Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites

Land surface temperature (LST) and emissivity are key parameters in estimating the land surface radiation budget, a major controlling factor of global climate and environmental change. In this study, Terra Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 LST and emissivity products are evaluated using long-term ground-based longwave radiation observations collected at six Surface Radiation Budget Network (SURFRAD) sites from 2000 to 2007. LSTs at a spatial resolution of 90 m from 197 ASTER images during 2000-2007 are directly compared to ground observations at the six SURFRAD sites. For nighttime data, ASTER LST has an average bias of 0.1 °C and the average bias is 0.3 °C during daytime. Aqua MODIS LST at 1 km resolution during nighttime retrieved from a split-window algorithm is evaluated from 2002 to 2007. MODIS LST has an average bias of − 0.2 °C. LST heterogeneity (defined as the Standard Deviation, STD, of ASTER LSTs in 1 × 1 km² region, 11 × 11 pixel in total) and instrument calibration error of pyrgeometer are key factors impacting the ASTER and MODIS LST evaluation using ground-based radiation measurements. The heterogeneity of nighttime ASTER LST is 1.2 °C, which accounts for 71% of the STD of the comparison, while the heterogeneity of the daytime LST is 2.4 °C, which accounts for 60% of the STD. Collection 5 broadband emissivity is 0.01 larger than that of MODIS Collection 4 products and ASTER emissivity. It is essential to filter out the abnormal low values of ASTER daily emissivity data in summer time before its application.     

Evaluation of MODIS land surface temperature with in-situ snow surface temperature from CREST-SAFE

This article presents the procedure and results of a temperature-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) product provided by the National Aeronautics and Space Administration Terra and Aqua Earth Observing System satellites using in-situ LST observations recorded at the Cooperative Remote Sensing Science and Technology Center – Snow Analysis and Field Experiment (CREST-SAFE) during the years of 2013 (January–April) and 2014 (February–April). A total of 314 day-and-night clear-sky thermal images, acquired by the Terra and Aqua satellites, were processed and compared to ground-truth data from CREST-SAFE with a frequency of one measurement every 3 min. CREST-SAFE is a synoptic ground station, located in the cold county of Caribou in Maine, USA, with a distinct advantage over most meteorological stations because it provides automated and continuous LST observations via an Apogee Model SI-111 Infrared Radiometer. This article also attempts to answer the question of whether a single pixel (1 km²) or several spatially averaged pixels should be used for satellite LST validation by increasing the MODIS window size to 5 × 5, 9 × 9, and 25 × 25 windows.

Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation with ground measurements, day values yielded slightly higher accuracy (about 1°C), both suggesting that MODIS LST retrievals are reliable for similar land-cover classes and atmospheric conditions. Increasing the MODIS window size showed an overestimation of in-situ LST and some improvement in the daytime Terra and night-time Aqua biases, with the highest accuracy achieved with the 5 × 5 window. A comparison between MODIS emissivity from bands 31, 32, and in-situ emissivity showed that emissivity errors (relative error = ?0.30%) were insignificant.     

Land surface temperature and emissivity retrieval by integrating MODIS data onboard Terra and Aqua satellites

This paper presents an algorithm to retrieve land surface temperature (LST) and emissivity by integrating MODIS (Moderate Resolution Imaging Spectroradiometer) data onboard Terra and Aqua satellites. For a study area, there will be four pairs of day and night observations by MODIS onboard two satellites every day. Solar zenith angle, view zenith angle, and atmospheric water vapour have first been taken as independent variables to analyse their sensitivities to the same infrared channel measurements of MODIS on both Terra and Aqua satellites. Owing to their similar influences on the same MODIS band from Terra and Aqua satellites, four pairs of MODIS data from Terra and Aqua satellites can be thought of as MODIS measurement on a satellite at different viewing angles and viewing time. Comparisons between the retrieved results and in-situ measurements at three test sites (Qinghai Lake, Poyang Lake and Luancheng in China) indicate that the root mean square (rms) error is 0.66 K, except for the sand in Poyang Lake area. The rms error is less than 0.7 K when the retrieved results are compared with Earth Observing System (EOS) MODIS LST data products using the physics-based day/night algorithm. Emissivities retrieved by this algorithm are well compared to EOS MODIS emissivity data products (V5). The proposed algorithm can therefore be regarded as complementary and an extension to the EOS physics-based day/night algorithm.     

