Estimation of instantaneous net surface longwave radiation from MODIS cloud-free data

This paper develops a statistical regression method to estimate the instantaneous Downwelling Surface Longwave Radiation (DSLR) for cloud-free skies using only the satellite-based radiances measured at the Top Of the Atmosphere (TOA), and subsequently combines the DSLR with the MODIS land surface temperature/emissivity products (MOD11_L2) to estimate the instantaneous Net Surface Longwave Radiation (NSLR). The proposed method relates the DSLR directly to the TOA radiances in the MODIS Thermal InfraRed (TIR) channels provided that the terrain altitude and the satellite Viewing Zenith Angle (VZA) are known. The simulation analysis shows that the instantaneous DSLR could be estimated by the proposed method with the Root Mean Square Error (RMSE) of 12.4 W/m² for VZA = 0 and terrain altitude z = 0 km. Similar results are obtained for the other VZAs and altitudes. Considering the MODIS instrumental errors of 0.25 K for the TOA brightness temperatures in channels 28, 33 and 34, and of 0.05 K for channels 29 and 31, and of 0.35 K for channel 36, the overall retrieval accuracy in terms of the RMSE is decreased to 13.1 W/m² for the instantaneous DSLR. Moreover, a comparison of MODIS derived DSLR and NSLR are done with the field measurements made at six sites of the Surface Radiation Budget Network (SURFRAD) in the United States for days with cloud-free conditions at the moment of MODIS overpass in 2006. The results show that the bias, RMSE and the square of the correlation coefficient (R²) between the MODIS derived DSLR with the proposed method and the field measured DSLR are 20.3 W/m², 30.1 W/m² and 0.91 respectively, and bias = 11.7 W/m², RMSE = 26.1 W/m² and R² = 0.94 for NSLR. In addition, the scheme proposed by Bisht et al. [Bisht, G., Venturini, V., Islam, S., & Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear-sky days. Remote Sensing of Environment, 97, 52-67], which requires the MODIS atmospheric profile product (MOD07) and also the MODIS land surface temperature/emissivity products (MOD11_L2) as inputs, is used to estimate the instantaneous DSLR and NSLR for comparison with the field measurements as well as the MODIS derived DSLR and NSLR using our proposed method. The results of the comparisons show that, at least for our cases, our proposed method for estimating DSLR from the MODIS radiances at the TOA and the resultant NSLR gives results comparable to those estimated with Bisht et al.'s scheme [Bisht, G., Venturini, V., Islam, S., & Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear-sky days. Remote Sensing of Environment, 97, 52-67].     

Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study

Net radiation is a key component in the surface radiation budget. Numerous studies have developed frameworks to estimate net radiation or its components (upwelling or downwelling longwave and/or shortwave radiation) from remote sensing data for clear sky conditions. Application of existing methodologies to estimate net radiation for cloudy sky conditions from remote sensing sensors remains a significant challenge. In this paper, we present a framework to estimate instantaneous and daily average net radiation under all sky conditions from using the data from the MODerate Resolution Imaging Spectroradiometer (MODIS), onboard from the Terra satellites. Bisht et al. (2005) methodology is used for the clear sky portion of the MODIS overpass; while for cloudy portion of the MODIS overpass an extension of Bisht et al. (2005) methodology is applied. The extension of Bisht et al. (2005) methodology utilizes the MODIS cloud data product (MOD06_L2) for cloud top temperature, cloud fraction, cloud emissivity, cloud optical thickness and land surface temperature for cloudy days. The methodology is applied over the Southern Great Plains (SGP) for a time period covering all seasons of 2006. During the MODIS-Terra overpasses in 2006 over the SGP, only 24% of day-overpasses and 9% of night-overpasses had 75% or more of the study region as cloud free. Thus, this proposed study is applicable to a large portion of the MODIS-Terra overpasses. The root mean square errors (RMSE) of instantaneous and daily average net radiation estimated under cloudy conditions using the MOD06_L2 product, comparing to ground-based measurements are 37 W m^− 2 and 38 W m^− 2, respectively. The strength of the proposed methodology is that it can rely exclusively on remote sensing data in the absence of ancillary ground observations, thus it has a potential to estimate surface energy budget globally.     

