Development of a model for the estimation of photosynthetically active radiation from geostationary satellite data in a tropical environment

This paper presents a model for the estimation of photosynthetically active radiation (PAR) from geostationary satellite data. The model is aimed to estimate the monthly average hourly PAR in a tropical environment. This model represents a physical relation of PAR incident on the earth's surface and satellite-derived earth-atmospheric albedo together with the absorption and scattering coefficients of various atmospheric constituents. The earth-atmospheric albedo was obtained from the Multifunctional Transport Satellite-1R (MTSAT-1R). The absorption of PAR by water vapor, an important process for the tropics, was computed from the ambient temperature and relative humidity. The absorption of PAR by aerosols was estimated by using the visibility data and aerosol optical properties obtained from the Aerosol Robotic Network (AERONET) of NASA in this region. The total column ozone from the Ozone Monitoring Instrument onboard of AURA satellite (OMI/AURA) was used for the estimation of the absorption of PAR by ozone. The model was validated against the monthly average hourly PAR from measurements at four solar radiation measuring stations situated in the tropical environment of Thailand. The values of the monthly average hourly PAR estimated from the model and those obtained from the measurement were in good agreement, with the root mean square error (RMSE) and mean bias error (MBE) of 9.8% and 0.6%, respectively. After the validation, the model was employed to estimate the monthly average hourly PAR over Thailand using a 4-year period of data from MTSAT-1R and other ancillary surface data. Values of the monthly average hourly PAR were presented as maps showing the geographical distribution of PAR. These maps reveal the diurnal and seasonal variation of PAR over the country.     

Local-scale heterogeneity of photosynthetically active radiation (PAR), absorbed PAR and net radiation as a function of topography, sky conditions and leaf area index

The local-scale spatial distribution of photosynthetically active radiation (PAR), absorbed PAR (APAR) and net all-wave radiation (Q^?) across the top of a forest canopy was investigated as a function of topography, sky conditions and forest heterogeneity for a forested hilly study site located in south-central Indiana, USA that is part of the FLUXNET and SpecNet networks. The method to estimate spatial variability of radiation components utilized theoretical radiation modeling applied to a topographic model combined with spatial distribution of leaf area index derived from IKONOS imagery and empirical models derived from data collected on a single flux tower. Modeled PAR and Q^? compared consistently well with observations from a single tower with differences typically less than 10%, although clear-sky conditions were simulated more accurately than cloudy conditions. Spatial variability of radiation was found to be very sensitive to topographic relief and could be scaled linearly by mean slope angle. Decreases in optical transmissivity and increases in cloudiness had a strong effect of reducing both the spatial average and standard deviation of radiation components. Spatial variability of APAR was 53% greater than PAR and the characteristic scale of variance was reduced due to finer scale and magnitude of variance of LAI. Clear seasonal patterns existed in both spatial average and standard deviation values with summer producing the largest mean values and weakest spatial variability due to smaller solar zenith angles and seasonality in both optical transmissivity (scaled linearly by specific humidity) and cloudiness. These findings of spatial variability illustrate the need to characterize the complex landscape patterns at flux tower sites, particularly where the goal is to relate flux tower data to satellite imagery.     

Estimation of high-spatial resolution clear-sky longwave downward and net radiation over land surfaces from MODIS data

Surface downwelling longwave radiation (LWDN) and surface net longwave radiation (LWNT) are two components in the surface radiation budget. In this study, we developed new linear and nonlinear models using a hybrid method to derive instantaneous clear-sky LWDN over land from the Moderate Resolution Imaging Spectroradiometer (MODIS) TOA radiance at 1 km spatial resolution. The hybrid method is based on extensive radiative transfer simulation (physical) and statistical analysis (statistical). Linear and nonlinear models were derived at 5 sensor view zenith angles (0°, 15°, 30°, 45°, and 60°) to estimated LWDN using channels 27-29 and 31-34. Separate models were developed for daytime and nighttime observations. Surface pressure effect was considered by incorporating elevation in the models. The linear LWDN models account for more than 92% of variations of the simulated data sets, with standard errors less than 16.27 W/m² for all sensor view zenith angles. The nonlinear LWDN models explain more than 93% of variations, with standard errors less than 15.20 W/m². The linear and nonlinear LWDN models were applied to both Terra and Aqua TOA radiance and validated using ground data from six SURFRAD sites. The nonlinear models outperform the linear models at five sites. The averaged root mean squared errors (RMSE) of the nonlinear models are 17.60 W/m² (Terra) and 16.17 W/m² (Aqua), with averaged RMSE ~ 2.5 W/m² smaller than that of the linear models. LWNT was estimated using the nonlinear LWDN models and the artificial neural network (ANN) model method that predicts surface upwelling longwave radiation. LWNT was also validated using the same six SURFRAD sites. The averaged RMSEs are 17.72 (Terra) and 16.88 (Aqua) W/m²; the averaged biases are − 2.08 (Terra) and 1.99 (Aqua) W/m². The LWNT RMSEs are less than 20 W/m² for both Terra and Aqua observations at all sites.     