Analysis of leaf area index products from combination of MODIS Terra and Aqua data

A prototype product suite, containing the Terra 8-day, Aqua 8-day, Terra-Aqua combined 8- and 4-day products, was generated as part of testing for the next version (Collection 5) of the MODerate resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products. These products were analyzed for consistency between Terra and Aqua retrievals over the following data subsets in North America: single 8-day composite over the whole continent and annual time series over three selected MODIS tiles (1200 × 1200 km). The potential for combining retrievals from the two sensors to derive improved products by reducing the impact of environmental conditions and temporal compositing period was also explored. The results suggest no significant discrepancies between large area (from continent to MODIS tile) averages of the Terra and Aqua 8-day LAI and surface reflectances products. The differences over smaller regions, however, can be large due to the random nature of residual atmospheric effects. High quality retrievals from the radiative transfer based algorithm can be expected in 90-95% of the pixels with mostly herbaceous cover and about 50-75% of the pixels with woody vegetation during the growing season. The quality of retrievals during the growing season is mostly restricted by aerosol contamination of the MODIS data. The Terra-Aqua combined 8-day product helps to minimize this effect and increases the number of high quality retrievals by 10-20% over woody vegetation. The combined 8-day product does not improve the number of high quality retrievals during the winter period because the extent of snow contamination of Terra and Aqua observations is similar. Likewise, cloud contamination in the single-sensor and combined products is also similar. The LAI magnitudes, seasonal profiles and retrieval quality in the combined 4-day product are comparable to those in the single-sensor 8-day products. Thus, the combined 4-day product doubles the temporal resolution of the seasonal cycle, which facilitates phenology monitoring in application studies during vegetation transition periods. Both Terra and Aqua LAI products show anomalous seasonality in boreal needle leaf forests, due to limitations of the radiative transfer algorithm to model seasonal variations of MODIS surface reflectance data with respect to solar zenith angle. Finally, this study suggests that further improvement of the MODIS LAI products is mainly restricted by the accuracy of the MODIS observations.     

AERONET-based surface reflectance validation network (ASRVN) data evaluation: Case study for railroad valley calibration site

The AERONET-based Surface Reflectance Validation Network (ASRVN) is an operational processing system developed for validation of satellite derived surface reflectance products at regional and global scales. The ASRVN receives 50 × 50 km² subsets of MODIS data centered at AERONET sites along with AERONET aerosol and water vapor data, and performs an atmospheric correction. The ASRVN produces surface bidirectional reflectance factor (BRF), albedo, parameters of the Ross-Thick Li-Sparse (RTLS) BRF model, as well as Hemispherical-Directional Reflectance Factor (HDRF), which is required for comparison with the ground-based measurements. This paper presents a comparison of ASRVN HDRF with the ground-based HDRF measurements collected during 2001-2008 over a bright calibration Railroad Valley, Nevada site as part of the MODIS land validation program. The ground measurements were conducted by the Remote Sensing Group (RSG) at the University of Arizona using an ASD spectrometer. The study reveals a good agreement between ASRVN and RSG HDRF for both MODIS Terra and Aqua with rmse ~ 0.01-0.025 in the 500 m MODIS land bands B1-B7. Obtained rmse is below uncertainties due to the spatial and seasonal variability of the bright calibration 1 km² area. While two MODIS instruments have a similar rmse in the visible bands, MODIS Aqua has a better agreement (lower rmse) with the ground data than MODIS Terra at wavelengths 0.87-2.1 μm. An independent overall good agreement of two MODIS instruments with the ground data indicates that the relative calibration of MODIS Terra and Aqua at medium-to-bright reflectance levels for the stated time period is significantly better than uncertainties of the ASRVN and ground data.     

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.     

