Moderate Resolution Imaging Spectroradiometer Algorithm Research Articles

Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity

Abstract. While the use and data assimilation (DA) of operational Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data is commonplace, MODIS is scheduled to sunset in the next year. For data continuity, focus has turned to the development of next-generation aerosol products and sensors such as those associated with the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPOESS Preparation Project (S-NPP) and NOAA-20. Like MODIS algorithms, products from these sensors require their own set of extensive error characterization and correction exercises. This is particularly true in the context of monitoring significant aerosol events that tax an algorithm's ability to separate cloud from aerosol and account for multiple scattering related errors exacerbated by uncertainties in aerosol optical properties. To investigate the performance of polar-orbiting satellite algorithms to monitor and characterize significant events, a level 3 (L3) product has been developed using a consistent aggregation methodology for 4 years of observations (2016–2019) that is referred to as the SSEC/NRL L3 product. Included in this product are the AErosol RObotic NETwork (AERONET), MODIS Dark Target, Deep Blue, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. These MODIS “baseline algorithms” are compared to NASA's recently released NASA Deep Blue algorithm for use with VIIRS. Using this new dataset, the relative performance of the algorithms for both land and ocean were investigated with a focus on the relative skill of detecting severe events and accuracy of the retrievals using AERONET. Maps of higher-percentile aerosol optical depth (AOD) regions of the world by product identified those with the highest measured AODs and determined what is high by local standards. While patterns in AOD match across products and median to moderate AOD values match well, there are regionally correlated biases between products based on sampling, algorithm differences, and AOD range – in particular for higher AOD events. Most notable are differences in boreal biomass burning and Saharan dust. Significant percentile biases must be accounted for when data are used in trend studies, data assimilation, or inverse modeling. These biases vary by aerosol regime and are likely due to retrieval assumptions in lower boundary conditions and aerosol optical models.

A novel method of identifying and analysing oil smoke plumes based on MODIS and CALIPSO satellite data

Abstract. Black carbon aerosols are the second largest contributor to global warming while also being linked to respiratory and cardiovascular disease. These particles are generally found in smoke plumes originating from biomass burning and fossil fuel combustion. They are also heavily concentrated in smoke plumes originating from oil fires, exhibiting the largest ratio of black carbon to organic carbon. In this study, we identified and analysed oil smoke plumes derived from 30 major industrial events within a 12-year timeframe. To our knowledge, this is the first study of its kind that utilized a synergetic approach based on satellite remote sensing techniques. Satellite data offer access to these events, which, as seen in this study, are mainly located in war-prone or hazardous areas. This study focuses on the use of MODIS (Moderate Resolution Imaging Spectroradiometer) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) products regarding these types of aerosol while also highlighting their intrinsic limitations. By using data from both MODIS instruments on board Terra and Aqua satellites, we addressed the temporal evolution of the smoke plume while assessing lidar-specific properties and plume elevation using CALIPSO data. The analysis method in this study was developed to better differentiate between oil smoke aerosols and the local atmospheric scene. We present several aerosol properties in the form of plume-specific averaged values. We believe that MODIS values are a conservative estimation of plume aerosol optical depth (AOD) since MODIS algorithms rely on general aerosol models and various atmospheric conditions within the look-up tables, which do not reflect the highly absorbing nature of these smoke plumes. Based on this study we conclude that the MODIS land algorithms are not yet suited for retrieving aerosol properties for these types of smoke plumes due to the strong absorbing properties of these aerosols. CALIPSO retrievals rely heavily on the type of lidar solutions showing discrepancy between constrained and unconstrained retrievals. Smoke plumes identified within a larger aerosol layer were treated as unconstrained retrievals and resulted in conservative AOD estimates. Conversely, smoke plumes surrounded by clear air were identified as opaque aerosol layers and resulted in higher lidar ratios and AOD values. Measured lidar ratios and particulate depolarization ratios showed values similar to the upper ranges of biomass burning smoke. Results agree with studies that utilized ground-based retrievals, in particular for Ångström exponent (AE) and effective radius (Reff) values. MODIS and CALIPSO retrieval algorithms disagree on AOD ranges, for the most part, due to the extreme light-absorbing nature of these types of aerosols. We believe that these types of studies are a strong indicator for the need of improved aerosol models and retrieval algorithms.