Development of a hybrid method for estimating land surface shortwave net radiation from MODIS data

Accurate estimation of shortwave net radiation (S_n) at a high spatial resolution is critical for regional and global land surface models. Current surface radiation budget products have fine temporal resolutions, but only coarse spatial resolutions that are not suitable for land applications. A hybrid algorithm was developed in this study to estimate S_n from MODIS data under both clear and cloudy sky conditions without requiring coarser resolution ancillary data. This algorithm was validated using ground measurements at seven sites of the SURFace RADiation budget observing network (SURFRAD) in the United States. Instantaneous S_n estimated by this method was also compared with GEWEX/SRB and ISCCP data, and other methods. The results indicate that our algorithm can produce S_n at 1-km resolution with improved accuracy and is easily implemented to generate operational global products. Daily integrated S_n is estimated at 1-km resolution using instantaneous S_n. These finer spatial resolution datasets capture the specific sequence of the redistribution of the available energy at the Earth's surface. Therefore, they support recent high resolution land surface models.     

Improved estimation of aerosol optical depth from MODIS imagery over land surfaces

Estimation of aerosol loadings is of great importance to the studies on global climate changes. The current Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol estimation algorithm over land is based on the “dark-object” approach, which works only over densely vegetated (“dark”) surfaces. In this study, we develop a new aerosol estimation algorithm that uses the temporal signatures from a sequence of MODIS imagery over land surfaces, particularly “bright” surfaces. The estimated aerosol optical depth is validated by Aerosol Robotic Network (AERONET) measurements. Case studies indicate that this algorithm can retrieve aerosol optical depths reasonably well from the winter MODIS imagery at seven sites: four sites in the greater Washington, DC area, USA; Beijing City, China; Banizoumbou, Niger, Africa; and Bratts Lake, Canada. The MODIS aerosol estimation algorithm over land (MOD04), however, does not perform well over these non-vegetated surfaces. This new algorithm has the potential to be used for other satellite images that have similar temporal resolutions.     

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.     

Estimating clear-sky land surface longwave upwelling radiation from MODIS data using a hybrid method

Surface longwave upwelling radiation (LWUP) is one of the four components for calculating the earth’s surface radiation budget. Under the general framework of the hybrid method, we developed linear models for estimating the global 1-km instantaneous clear-sky LWUP from the top-of-atmosphere (TOA) radiance of the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared channels 29, 31, and 32. Extensive radiative transfer simulations were conducted to produce a large number of representative samples, from which the linear model was derived. The derived hybrid model was first evaluated using ground measurements collected at 15 sites from two networks (SURFRAD and ASRCOP). According to the validation results, the average bias and root mean square error (RMSE) of ?0.55 and 15.76 W m^?2, respectively, were obtained by averaging the mean bias and RMSE for the two networks. Compared to a hybrid method developed by a previous study and the temperature-emissivity method, our linear model had a superior performance.     

Mapping incident photosynthetically active radiation from MODIS data over China

Photosynthetically active radiation (PAR) is a key input parameter for almost all terrestrial ecosystem models, but the spatial resolution of current PAR products is too coarse to satisfy regional application requirements. In this paper, we present an operational system for PAR retrieval from MODIS data that is based on an idea proposed by [Liang, S., Zheng, T., Liu, R., Fang, H., Tsay, S. -C., & Running, S. (2006). Estimation of incident photosynthetically active radiation from Moderate Resolution Imaging Spectrometer data. Journal of Geophysical Research, 111, D15208. doi:10.1029/2005JD006730]. However, the operational system for PAR retrieval described here contains several improvements. The algorithm utilizes MODIS 1B data combining MODIS land surface products and BRDF model parameters products to directly estimate diffuse PAR, direct PAR and total PAR. Times-series data interpolation removes the noise and cloud contamination of land surface reflectance. PAR is retrieved by searching look-up tables calculated using a radiative transfer model. The system can automatically process MODIS 1B data to generate instantaneous and daily PAR. The instantaneous PAR products are compared with observational data from seven ChinaFLUX stations, and daily total PAR estimates are compared with those estimates of global radiation from 98 meteorological stations over China. The results indicate that this approach can produce reasonable PAR estimates, although this method overestimates PAR for low values of PAR.     