New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products

This paper discusses the lessons learned from analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) Land-Surface Temperature/Emissivity (LST) products in the current (V4) and previous versions, and presents eight new refinements for V5 product generation executive code (PGE16) and the test results with real Terra and Aqua MODIS data. The major refinements include considering surface elevation when using the MODIS cloudmask product, removal of temporal averaging in the 1 km daily level-3 LST product, removal of cloud-contaminated LSTs in level-3 LST products, and the refinements for the day/night LST algorithm. These refinements significantly improved the spatial coverage of LSTs, especially in highland regions, and the accuracy and stability of the MODIS LST products. Comparisons between V5 LSTs and in-situ values in 47 clear-sky cases (in the LST range from − 10 °C to 58 °C and atmospheric column water vapor range from 0.4 to 3.5 cm) indicate that the accuracy of the MODIS LST product is better than 1 K in most cases (39 out of 47) and the root of mean squares of differences is less than 0.7 K for all 47 cases or 0.5 K for all but the 8 cases apparently with heavy aerosol loadings. Emissivities retrieved by the day/night algorithm are well compared to the surface emissivity spectra measured by a sun-shadow method in two field campaigns. The time series of V5 MODIS LST product over two sites (Lake Tahoe in California and Namco lake in Tibet) in 2003 are evaluated, showing that the quantity and quality of MODIS LST products depend on clear-sky conditions because of the inherent limitation of the thermal infrared remote sensing.     

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.     

Detection rates of the MODIS active fire product in the United States

MODIS active fire data offer new information about global fire patterns. However, uncertainties in detection rates can render satellite-derived fire statistics difficult to interpret. We evaluated the MODIS 1 km daily active fire product to quantify detection rates for both Terra and Aqua MODIS sensors, examined how cloud cover and fire size affected detection rates, and estimated how detection rates varied across the United States. MODIS active fire detections were compared to 361 reference fires (≥ 18 ha) that had been delineated using pre- and post-fire Landsat imagery. Reference fires were considered detected if at least one MODIS active fire pixel occurred within 1 km of the edge of the fire. When active fire data from both Aqua and Terra were combined, 82% of all reference fires were found, but detection rates were less for Aqua and Terra individually (73% and 66% respectively). Fires not detected generally had more cloudy days, but not when the Aqua data were considered exclusively. MODIS detection rates decreased with fire size, and the size at which 50% of all fires were detected was 105 ha when combining Aqua and Terra (195 ha for Aqua and 334 ha for Terra alone). Across the United States, detection rates were greatest in the West, lower in the Great Plains, and lowest in the East. The MODIS active fire product captures large fires in the U.S. well, but may under-represent fires in areas with frequent cloud cover or rapidly burning, small, and low-intensity fires. We recommend that users of the MODIS active fire data perform individual validations to ensure that all relevant fires are included.     

A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States

Improved wildland fire emission inventory methods are needed to support air quality forecasting and guide the development of air shed management strategies. Air quality forecasting requires dynamic fire emission estimates that are generated in a timely manner to support real-time operations. In the regulatory and planning realm, emission inventories are essential for quantitatively assessing the contribution of wildfire to air pollution. The development of wildland fire emission inventories depends on burned area as a critical input. This study presents a Moderate Resolution Imaging Spectroradiometer (MODIS) - direct broadcast (DB) burned area mapping algorithm designed to support air quality forecasting and emission inventory development. The algorithm combines active fire locations and single satellite scene burn scar detections to provide a rapid yet robust mapping of burned area. Using the U.S. Forest Service Fire Sciences Laboratory (FiSL) MODIS-DB receiving station in Missoula, Montana, the algorithm provided daily measurements of burned area for wildfire events in the western U.S. in 2006 and 2007. We evaluated the algorithm's fire detection rate and burned area mapping using fire perimeter data and burn scar information derived from high resolution satellite imagery. The FiSL MODIS-DB system detected 87% of all reference fires > 4 km², and 93% of all reference fires > 10 km². The burned area was highly correlated (R² = 0.93) with a high resolution imagery reference burn scar dataset, but exhibited a large over estimation of burned area (56%). The reference burn scar dataset was used to calibrate the algorithm response and quantify the uncertainty in the burned area measurement at the fire incident level. An objective, empirical error based approach was employed to quantify the uncertainty of our burned area measurement and provide a metric that is meaningful in context of remotely sensed burned area and emission inventories. The algorithm uncertainty is ± 36% for fires 50 km² in size, improving to ± 31% at a fire size of 100 km². Fires in this size range account for a substantial portion of burned area in the western U.S. (77% of burned area is due to fires > 50 km², and 66% results from fires > 100 km²). The dominance of these large wildfires in burned area, duration, and emissions makes these events a significant concern of air quality forecasters and regulators. With daily coverage at 1-km² spatial resolution, and a quantified measurement uncertainty, the burned area mapping algorithm presented in this paper is well suited for the development of wildfire emission inventories. Furthermore, the algorithm's DB implementation enables time sensitive burned area mapping to support operational air quality forecasting.     