A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm

Evapotranspiration (ET) is a vital part of the hydrological cycle and the water–energy balance. To explore the characteristics of five typical remote sensing evapotranspiration datasets and provide guidance for algorithm development, we used reconstructed evapotranspiration (Recon) data based on ground and GRACE satellite observations as a benchmark and evaluated five remote sensing datasets for 592 watersheds across the continental United States. The Global Land Evaporation Amsterdam Model (GLEAM) dataset (with bias and RMSE values of 23.18 mm/year and 106.10 mm/year, respectively), process-based land surface evapotranspiration/heat flux (P-LSH) dataset (bias = 22.94 mm/year and RMSE = 114.44 mm/year) and the Penman–Monteith–Leuning (PML) algorithm generated ET dataset (bias = −17.73 mm/year and RMSE = 108.97 mm/year) showed the better performance on a yearly scale, followed by the model tree ensemble (MTE) dataset (bias = 99.45 mm/year and RMSE = 141.32 mm/year) and the moderate-resolution imaging spectroradiometer (MODIS) dataset (bias = −106.71 mm/year and RMSE = 158.90 mm/year). The P-LSH dataset outperformed the other four ET datasets on a seasonal scale, especially from March to August. Both PML and MTE showed better overall accuracy and could accurately capture the spatial variability of evapotranspiration in arid regions. The P-LSH and GLEAM products were consistent with the Recon data in middle-value section. MODIS and MTE had larger bias and RMSE values on a yearly scale, whereby the MODIS and MTE datasets tended to underestimate and overestimate ET values in all the sections, respectively. In the future, the aim should be to reduce bias in the MODIS and MTE algorithms and further improve seasonality of the ET estimation in the GLEAM algorithm, while the estimation accuracy of the P-LSH and MODIS algorithms should be improved in arid regions. Our analysis suggests that combining artificial intelligence algorithms or data-driven algorithms and physical process algorithms will further improve the accuracy of ET estimation algorithms and the quality of ET datasets, as well as enhancing their capacity to be applied in different climate regions.

Comparing the accuracy of MODIS data products for vegetation detection between two environmentally dissimilar ecoregions: the Chocó-Darien of South America and the Great Basin of North America

The daily images produced by the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor aboard the Terra and Aqua satellites have been widely used to monitor global vegetation. Using these data, the Earth Observing System operated by US National Aeronautics and Space Administration (NASA) has developed a variety of MODIS products focused on the monitoring and evaluation of vegetation condition. These products have three possible sources of variation that can affect the sensitivity of vegetation detection: 1) orbital and mechanical differences between MODIS sensors aboard Aqua or Terra, 2) the preprocessing algorithms used to generate multitemporal cloud-free mosaics (MAIAC or original MODIS algorithm), and/or 3) post-processing algorithms applied by users to optimize vegetation index values derived from temporal sequences of imagery. We evaluated these sources of variation by comparing the results of a vegetation classification for two different ecoregions. The accuracies of vegetation classifications utilizing either the Aqua or Terra MODIS sensors, the MAIAC or original MODIS preprocessing algorithms, and two common post-processing techniques (Asymmetric Gaussian or Savitzky and Golay function) were compared to determine which set of techniques or sensors yielded the best results. The ecoregions we chose to use were the Great Basin of North America and Chocó-Darien of South America. We compared four different MODIS data products (MOD13Q1, MYD13Q1, MOD09Q1, and MYD09Q1) as predictor variables using Random Forest as the classification algorithm to generate a land cover map. We found that the accuracy of the vegetation classifications (using Kappa as measure of accuracy) changed significantly depending on the MODIS platform (Terra or Aqua), the preprocessing algorithm (MAIAC or MODIS), and the two postprocessing algorithms for both ecoregions. Our result suggests that comparative analyses are needed to optimize the results when equivalent MODIS products are used in vegetation detection and classification.