Improvements of the MODIS terrestrial gross and net primary production global data set

MODIS primary production products (MOD17) are the first regular, near-real-time data sets for repeated monitoring of vegetation primary production on vegetated land at 1-km resolution at an 8-day interval. But both the inconsistent spatial resolution between the gridded meteorological data and MODIS pixels, and the cloud-contaminated MODIS FPAR/LAI (MOD15A2) retrievals can introduce considerable errors to Collection4 primary production (denoted as C4 MOD17) results. Here, we aim to rectify these problems through reprocessing key inputs to MODIS primary vegetation productivity algorithm, resulting in improved Collection5 MOD17 (here denoted as C5 MOD17) estimates. This was accomplished by spatial interpolation of the coarse resolution meteorological data input and with temporal filling of cloud-contaminated MOD15A2 data. Furthermore, we modified the Biome Parameter Look-Up Table (BPLUT) based on recent synthesized NPP data and some observed GPP derived from some flux tower measurements to keep up with the improvements in upstream inputs. Because MOD17 is one of the down-stream MODIS land products, the performance of the algorithm can be largely influenced by the uncertainties from upstream inputs, such as land cover, FPAR/LAI, the meteorological data, and algorithm itself. MODIS GPP fits well with GPP derived from 12 flux towers over North America. Globally, the 3-year MOD17 NPP is comparable to the Ecosystem Model-Data Intercomparison (EMDI) NPP data set, and global total MODIS GPP and NPP are inversely related to the observed atmospheric CO₂ growth rates, and MEI index, indicating MOD17 are reliable products. From 2001 to 2003, mean global total GPP and NPP estimated by MODIS are 109.29 Pg C/year and 56.02 Pg C/year, respectively. Based on this research, the improved global MODIS primary production data set is now ready for monitoring ecological conditions, natural resources and environmental changes.     

Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear sky days

A simple scheme is proposed to estimate instantaneous net radiation over large heterogeneous areas for clear sky days using only remote sensing observations. Our method attempts to develop an algorithm which primarily uses remote sensing information and eliminates the need for ground information as model input, by using various land and atmospheric data products available from Terra-MODIS. It explicitly recognizes the need for spatially varied parameters and provides a distributed net radiation map over large heterogeneous domain with fine spatial resolution. Since instantaneous net radiation estimates have limited scope compared to daily average values or diurnal cycle, a sinusoidal model is proposed to estimate diurnal cycle of net radiation. The sinusoidal model is capable of retrieving the diurnal variations of net radiation with a single instantaneous net radiation estimate from the satellite. Preliminary results, using data over Southern Great Plains, show good agreement with ground-based observations. It appears that the methodology presented here can estimate instantaneous and daily net radiation with comparable accuracy to those of current methods that use ground-based observations and mainly provide point estimates.     

Estimation of net surface shortwave radiation from MODIS data

Net surface shortwave radiation (NSSR) is a key quantity for the estimation of surface energy budget and is used in various land-surface models. In this article, two different methodologies, including three empirical algorithms and one advanced simplified theoretical algorithm for estimating instantaneous NSSR from Moderate Resolution Imaging Spectroradiometer (MODIS) data are explored and summarized. An advanced simplified theoretical algorithm is developed by combining two simplified radiative-transfer models with various MODIS atmosphere and land products. To comprehensively evaluate these algorithms, ground measurements from seven stations widely distributed in different climatic regions of China are used. The results indicate that under clear-sky conditions, the three empirical algorithms present appreciable difference in accuracy, while under cloudy skies, they produce similar, but not very good, predictions. Compared with these empirical methods, the simplified theoretical algorithm we adopt can significantly improve accuracy. The root mean square difference (RMSD) yielded by this algorithm is approximately 54 W?m^?2 under clear skies and 83 W?m^?2 under cloudy skies, respectively. Since the utility of instantaneous NSSR estimates is limited compared to that of the daily average value, a simple scheme to acquire the daily average NSSR is established, which is based on instantaneous estimations from two satellite MODIS sensors (Terra: AM and Aqua: PM), and the daily average NSSR over the Beijing area is also mapped.     