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.     

Spatio-temporal distribution of burned areas by ecoregions in Mexico and Central America

Fire activity in Mexico and Central America, and its associated emissions, has impacts across multiple scales. On the local-to-regional scale, fire activity impacts land use, productivity, and biodiversity. On the regional-to-global scale, fire activity impacts hydrological, biogeochemical, and atmospheric processes. A consistent, reliable, large-scale characterization of the spatial and temporal distribution of fire burned area is required to assess its environmental impacts and to support natural resources’ management. The spatial and temporal distributions of fire burned areas in ecoregions of Mexico and Central America are evaluated in this study for the period 2001–2014, using the satellite Moderate Resolution Imaging Spectroradiometer (MODIS) MCD45 Burned Area data set. The methodology combines the 500 m burned area product with a MODIS land cover product and a map of North American land cover to calculate the spatiotemporal variability of fire activity as a function of land-use type.

The total burned area over Mexico and Central America over the period 2001–2014 was found to be 614,243.5 km², but with significant interannual variability over the 14 years included in the study. Indeed, the minimum burned area over the period was 9892.25 km² in 2014 and the maximum was 37,669.50 km² in 2011, a fourfold increase. Burned areas were found to be concentrated in northern Mexico and on the Pacific coast, mainly from October to June. Agricultural burned areas accounted for 37% and 43% of total detected burns in Mexico and Central America, respectively. The largest extent of burned surface occurs in May for most land-cover types. The maximum density of burned areas occurred in the tropical dry forests ecoregion during the dry season. Both in Mexico and Central America, burned area anomalies have significant anti-correlation with precipitation anomalies.     

Large area monitoring with a MODIS-based Disturbance Index (DI) sensitive to annual and seasonal variations

Disturbance of the vegetated land surface, due to factors such as fire, insect infestation, windthrow and harvesting, is a fundamental driver of the composition forested landscapes with information on disturbance providing critical insights into species composition, vegetation condition and structure. Long-term climate variability is expected to lead to increases in both the magnitude and distribution of disturbances. As a consequence it is important to develop monitoring systems to better understand these changes in the terrestrial biosphere as well to inform managers about disturbance agents more typically captured through specific monitoring programs (such as focused on insect, fire, or agricultural conditions). Changes in the condition, composition and distribution pattern of vegetation can lead to changes in the spectral and thermal signature of the land surface. Using a 6-year time series of MODerate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) data we apply a previously proposed Disturbance Index (DI) which has been shown to be sensitive to both continuous and discontinuous change. Using Canada as an example area, we demonstrate the capacity of this Disturbance Index to monitor land dynamics over time. As expected, our results confirm a significant relationship between area flagged as disturbed by the index and area burnt as estimated from other satellite sources (R² = 0.78, p < 0.0001). The DI also demonstrates a sensitivity to capture and depict changes related to insect infestations. Further, on a regional basis the DI produces change information matching measured wide-area moisture conditions (i.e., drought) and corresponding agricultural outputs. These findings indicate that for monitoring a large area, such as Canada, the time series based DI is a useful tool to aid in change detection and national monitoring activities.     