Aerosol Optical Depth from MODIS satellite data above the Pierre Auger Observatory

Aerosol optical depth can be retrieved from measurements performed by Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument. The MODIS satellite system includes two polar satellites, Terra and Aqua. Each of them flies over the Pierre Auger Observatory once a day, providing two measurements of aerosols per day and covering the whole area of the Observatory. MODIS aerosol data products have been generated by three dedicated algorithms over bright and dark land and over ocean surface. We choose the Deep Blue algorithm data to investigate the distribution of aerosols over the Observatory, as this algorithm is the most appropriate one for semi-arid land of the Pierre Auger Observatory. This data algorithm allows us to obtain aerosol optical depth values for the investigated region, and to build cloud-free aerosol maps with a horizontal resolution 0.1°×0.1°. Since a suffcient number of measurements was obtained only for Loma Amarilla and Coihueco fluorescence detector (FD) sites of the Pierre Auger Observatory, a more detailed analysis of aerosol distributions is provided for these sites. Aerosols over these FD sites are generally distributed in a similar way each year, but some anomalies are also observed. These anomalies in aerosol distributions appear mainly due to some transient events, such as volcanic ash clouds, fires etc. We conclude that the Deep Blue MODIS algorithm provides more realistic aerosol optical depth values than other available algorithms.

An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness. Part 2: Application and validation in Asia

Since small aerosol particles are mostly anthropogenic, the fine-mode aerosol optical thickness (fAOT) can be used to infer PM2.5 amounts. However, satellite-based fAOT products such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS) are highly uncertain over land. An improved fAOT retrieval method called the look-up table–spectral deconvolution algorithm (LUT-SDA) was tested and improved using data from Asia. The improvement is achieved by accounting for seasonal changes instead of using constant annual mean values of the aerosol parameters used in the LUT-SDA derived from the Aerosol Robotic Network (AERONET) data from 2010 to 2014. Compared with the previous version of the LUT-SDA developed for Beijing, Hong Kong, and Osaka, the updated LUT-SDA generates more accurate fine-mode fractions (FMFs) with the total mean root-mean-square error (RMSE) decreasing from 0.24 to 0.18. The updated LUT-SDA was then applied to retrieve fAOT and was validated by retrievals from 45 AERONET sites over the period 2015 to 2016. A good accuracy was achieved by this method with 31% of the validation sites having >50% of retrievals falling within the estimated error (EE) envelope ± (0.05 + 0.15 × AERONET fAOT) and 42% of the validation sites having 40–50% of retrievals falling within the EE envelope. In the total validation and comparison with the MODIS Collection 6 fAOT, the fAOT retrievals from the LUT-SDA agreed more closely with AERONET retrievals, showing a low bias. About 48% of the LUT-SDA-based fAOT retrievals fell within the EE envelope (RMSE = 0.29), while ~22% of the MODIS-based fAOT retrievals fell within the EE envelope (RMSE = 0.42). The fAOT was significantly underestimated by the MODIS algorithm in most areas of Asia with many values of zero. This study demonstrates that the refined LUT-SDA method is valid for the large-scale estimation of fAOT from satellite images.

First assessment of MODIS satellite ocean color products (OC3 and nFLH) in the Inner Sea of Chiloe, northern Patagonia

The use of remote sensing has allowed enormous progress in our understanding of biophysical processes worldwide. Despite their importance, the use of satellite bio-optical products is still limited due to optical complexity. In this study we assess the performance of the Moderate Resolution Imaging Spectroradiometer algorithm for chlorophyll-a (MODIS-OC3) and of normalized Fluorescence Line Height (nFLH), in inner waters of northern Chilean Patagonia (41°-45oS). Satellite data were evaluated using a compilation of in situ chlorophyll-a data collected by the CIMAR-FIORDOS program during 2003-2012. During austral spring MODIS-OC3 showed a significant relationship with in situ measurements (R2 = 0.2, P = 0.03, F = 5.33) in comparison with winter when the relationship was non-significant. In contrast, nFLH explained a significant fraction of observed variance of Chl-a during austral winter (R2 = 0.54, P = <0.01, F = 10.68) and this relationship was not significant in spring. Our preliminary results offer an excellent chance to understand patterns of variability of the autotrophic biomass and physiological status in optically complex interior marine ecosystems.