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].     

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。     

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.     

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.     

Mapping surface temperature in large lakes with MODIS data

Satellite sensor MODIS on two platforms can produce Sea Surface Temperature over certain regions about three to four times per day. Our objective was to test if the MODIS SST product can be applied for lakes whose surface areas are large enough to be observed at the MODIS spatial resolution and to compare the satellite-derived lake surface temperatures with in situ measurements. Surface temperatures for Lakes Vänern and Vättern in Sweden, two of the largest European lakes, are extracted from the MODIS/Terra images for period 2001-2003. The results are analyzed on different quality levels, as all MODIS L2 products are equipped with an additional quality flag. We present temperature development over 2001-2003, and show the capability of the MODIS SST product to couple the known thermodynamical features in the lakes under study, where temperature varies greatly with space and time. These results can complement lake monitoring programs anywhere.     

Mapping plant functional types from MODIS data using multisource evidential reasoning

Reliable information about the geographic distribution and abundance of major plant functional types (PFTs) around the world is increasingly needed for global change research. Using remote sensing techniques to map PFTs is a relatively recent field of research. This paper presents a method to map PFTs from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a multisource evidential reasoning (ER) algorithm. The method first utilizes a suite of improved and standard MODIS products to generate evidence measures for each PFT class. The multiple lines of evidence computed from input data are then combined using Dempster's Rule of combination. Finally, a decision rule based on maximum support is used to make classification decisions. The proposed method was tested over the states of Illinois, Indiana, Iowa, and North Dakota, USA where crops dominate. The Cropland Data Layer (CDL) data provided by the United States Department of Agriculture were employed to validate our new PFT maps and the current MODIS PFT product. Our preliminary results suggest that multisource data fusion is a promising approach to improve the mapping of PFTs. For several major PFT classes such as crop, trees, and grass and shrub, the PFT maps generated with the ER method provide greater spatial details compared to the MODIS PFT. The overall accuracies increased for all the four states, with the biggest improvement occurring in Iowa from 51% (MODIS) to 64% (ER). The overall kappa statistic also increased for all the four states, with the biggest improvement occurring in Iowa from 0.03 (MODIS) to 0.38 (ER). The paper concludes with a discussion of several methodological issues pertaining to the further improvement of the ER approach.     

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.     

Calibration of NOAA16 AVHRR over a desert site using MODIS data

This paper presents a new approach to AVHRR-sensors cross-calibration in the visible to shortwave-infrared spectral domain using an a-priori, well calibrated sensor (MODIS). The approach has been tested over a stable Sahara desert site and was initially applied to compare the absolute calibration coefficients of three different bands of the Terra and Aqua MODIS instruments. The observed agreement was better than 1% for bands 1 (0.67 μm), 2 (0.87 μm) and 7 (2.13 μm). The approach was then applied to cross-calibrate the AVHRR sensor onboard NOAA16. The absolute calibration coefficients derived for bands 1 and 2, using the Terra MODIS as a reference, were compared to the vicarious coefficients derived using the ocean and clouds method [Vermote E.F. and Kaufman Y.J. (1995). Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views, International Journal of Remote Sensing, 16, 13, 2317-2340.]. The coefficients were consistent within less than 1%.     

Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS

Climate change is predicted to alter the canopy phenology of temperate and boreal forests, which will affect carbon, water, and energy budgets. Therefore, there is a great need to evaluate remotely sensed products for their potential to accurately capture canopy dynamics. The objective of this study was to compare several products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to field measurements of fraction photosynthetically active radiation (FPAR) and plant area index (PAI) for a deciduous broadleaf forest in northern Wisconsin in 2002. MODIS products captured the general phenological development of the canopy although MODIS products overestimated the leaf area during the overstory leaf out period. Field data suggest that the period from budburst to canopy maturity, or maximum PAI, occurred in 10 to 12 days while MODIS products predicted onset of greenness and maturity from 1 to 21 days and 0 to 19 days earlier than that from field observations, respectively. Temporal compositing of MODIS data and understory development are likely key factors explaining differences with field data. Maximum PAI estimates differed only by 7% between field derived and MODIS-based estimates of LAI. Implications for ecosystem modeling of carbon and water exchange and future research needs are discussed.     

Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration

The approach of using primarily satellite observations to estimate ecosystem gross primary production (GPP) without resorting to interpolation of many surface observations has recently shown promising results. Previous work has shown that the remote sensing based greenness and radiation (GR) model can give accurate GPP estimates in crops. However, the feasibility of its application and the model calibration to other ecosystems remain unknown. With the enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) images and the surface based estimates of photosynthetically active radiation (PAR), we provide an analysis of the GR model for estimating monthly GPP using flux measurements at fifteen sites, representing a wide range of ecosystems with various canopy structures and climate characteristics. Results demonstrate that the GR model can provide better estimates of GPP than that of the temperature and greenness (TG) model for the overall data classified as non-forest (NF), deciduous forest (DF) and evergreen forest (EF) sites. Calibration of the GR model is also conducted and has shown reasonable results for all sites with a root mean square error of 47.18 g C/m²/month. Different coefficients acquired for the three plant functional types indicate that there are shifts of importance among various factors that determine the monthly vegetation GPP. The analysis firstly shows the potential use of the GR model in estimating GPP across biomes while it also points to the needs of further considerations in future operational applications.     

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.     

Mapping insect defoliation in Scots pine with MODIS time-series data

Insect damage is a general problem that disturbs the growth of forests, causing economic losses and affecting carbon sequestration. Coarse-resolution data from satellites are potentially useful for national and regional mapping of forest damage, but the accuracy of these methods has not been fully examined. In this study, a method was tested for the mapping of defoliation in Scots pine [Pinus silvestris] forests in southeast Norway caused by the pine sawfly [Neodiprion sertifer], with the use of multi-temporal MODIS 16-day composite vegetation index data and the TIMESAT processing method. The damage mapping method used differences in summer mean values and angles of the seasonal profiles, indicating decreasing foliage density, to identify pixels that represent areas containing forest damage. In addition to 16-day NDVI the Wide Dynamic Range Vegetation Index (WDRVI) was tested. Damage areas were identified by classifying data into pixels representing damaged versus undamaged forest areas using a boolean combination of thresholded parameters. Classification results were evaluated against the change in LAI estimated from airplane LIDAR measurements, as an indicator of defoliation. The damage classifications detected 71% to 82% of the pixels with damage, and had kappa coefficients varying between 0.48 and 0.63, indicating some overestimation. This was due e.g. to failure to include clear-cut areas in the evaluation data. Damage classification with WDRVI only resulted in slight improvement compared to the NDVI. Only weak relationships were found between the LIDAR-estimated defoliation and the change parameters obtained from MODIS. Consequently, mapping of the degree of defoliation from MODIS was abandoned. In conclusion, the damage detection method based on MODIS data was found to be useful for locating insect damage, but not for estimating its intensity. Control of the detected damage areas using high-resolution remote sensing data, aerial survey, or fieldwork is recommended for accurate delineation in operational applications.     

Mapping solar ultraviolet radiation from satellite data in a tropical environment

This paper presents a technique for mapping erythemally-weighted solar ultraviolet (EUV) radiation from satellite data in a tropical environment. A satellite-based EUV radiation model was formulated for calculating the EUV daily dose from satellite-derived earth-atmospheric albedo, total column ozone and other ground-based ancillary data. The earth-atmospheric albedo was obtained from a geostationary satellite (GMS5) while the total column ozone was retrieved from a polar orbiting satellite (EP/TOMS). The model was validated against the monthly average EUV daily dose from the measurements at four solar radiation monitoring stations located in the tropical environment of Thailand. The monthly average EUV daily dose calculated from the model was in reasonable agreement with that obtained from the measurement, with root mean square difference (RMSD) and mean bias difference (MBD) of 12.3% and 0.7%, respectively. After the validation, the model was used to calculate the monthly average EUV daily dose over Thailand employing an 8-year period of data from GMS5, EP/TOMS and other ancillary surface data. Values of the monthly average of EUV daily dose were presented as monthly and yearly maps. These maps reveal that the tropical monsoons have a strong influence on the EUV in this region.     

Mapping PAR using MODIS atmosphere products

Instantaneous PAR (Photosynthetically Active Radiation), computed from atmospheric parameters from individual images from the MODIS sensors aboard the Terra and Aqua satellite platforms, is combined to derive daily integrated PAR and mapped to a local coordinate system. Compared to field observations, the daily integrated PAR values were shown to have average errors in the order of 5-8%, with individual estimation errors as high as 21%, but monthly averages showed much better correspondence with observations yielding averaged absolute errors of around 5%. The error appears to be mostly related to uncertainties in the MODIS aerosol retrieval accuracy. This accuracy and the medium spatial resolution of the PAR map compare very favourably to other sources of PAR data and make this a useful product in the improved assessment of vegetation dynamics.     

Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.     

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.