基于Landsat8热红外遥感数据的山地地表温度地形效应研究

地表温度是影响地表能量收支平衡的重要参量,能够综合反演地表的水热交换过程。虽然当前在基于地表温度开展全球或者区域尺度的地表能量平衡研究方面取得一系列的进展,但是面向山地区域尺度的类似研究仍然面临较大的挑战。为分析山地复杂地形对山地地表温度时空分布的影响规律,基于具有较高空间分辨率的Landsat 8热红外数据,以我国西南典型山地为研究对象,定量反演该区域的地表温度空间分布状况,结合SRTM90DEM数据,选择从海拔、坡度和坡向3个关键地形因子角度分析山地地表温度的地形效应特征。结果发现:山地地表温度随地形因子均呈现出十分显著的变化特征。总体而言,地表温度均随着海拔和坡度的升高而降低,而在坡向方面,南坡的温度相比北坡的温度要高。在地形效应分析的基础上,通过开展1km空间尺度地形和地表温度的空间统计分析发现,山地1km尺度下地表温度存在较大的空间异质性,且其影响不可忽略。研究结果表明:开展山地地表水热过程遥感动态监测需高空间分辨率地表温度作为数据支持,以准确描述山地地形因素对地表能量交换过程的影响。     

MODIS time-series imagery for forest disturbance detection and quantification of patch size effects

The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m single day surface reflectance (MOD09GQK) and 16-day composite gridded vegetation index data (MOD13Q1) were used to detect forest harvest disturbance between 2000 and 2004 in northern Maine. A MODIS multi-date Normalized Difference Vegetation Index (NDVI) forest change detection map was developed from each MODIS data set. A Landsat TM/ETM+ change detection map was developed as a reference to assess the effect of disturbed forest patch size on classification accuracy (agreement) and disturbed area estimates of MODIS. The MODIS single day and 16-day composite data showed no significant difference in overall classification accuracies. However, the 16-day NDVI change detection map had marginally higher overall classification accuracy (at 85%), but had significantly lower detection accuracy related to disturbed patch size than the single day NDVI change detection map. The 16-day composite NDVI data achieved 69% detection accuracy and the single day NDVI achieved 76% when the disturbed patch size was greater than 20 ha. The detection accuracy increased to approximately 90% for both data sets when the patch size exceeded 50 ha. The R² (range 0.6 to 0.9) and slope (range 0.5 to 0.9) of regression lines between Landsat and MODIS data (based on forest disturbance percent of township) increased with the mean disturbed patch size of each township. The 95% confidence intervals of forest disturbance percent estimate for each township were narrow with less than 1% of each township at the mean MODIS forest disturbance level.     

Comparative Analysis on Three Land Surface Temperature Inversion Algorithm based on Landsat 8 Data over Sanheba Basin

The Land Surface Temperature (LST) of TIRS10 / Landsat 8 remote sensing data is studied and analyzed by combining the data and related parameters of Sanheba basin,and the LST inversion algorithm are used the Radiative Transfer Equation Method (RTE),Mono\|Window algorithm (MW) and Single\|Channel Method (SC).The parameters of the MW algorithm are corrected.The LST gray scale and density segmentation graphs,the histogram of LST and the cross validation flank are used to compare the results of the LST inversion algorithm.The results show that the three kinds of algorithms are similar to the linear fitting degree of LST,and the spatial distribution is consistent.The RTE and SC algorithm are close to each other,the average error of algorithm is 0~0.05 K.the LST of MW algorithm is higher than that of the other two algorithms,the average error of algorithm is 0~1.27 K.The LST of different land cover types in this basin is compared,and the inversion results can effectively reflect the details of the surface thermal field structure according to the different land cover types.The LST values obtained by these three algorithms are compared with the MODIS LST product values.The results show that there is a significant correlation between the LST values and the MODIS LST products.In this paper,3 kinds of the LST inversion algorithms are analyzed detailed accurate on TIRS10/Landsat 8 remote sensing data,provide a reference for other thermal infrared satellite data inversion LST algorithm,but also for the subsequent LST improve the accuracy of inversion basis.     