How Do Aerosol Properties Affect the Temporal Variation of MODIS AOD Bias in Eastern China?

The rapid changes of aerosol sources in eastern China during recent decades could bring considerable uncertainties for satellite retrieval algorithms that assume little spatiotemporal variation in aerosol single scattering properties (such as single scattering albedo (SSA) and the size distribution for fine-mode and coarse mode aerosols) in East Asia. Here, using ground-based observations in six AERONET sites, we characterize typical aerosol optical properties (including their spatiotemporal variation) in eastern China, and evaluate their impacts on Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol retrieval bias. Both the SSA and fine-mode particle sizes increase from northern to southern China in winter, reflecting the effect of relative humidity on particle size. The SSA is ~0.95 in summer regardless of the AEROENT stations in eastern China, but decreases to 0.85 in polluted winter in northern China. The dominance of larger and highly scattering fine-mode particles in summer also leads to the weakest phase function in the backscattering direction. By focusing on the analysis of high aerosol optical depth (AOD) (&gt;0.4) conditions, we find that the overestimation of the AOD in Dark Target (DT) retrieval is prevalent throughout the whole year, with the bias decreasing from northern China, characterized by a mixture of fine and coarse (dust) particles, to southern China, which is dominated by fine particles. In contrast, Deep Blue (DB) retrieval tends to overestimate the AOD only in fall and winter, and underestimates it in spring and summer. While the retrievals from both the DT and DB algorithms show a reasonable estimation of the fine-mode fraction of AOD, the retrieval bias cannot be attributed to the bias in the prescribed SSA alone, and is more due to the bias in the prescribed scattering phase function (or aerosol size distribution) in both algorithms. In addition, a large yearly change in aerosol single scattering properties leads to correspondingly obvious variations in the time series of MODIS AOD bias. Our results reveal that the aerosol single scattering properties in the MODIS algorithm are insufficient to describe a large variation of aerosol properties in eastern China (especially change of particle size), and can be further improved by using newer AERONET data.

Assessment of satellite-based chlorophyll-a retrieval algorithms for high solar zenith angle conditions

Numerous empirical algorithms have been operationally used to retrieve global ocean chlorophyll-a (Chla) concentrations from ocean color satellite data, for example, the OC4V4 algorithm for sea-viewing wide field-of-view sensor and the OC3M algorithm for moderate-resolution imaging spectroradiometer. However, the algorithms have been established and validated based on in situ data mainly measured under low to moderate solar zenith angle (SZA) (<70 deg). Currently, with the development of geostationary satellite ocean color remote sensing, enabling observations from early morning to late afternoon, it has become necessary to know whether the empirical Chla algorithms can be applied to high SZA. The performances of seven widely used Chla algorithms (i.e., OC2, OC3M, OC3V, OC4V4, Clark, ocean-color index, and Yellow Sea Large Marine Ecosystem Ocean Color Work Group) under high SZAs were evaluated using the global in situ ocean color dataset (NASA bio-optical marine algorithm dataset). The results show that the performances of all seven algorithms decreased significantly under high SZAs compared with those under low to moderate SZAs. For instance, for the OC4V4 algorithm, the relative percent difference (RPD) and root-mean-square error (RMSE) were 13.78% and 1.66 μg/L for the whole dataset and 3.95% and 1.49 μg/L for SZAs ranging from 30 deg to 40 deg, respectively. However, RPD and RMSE values increased to 30.45% and 6.10 μg/L for SZAs larger than 70 deg. Adjusting the coefficients of the algorithms using the in situ dataset with high SZA can only slightly improve the performance. The bidirectional remote sensing reflectance can explain the underestimation of the retrieved Chla under high SZA, and the low sensitivity of the blue–green ratio is responsible for the relatively larger scatter between the retrieved and the in situ Chla under high SZA.