基于船载太阳光度计的中国海域MODIS卫星反演气溶胶光学参数验证

利用2006~2007年中国海洋大学东方红2号科考船在中国黄海和东海海域的POM-01MK2太阳光度计气溶胶光学参数的观测资料,与MODIS反演结果进行比较,给出了光学厚度、| ngstrm指数和粒子有效半径的对比结果。结果表明：两种观测方法得到的气溶胶光学厚度的一致性较好,相关系数（标准差）达0.97（0.08）,73%的对比结果在期望误差（Δτ=±0.03±0.05τ）之内;季节分类对比结果,秋季和春季相关系数（标准差）均为0.97（0.08）;海域分类对比结果,黄海北部海域和黄海南部海域相关系数（标准差）分别为0.98（0.08）和0.76（0.10）。MODIS反演得到的| ngstrm指数偏低,相关系数（标准差）为0.67（0.23）;按季节分类得到,秋季和春季相关系数（标准差）分别为0.71（0.27）和0.62（0.19）;按海域分类得到,黄海北部海域和黄海南部海域相关系数（标准差）分别为0.87（0.07）和0.70（0.30）。粒子有效半径的对比结果偏差和离散度较大,相关系数（标准差）仅为0.31（0.10）。造成这种现象的原因可能是该海域沙尘气溶胶和人类源二次气溶胶浓度较高导致海上气溶胶光学性质同MODIS反演中使用的光学参量有较大差别。     

Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in pixel-wise LST. Spatial scaling may account for the uncertainty, however, different approaches lead to differences in scaled values. Satellite-retrieved LST may be representative of the pixel-wise LST and useful for scaling analysis, but the limited accuracy of retrieved values adds uncertainty into the scaled values. Based on the Stefan-Boltzmann (S-B) law, this study proposed scaling approaches for LST over flat and relief areas to explore the combined uncertainties in scaling using satellite-retrieved data. To take advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from these two sensors were examined for part of the Loess Plateau in China. 90-m ASTER LST data were scaled up to 1 km using the proposed approaches, and variation in the LST was generally reduced after scaling. Amongst the sources of uncertainties, surface heterogeneity (emissivity) and different scaling approaches resulted in very minor differences, with a maximum difference of 0.2 K for the upscaled LST. Terrain features, taken as an areal weighting factor, had negligible effects on the upscaled value. Limited accuracy of the retrieved LST was the major uncertainty. The overall LST increased 0.6 K on average with correction for terrain-induced angular effect and 0.4 K for both angular and adjacency effects over the study area. Accounting for terrain correction in scaling is necessary for rugged areas. With terrain correction, the upscaled ASTER LST achieved an agreement of − 0.1 ± 1.87 K and a root mean square error (RMSE) of 1.87 K overall with the 1-km MODIS LST rectified by Wan et al.'s approach [Wan, Z., Zhang, Y., Zhang Q., Li, Z.-L. (2002b), Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, 83, 163-180]. Refining the rectification approach resulted in a better agreement of − 0.2 ± 1.57 K and a RMSE of 1.58 K.     

Continental-scale net radiation and evapotranspiration estimated using MODIS satellite observations

Evapotranspiration (ET) is a major pathway for water loss from many ecosystems, and its seasonal variation affects soil moisture and net ecosystem CO₂ exchange. We developed an algorithm to estimate ET using a semi-empirical Priestley-Taylor (PT) approach, which can be applied at a range of spatial scales. We estimated regional net radiation (R_net) at monthly time scales using MODerate resolution Imaging Spectroradiometer (MODIS) albedo and land surface temperature. Good agreement was found between satellite-based estimates of monthly R_net and field-measured R_net, with a RMSE of less than 30 W m^− 2. An adjustable PT coefficient was parameterized as a function of leaf area index and soil moisture based on observations from 27 AmeriFlux eddy covariance sites. The biome specific optimization using tower-based observations performed well, with a RMSE of 17 W m^− 2 and a correlation of 0.90 for predicted monthly latent heat. We implemented the approach within the hydrology module of the CASA biogeochemical model, and used it to estimate ET at a 1 km spatial resolution for the conterminous United States (CONUS). The RMSE of modeled ET was reduced to 21.1 mm mon^− 1, compared to 27.1 mm mon^− 1 in the original CASA model. The monthly ET rates averaged over the Mississippi River basin were similar to those derived using GRACE satellite measurements and river discharge data. ET varied substantially over the CONUS, with annual mean values of 110 ± 76 mm yr^− 1 in deserts, 391 ± 176 mm yr^− 1 in savannas and grasslands, and 840 ± 234 mm yr^− 1 in broadleaf forests. The PT coefficient was the main driver for the spatial variation of ET in arid areas, whereas R_net controlled ET when mean annual precipitation was higher than approximately 400 mm yr^− 1.     