Evaluation and improvement of the MODIS land surface temperature/emissivity products using ground‐based measurements at a semi‐desert site on the western Tibetan Plateau

Current MODerate‐resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST, surface skin temperature)/emissivity products are evaluated and improvements are investigated. The ground‐based measurements of LST at Gaize (32.30° N, 84.06° E, 4420 m) on the western Tibetan Plateau from January 2001 to December 2002 agree well (mean and standard deviation of differences of 0.27 K and 0.84 K) with the 1‐km Version 004 (V4) Terra MODIS LST product (MOD11A1) generated by the split‐window algorithm. Spectral emissivities measured from surface soil samples collected at and around the Gaize site are in close agreement with the landcover‐based emissivities in bands 31 and 32 used by the split‐window algorithm. The LSTs in the V4 MODIS LST/emissivity products (MYD11B1 for Aqua and MOD11B1 for Terra) from the day/night LST algorithm are higher by 1–1.7 K (standard deviation around 0.6 K) in comparisons to the 5‐km grid aggregated values of the LSTs in the 1‐km products, which is consistent with the results of a comparison of emissivities. On average, the emissivity in MYD11B1 (MOD11B1) is 0.0107 (0.0167) less than the ground‐based measurements, which is equivalent to a 0.64 K (1.25 K) overestimation of LST around the average value of 285 K. Knowledge obtained from the evaluation of MODIS LST/emissivity retrievals provides useful information for the improvement of the MODIS LST day/night algorithm. Improved performance of the refined (V5) day/night algorithm was demonstrated with the Terra MODIS data in May–June 2004.     

Estimating northern peatland CO2 exchange from MODIS time series data

Studies using satellite sensor-derived data as input to models for CO₂ exchange show promising results for closed forest stands. There is a need for extending this approach to other land cover types, in order to carry out large-scale monitoring of CO₂ exchange. In this study, three years of eddy covariance data from two peatlands in Sweden were averaged for 16-day composite periods and related to data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled photosynthetic photon flux density (PPFD). Noise in the time series of MODIS 250 m vegetation indices was reduced by using double logistic curve fits. Smoothed normalized difference vegetation index (NDVI) showed saturation during summertime, and the enhanced vegetation index (EVI) generally gave better results in explaining gross primary productivity (GPP). The strong linear relationships found between GPP and the product of EVI and modeled PPFD (R² = 0.85 and 0.76) were only slightly stronger than for the product of EVI and MODIS daytime 1 km land surface temperature (LST) (R² = 0.84 and 0.71). One probable reason for these results is that several controls on GPP were related to both modeled PPFD and daytime LST. Since ecosystem respiration (ER) was largely explained by diurnal LST in exponential relationships (R² = 0.89 and 0.83), net ecosystem exchange (NEE) was directly related to diurnal LST in combination with the product of EVI and modeled PPFD in multiple exponential regressions (R² = 0.81 and 0.73). Even though the R² values were somewhat weaker for NEE, compared to GPP and ER, the RMSE values were much lower than if NEE would have been estimated as the sum of GPP and ER. The overall conclusion of this study is that regression models driven by satellite sensor-derived data and modeled PPFD can be used to estimate CO₂ fluxes in peatlands.     

Spatially and temporally continuous LAI data sets based on an integrated filtering method: Examples from North America

Leaf Area Index (LAI) is an important biophysical variable for characterizing the land surface vegetation. Global LAI product has been routinely produced from the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellite platforms. However, the MODIS standard LAI product is not continuous both spatially and temporally. To fill the gaps and improve the quality, we have developed a data filtering algorithm. This filter, called the temporal spatial filter (TSF), integrates both spatial and temporal characteristics for different plant functional types. The spatial gaps are first filled with the multi-year averages of the same day. If the values are missing over all years, the pixel is filled with a new estimate using the vegetation continuous field-ecosystem curve fitting method. The TSF integrates both the multi-seasonal average trend (background) and the seasonal observation. We implement this algorithm using the MODIS Collection 4 LAI product over North America. Comparison of the TSF results with the Savitzky-Golay filter indicates that the TSF performs much better in restoring the spatial and temporal distribution of seasonal LAI trends. The new LAI product has been validated by comparing with field measurements and the derived LAI maps from ETM+ data at a broadleaf forest site and an agricultural site. The validation results indicate that the new LAI product agrees better with both the field measurements and LAI values obtained from the ETM+ than does the MODIS LAI standard product, which usually shows higher LAI values.