The Sensitivity of AOD Retrieval to Aerosol Type and Vertical Distribution over Land with MODIS Data

This study is to evaluate the sensitivity of Aerosol Optical Depth (AOD τ) to aerosol vertical profile and type, using the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 algorithm over land. Four experiments were performed, using different aerosol properties including 3 possible non-dust aerosol models and 14 vertical distributions. The algorithm intrinsic uncertainty was investigated as well as the interplay effect of aerosol vertical profile and type on the retrieval. The results show that the AOD retrieval is highly sensitive to aerosol vertical profile and type. With 4 aerosol vertical distributions, the algorithm with a fixed vertical distribution gives about 5% error in the AOD retrieval with aerosol loading τ ≤ 0 . 5 . With pure aerosols (smoke and dust), the retrieval of AOD shows errors ranging from 2% to 30% for a series of vertical distributions. Errors in aerosol type assumption in the algorithm can lead to errors of up to 8% in the AOD retrieval. The interplay effect can give the AOD retrieval errors by over 6%. In addition, intrinsic algorithm errors were found, with a value of &gt;3% when τ&gt; 3.0. This is due to the incorrect estimation of the surface reflectance. The results suggest that the MODIS algorithm can be improved by considering a realistic aerosol model and its vertical profile, and even further improved by reducing the algorithm intrinsic errors.

Validation of MODIS Aerosol Optical Depth Retrieval over Mountains in Central China Based on a Sun-Sky Radiometer Site of SONET

The 3 km Dark Target (DT) aerosol optical depth (AOD) products, 10 km DT and Deep Blue (DB) AOD products from the Collection 6 (C6) product data of Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with Sun-sky Radiometer Network (SONET) measurements at Song Mountain in central China, where ground-based remote sensing measurements of aerosol properties are still very limited. The seasonal variations of AODs are significant in the Song Mountain region, with higher AODs in spring and summer and lower AODs in autumn and winter. Annual mean AODs (0.55 µm) vary in the range of 0.5–0.7, which indicates particle matter (PM) pollutions in this mountain region. Validation against one-year ground-based measurements shows that AOD retrievals from the MODIS onboard Aqua satellite are better than those from the Terra satellite in Song Mountain. The 3 km and 10 km AODs from DT algorithms are comparable over this region, while the AOD accuracy of DB algorithm is relatively lower. However, the spatial coverage of DB products is higher than that of 10 km DT products. Moreover, the optical and microphysical characteristics of aerosols at Song Mountain are analyzed on the basis of SONET observations. It suggests that coarse-mode aerosol particles dominate in spring, and fine-mode particles dominate in summer. The aerosol property models are also established and compared to aerosol types used by MODIS algorithm.

북반구 해빙 지역에서 세 종류 위성관측 표면온도에 대한 오차분석

We investigated the relative errors of satellite-observed Surface Skin Temperature (SST) data caused by sea ice in the northern hemispheric ocean () during April 16-24, 2003-2014 by intercomparing MODerate Resolution Imaging Spectroradiometer (MODIS) Ice Surface Temperature (IST) data with two types of Atmospheric Infrared Sounder (AIRS) SST data including one with the AIRS/Advanced Microwave Sounding Unit-A (AMSU) and the other with `AIRS only`. The MODIS temperatures, compared to the AIRS/AMSU, were systematically up to ~1.6 K high near the sea ice boundaries but up to ~2 K low in the sea ice regions. The main reason of the difference of skin temperatures is that the MODIS algorithm used infrared channels for the sea ice detection (i.e., surface classification), while microwave channels were additionally utilized in the AIRS/AMSU. The `AIRS only` algorithm has been developed from NASA`s Goddard Space Flight Center (NASA/GSFC) to prepare for the degradation of AMSU-A by revising part of the AIRS/AMSU algorithm. The SST of `AIRS only` compared to AIRS/AMSU showed a bias of 0.13 K with RMSE of 0.55 K over the region. The difference between AIRS/AMSU and `AIRS only` was larger over the sea ice boundary than in other regions because the `AIRS only` algorithm utilized the GCM temperature product (NOAA Global Forecast System) over seasonally-varying frozen oceans instead of the AMSU microwave data. Three kinds of the skin temperatures consistently showed significant warming trends () in the latitude band of . The systematic disagreement among the skin temperatures could affect the discrepancies of their trends in the same direction of either warming or cooling.

