivity are of order 0.8K. The appendix presents a proof of convergence of the iterative contraction mapping algorithm. The expectation values of surface temperature in multiple bands, and jointly in all bands, converge to a fixed point, within a stipulated convergence criterion. Provided the support for the calculation of the expectation value brackets the maximum in the joint posterior probability for LST, the fixed point converges to the MAP LST estimate in the limit .

Improving Predictions of Water and Heat Fluxes by Assimilating MODIS Land Surface Temperature Products into the Common Land Model

AbstractFour data assimilation scheme combinations derived from two strategies and two optimization algorithms [the ensemble Kalman filter (EnKF) and the shuffled complex evolution method developed at The University of Arizona (SCE-UA)] are developed based on the Common Land Model (CLM) to improve predictions of water and heat fluxes. The first strategy is constructed through adjusting the soil temperature, while the second strategy adjusts the soil moisture. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products are compared with ground-measured surface temperature, and assimilated into the CLM. The relationship equation between the MODIS LST products and CLM surface temperature is taken as the observation operator and the root-mean-square error (RMSE) is applied as the observation error. The assimilation results are validated by measurements from six observation sites located in Germany, the United States, and China. Results indicate that the developed data assimilation schemes can improve estimates of water and heat fluxes. Overall, strategy 2 is superior to strategy 1 when using the same optimization algorithm. The EnKF algorithm performs slightly better than the SCE-UA algorithm when using the same strategy. Strategy 2 combined with the EnKF algorithm performs best for water and heat fluxes, and the reductions in the RMSE are found to be 24.0 and 15.2 W m−2 for sensible and latent heat fluxes, respectively. The joint assimilation of the MODIS LST and soil moisture observations can produce better results for strategy 2 with the SCE-UA. Since preprocessing model parameters are used in this study, the uncertainties in the model parameters may have resulted in suboptimal assimilation results. Therefore, model calibrations should be conducted in the future.

Remote sensing and census based assessment and scope for improvement of rice and wheat water productivity in the Indo-Gangetic Basin

Understanding of crop water productivity (WP) over large scale, e.g., river basin, has significant implications for sustainable basin development planning. This paper presents a simplified approach to combine remote sensing, census and weather data to analyze basin rice and wheat WP in Indo-Gangetic River Basin, South Asia. A crop dominance map is synthesized from ground truth data and three existing LULC maps. National statistics on crop area and production information are collected and the yield is interpolated to pixel level using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI). Crop evapotranspiration is mapped using simplified surface energy balance (SSEB) model with MODIS land surface temperature products and meteorological data collected from 56 weather stations. The average ET by rice and wheat is 368 mm and 210 mm respectively, accounting for only 69% and 65% of potential ET, and 67% and 338% of rainfall of the crop growth period measured from Tropical Rainfall Measurement Mission (TRMM). Average WP for rice and wheat is 0.84 and 1.36 kg/m3 respectively. WP variability generally follows the same trend as shown by crop yield disregarding climate and topography changes. Sum of rice-wheat water productivity, however, exhibits different variability leading to better understanding of irrigation water management as wheat heavily relies on irrigation. Causes for variations and scope for improvement are also analyzed.

Evaluation of surface and near‐surface melt characteristics on the Greenland ice sheet using MODIS and QuikSCAT data