Percent surface water estimation from MODIS BRDF 16-day image composites

Four models for deriving percent surface water estimates were developed for use with MODIS16-day Bidirectional Reflectance Distribution Function (BRDF) corrected composite images. The models allow intra-annual surface water estimates to be produced with 1 km spatial resolution and an 8-day temporal resolution when applied to image composites from sensors on both the Aqua and Terra platforms. The surface water models are conceptually simple, relying on widely used indices (NDVI, NDWI, and tasseled cap), but computationally intensive. The models differ in the time and effort required to produce or acquire the inputs necessary for model training. The models were applied and tested in Yukon Flats National Wildlife Refuge, an area with varied surface water types including ponds, fens, and the Yukon River and its tributaries. Resulting accuracies peaked with an R² of approximately 0.625, and model accuracies were higher for pixels with higher percentages of water.     

Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains

Evapotranspiration (ET) cannot be measured directly from satellite observations but remote sensing can provide a reasonably good estimate of evaporative fraction (EF), defined as the ratio of ET and available radiant energy. It is feasible to estimate EF using a contextual interpretation of radiometric surface temperature (T_o) and normalized vegetation index (NDVI) from multiple satellites. Recent studies have successfully estimated net radiation (R_n) over large heterogeneous areas for clear sky days using only remote sensing observations. With distributed maps of EF and R_n, it is now possible to explore the feasibility and robustness of ET estimation from multiple satellites. Here we present the results of an extensive inter-comparison of spatially distributed ET and related variables (NDVI, T_o, EF and R_n) derived from MODIS and AVHRR sensors onboard EOS Terra, NOAA14 and NOAA16 satellites respectively. Our results show that although, NDVI and T_o differ with the sensor response functions and overpass times, contextual space of NDVI-T_o diagram gives comparable estimates of EF. The utility of different sensors is demonstrated by validating the estimated ET results to ground flux stations over the Southern Great Plains with a root mean square error of 53, 51 and 56.24 Wm^− 2, and a correlation of 0.84, 0.79 and 0.77 from MODIS, NOAA16 and NOAA14 sensors respectively.     

A web-based application for beekeepers to visualise patterns of growth in floral resources using MODIS data

The honey bee industry is of immense importance to global agriculture. In many countries beekeepers are migratory and move their hives between flowering events. Predicting such flowering events is particularly difficult in Australia due to the irregular flowering of eucalypts. We have developed a web-based application for Victorian beekeepers to visualise patterns of growth in floral resources using MODIS and other data, and thus make remote predictions about whether flowering will occur at their apiary sites. We demonstrate the use of this application through comparing ironbark (Eucalyptus tricarpa) growth patterns with flowering and honey production records. While the scientific community as a whole has embraced the use of satellite imagery as a tool for phenological studies, our prototype represents the first attempt to make this same information available to a more general audience.     

Estimation of Surface Solar Radiation Using MODIS Satellite Data and RSTAR Model

The MODIS aerosol product and cloud product data are combined with the atmospheric radiation transmission model RSTAR to calculate the surface solar radiation under sunny and cloudy conditions.The results are compared with the ground observed values of Xianghe integrated radiation station.It shows that the simulated value and observed value have good correlation.The R² and RMSE are 0.95 and 38.8 W/m² in the clear skies,while 0.88 and 88.2 W/m² in the cloudy skies.The results show that more cloud\|aerosol mixed states is in Xianghe station,while MODIS can only invert single microphysical parameter of cloud layer,which leads to error of model input parameter,bringing error to the calculation result of surface solar radiation.In order to quantitatively analyze the effect,the RSTAR radiation model was used to calculate the radiance values,and invert f different cloud and aerosol optical thickness,calculating surface solar radiation.The results show that the error of estimation of surface solar radiation is 1.29%~1.56% when the aerosol optical thickness (AOD)is 0.1,compared with the single layer.With the increase of AOD,the effect of AOD increased obviously.The relative error was 17.79%~18.38% when AOD was 1.2.For the heavily polluted areas of North China,it is important to analyze the influence of aerosol on the surface solar radiation under cloud cover,which is very important to improve the calculation accuracy of surface solar radiation under cloud conditions.     