Evaluating Parameter Adjustment in the MODIS Gross Primary Production Algorithm Based on Eddy Covariance Tower Measurements

How well parameterization will improve gross primary production (GPP) estimation using the MODerate-resolution Imaging Spectroradiometer (MODIS) algorithm has been rarely investigated. We adjusted the parameters in the algorithm for 21 selected eddy-covariance flux towers which represented nine typical plant functional types (PFTs). We then compared these estimates of the MOD17A2 product, by the MODIS algorithm with default parameters in the Biome Property Look-Up Table, and by a two-leaf Farquhar model. The results indicate that optimizing the maximum light use efficiency (εmax) in the algorithm would improve GPP estimation, especially for deciduous vegetation, though it could not compensate the underestimation during summer caused by the one-leaf upscaling strategy. Adding the soil water factor to the algorithm would not significantly affect performance, but it could make the adjusted εmax more robust for sites with the same PFT and among different PFTs. Even with adjusted parameters, both one-leaf and two-leaf models would not capture seasonally photosynthetic dynamics, thereby we suggest that further improvement in GPP estimaiton is required by taking into consideration seasonal variations of the key parameters and variables.

Comparison of AOD between CALIPSO and MODIS: significant differences over major dust and biomass burning regions

Abstract. Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) provide global vertical profiles of aerosol optical properties for the first time. In this study, we employed about 6 yr (2006–2011) of CALIPSO level 3 monthly mean gridded aerosol optical depth (AOD) products (daytime and nighttime) for cloud-free conditions, to compare with the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua level 3 monthly mean AOD dataset for the same time period. While the spatial distribution and seasonal variability of CALIPSO AOD is generally consistent with that of MODIS, CALIPSO is overall lower than MODIS as MODIS has higher frequency than CALIPSO for most bins of AOD. The correlation between MODIS and CALIPSO is better over ocean than over land. We focused on four regions that have large systematic differences: two over dust regions (the Sahara and Northwest China) and two over biomass burning regions (South Africa and South America). It is found that CALIPSO AOD is significantly lower than MODIS AOD over dust regions during the whole time period, with a maximum difference of 0.3 over the Saharan region and 0.25 over Northwest China. For biomass burning regions, CALIPSO AOD is significantly higher than MODIS AOD over South Africa, with a maximum difference of 0.25. Additionally CALIPSO AOD is slightly higher than MODIS AOD over South America for most of the time period, with a few exceptions in 2006, 2007, and 2010, when biomass burning is significantly stronger than during other years. We analyzed the impact of the satellite spatial and temporal sampling issue by using level 2 CALIPSO and MODIS products, and these systematic differences can still be found. The results of this study indicate that systematic differences of CALIPSO relative to MODIS are closely associated with aerosol types, which vary by location and season. Large differences over dust and biomass burning regions may suggest that assumptions made in satellite retrievals, such as the assumed lidar ratios for CALIPSO retrievals over dust and biomass burning regions or the surface reflectance information and/or the aerosol model utilized by the MODIS algorithm, are not appropriate.