The Greenland ice sheet has been the focus of much attention recently because of increasing melt in response to regional climate warming and can be studied using Moderate Resolution Imaging Spectroradiometer (MODIS) and Quick Scatterometer (QuikSCAT) data. To improve our ability to measure surface melt, we use remote sensing data products to study surface and near‐surface melt characteristics of the Greenland ice sheet for the 2007 melt season when record melt extent and runoff occurred. MODIS daily land surface temperature (LST), MODIS daily snow albedo, and a special diurnal melt product derived from QuikSCAT (QS) scatterometer data, are all effective in measuring the evolution of melt on the ice sheet. These daily products, produced from different parts of the electromagnetic spectrum, are sensitive to different geophysical features, though QS‐ and MODIS‐derived melt generally show excellent correspondence when surface melt is present. Values derived from the MODIS daily snow albedo product drop in response to melt and change with apparent grain size changes. For the 2007 melt season, the MODIS LST and QS products detect 766,184 km2 ± 8% and 862,769 km2 ± 3% of melt, respectively. The QS product detects about 11% greater melt extent than is detected by the MODIS LST product probably because QS is more sensitive to surface melt and can also see subsurface melt. The consistency of the response of the different products demonstrates unequivocally that physically meaningful melt/freeze boundaries are detected. We have demonstrated that when these products are used together we can improve the precision in mapping surface and near‐surface melt extent on the Greenland ice sheet.

Temperature‐based and radiance‐based validations of the V5 MODIS land surface temperature product

The V5 level 2 land surface temperature (LST) product of the Moderate Resolution Imaging Spectroradiometer (MODIS) was validated over homogeneous rice fields in Valencia, Spain, and the Hainich forest in Germany. For the Valencia site, ground LST measurements were compared with the MOD11_L2 product in the conventional temperature‐based (T‐based) method. We also applied the alternative radiance‐based (R‐based) method, with in situ LSTs calculated from brightness temperatures in band 31 through radiative transfer simulations using temperature and water vapor profiles and surface emissivity data. At the Valencia site, profiles were obtained from local radiosonde measurements and from National Centers for Environmental Prediction (NCEP) data. The R‐based method was applied at the Hainich site using radiosonde profiles from a nearby sounding station and NCEP profiles. The T‐based validation showed average bias (MODIS minus ground) of −0.3 K, standard deviation of 0.6 K and root mean square error (RMSE) of ±0.7 K. For the R‐based method, the quality of the atmospheric profiles was assessed through the difference δ(T31–T32) between the actual MODIS and the profile‐based calculated brightness temperature difference in bands 31 and 32. For the cases where −0.3 K < δ(T31–T32) < 0.5 K, the R‐based method yielded LST errors with small biases and RMSE = ±0.6 K for the two sites. These results show the high accuracy and precision of the MODIS LST product for the two sites studied. The good performance of the R‐based method opens the possibility for a more complete validation including heterogeneous surfaces where the T‐based method is not feasible.

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.

Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval

Data from three thermal sensors with different spatial resolution were assessed for urban surface temperature retrieval over the Yokohama City, Japan. The sensors are Thermal Airborne Broadband Imager (TABI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODerate resolution Imaging Spectroradiometer (MODIS). Two algorithms were developed for land surface temperature (LST) retrieval from TABI image and ASTER thermal infrared (TIR) channels 13 and 14. In addition, ASTER LST and MODIS LST products were also collected. All the LST images were assessed by analyzing the relationship between LST and normalized difference vegetation index (NDVI) and by spatial distributions of LST profiles, derived from typical transects over the LST images. In this study, a strong negative relationship between LST and NDVI has been demonstrated although the degree of correlation between NDVI and LST varies slightly among the different LST images. Cross-validation among the LST images retrieved from the three thermal sensors of different spatial resolutions indicates that the LST images retrieved from the 2 channel ASTER data and a single band TABI thermal image using our developed algorithms are reliable. The LST images retrieved from the three sensors should have different potential to urban environmental studies. The MODIS thermal sensor can be used for the synoptic overview of an urban area and for studying urban thermal environment. The ASTER, with its TIR subsystem of 90-m resolution, allows for a more accurate determination of thermal patterns and properties of urban land use/land cover types, and hence, a more accurate determination of the LST. In consideration of the high heterogeneity of urban environment, the TABI thermal image, with a high spatial resolution of 2 m, can be used for rendering and assessing complex urban thermal patterns and detailed distribution of LST at the individual house level more accurately.