Comparison of atmospheric aerosol climatologies over southwestern Spain derived from AERONET and MODIS

The southwestern area of Spain, by its geographical and climatological conditions, is a key location for the characterization of atmospheric aerosol properties. The present study is aimed at evaluating the reliability of satellite-based aerosol climatologies, as inferred from level 2 standard aerosol products such as the Terra-MODIS (Moderate Resolution Imaging Radiometer) MOD04 aerosol product, with an application over this region during the period 2000-2008.This evaluation is carried out by means of comparison with ground-based data from the AERONET station of El Arenosillo (Spain, 37.1N, 6.7W), which has been providing continuous data since 2000. The focus of this paper is the climatology of two aerosol optical parameters: the aerosol optical depth (AOD) and the Ångström exponent.AERONET ground-based measurements give an annual mean value of 0.16 ± 0.12 and a median of 0.12 for the AOD, and a mean value of 1.20 ± 0.47 for the Ångström exponent. The seasonal pattern is characterized by two maxima, the most important maximum occurs in summer months, and the other one in late-winter/early-spring. Lowest values appear in fall and winter, however, a local minimum is observed in July which is only detected with the long-term data series.The mean climatological AOD based on AERONET exhibits complex seasonal patterns (i.e. with multiple local extrema), which are not always captured by MODIS-based climatology. MODIS only reproduces low values of the AOD in winter and high values in summer, as well as the local minimum of July which is sharper when using over-land retrievals. The time series of the AOD retrieved from MODIS both over land and ocean are in relatively good agreement with the ground-based measurements, with a monthly overestimation of about 30% on average, and higher differences in spring. Seasonal patterns from MODIS are better reproduced over land than over ocean. The agreement between daily AERONET and MODIS, as assessed by linear regression, gives correlation coefficients above 80% and an intercept bias below 0.03.     

MODIS数据地表温度反演劈窗算法比较

针对MODIS数据,分析比较了QIN和Wan-Dozier两种劈窗算法地表温度(LST)反演精度和误差分布。首先利用辐射传输模型MODTRAN4.0,结合TIGR大气廓线数据,评价两种算法绝对精度,然后基于误差传递理论分析评价二者的总精度,最后对两种算法的LST反演结果进行比较。研究表明针对所有廓线数据,两种算法绝对精度相差不大,但Wan-Dozier算法绝对精度受地表温度和水汽含量变化的影响程度要大于QIN算法;两种算法总精度相差不大,且主要误差源均为算法绝对精度和地表比辐射率精度,QIN算法反演结果对地表比辐射率的敏感性要略高于Wan-Dozier算法;两种算法得到研究区LST分布情况基本一致,均可表现空间LST分布差异,其中水体和裸土的LST反演结果差异较大,城镇和植被平均温度差异在0.5 K以内。     

Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers

We combined remote sensing and in-situ measurements to estimate evapotranspiration (ET) from riparian vegetation over large reaches of western U.S. rivers and ET by individual plant types. ET measured from nine flux towers (eddy covariance and Bowen ratio) established in plant communities dominated by five major plant types on the Middle Rio Grande, Upper San Pedro River, and Lower Colorado River was strongly correlated with Enhanced Vegetation Index (EVI) values from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the NASA Terra satellite. The inclusion of maximum daily air temperatures (T_a) measured at the tower sites further improved this relationship. Sixteen-day composite values of EVI and T_a were combined to predict ET across species and tower sites (r² = 0.74); the regression equation was used to scale ET for 2000-2004 over large river reaches with T_a from meteorological stations. Measured and estimated ET values for these river segments were moderate when compared to historical, and often indirect, estimates and ranged from 851-874 mm yr^− 1. ET of individual plant communities ranged more widely. Cottonwood (Populus spp.) and willow (Salix spp.) stands generally had the highest annual ET rates (1100-1300 mm yr^− 1), while mesquite (Prosopis velutina) (400-1100 mm yr^− 1) and saltcedar (Tamarix ramosissima) (300-1300 mm yr^− 1) were intermediate, and giant sacaton (Sporobolus wrightii) (500-800 mm yr^− 1) and arrowweed (Pluchea sericea) (300-700 mm yr^− 1) were the lowest. ET rates estimated from the flux towers and by remote sensing in this study were much lower than values estimated for riparian water budgets using crop coefficient methods for the Middle Rio Grande and Lower Colorado River.