Comparison of Aerosol Products Retrieved from AERONET, MICROTOPS and MODIS over a Tropical Urban City, Pune, India

Aerosol Optical Depth (AOD) measurements from Aerosol Robotic NETwork (AERONET; level 2.0), Microtops - II sun-photometer and MODerate Resolution Imaging Spectroradiometer (MODIS) (Terra and Aqua; level 2, collection 5, dark target) were compared and used to characterize aerosols over Pune, India. AODs from Microtops and MODIS were compared with those measured by AERONET to evaluate the measurement quality. To the best of our knowledge, this is the first systematic comparison of MODIS aerosol products over Pune, India. The results of the analysis show that during 2008-10, 68% to 84% of the MODIS AODs fell within an expected error, as defined by the MODIS science team, and thus the retrievals from this system are validated and accepted. In addition, during pre-monsoon periods MODIS retrievals are better-matched with ground-based measurements. On the seasonal scale, MODIS retrievals corroborate well with ground-based measurements, with correlation coefficients ranging from 0.62 to 0.93. Despite an overall satellite-ground agreement, MODIS tends to under-estimate AOD during winter, and this may be due to improper assumptions of surface reflectance and the incorrect selection of aerosol types. AERONET retrieved single scattering albedo (SSA) values in winter (0.82-0.86), suggesting the dominance of absorbing aerosols, slightly increased (0.87-0.89) in pre-monsoon season, indicating more scattering type of aerosols. These values are about 8.9%-1.1% lower than those of the assumed SSA values in the MODIS algorithm.

A simple empirical method for estimating surface water vapour pressure using MODIS near-infrared channels: applications to northern Spain's Asturias region

In this article, we present a simple methodology for obtaining algorithms to estimate surface water vapour pressure (e 0) over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The algorithm obtained in this case is adapted to the particular climatic characteristics of the Asturias region, but the methodology can easily be extrapolated and used to obtain algorithms for other regions around the world. The proposed method estimates e 0 from a simple linear combination of the radiances of the MODIS near-infrared (NIR) channels more commonly applied to total precipitable water (W) estimations. Comparison between the e 0 data measured at the ground-based meteorological stations in Asturias (daily data from 2004) versus the values predicted using the proposed algorithm gives R 2 = 0.76 and residual standard error (RSE) = 2.07 hPa (16%). The algorithm was tested using the data from 2008 obtained in Asturias and in two sites outside of Asturias with similar latitudes and radiosonde observations (La Coruña and Santander). The resulting validation demonstrates that the algorithm gives good results in Asturias (root-mean-square deviation (RMSD) = 2.50 hPa (19%) and bias = 1.26 hPa, with R 2 = 0.65) and when La Coruña is included (R 2 = 0.61), but that its validity is decreased when Santander is also included (R 2 = 0.56). The possibility of obtaining e 0 from three global MODIS algorithms for W retrieval was also tested and compared to our algorithm. The results show that our algorithm gives better results than the International MODIS/Atmospheric InfraRed Sounder Processing Package (IMAPP) Water Vapour Near-Infrared (WVNIR) product and the Sobrino algorithm. The MODIS Total Precipitable Water (MOD05) product is worse than that obtained with our algorithm in Asturias (R 2 = 0.61 vs. R 2 = 0.65), but the two values are similar if the stations in La Coruña (R 2 = 0.60) and Santander (R 2 = 0.56) are included in the comparison. The dominant advantage of the novel algorithm proposed in this study is that it is simpler and can be produced quickly in real time.

Validation of MODIS retrieval aerosol optical depth and an investigation of aerosol transport over Mohal in north western Indian Himalaya

This study deals with the optical properties of aerosols during 2007 over Mohal (31.9º N, 77.12º E) in north western Indian Himalaya, investigated using ground-based measurements and multi-satellite data. The daily average (mean ± standard deviation) aerosol optical depth (AOD) at 500 nm, Ångström exponent and turbidity coefficient values were 0.2 ± 0.1, 1.1 ± 0.3 and 0.1 ± 0.1, respectively. About 84% of AOD values retrieved from satellites were found to be within an uncertainty limit with a significant correlation coefficient around 0.70. The present study suggests that AOD retrieval using the Moderate Resolution Imaging Spectroradiometer (MODIS) is able to characterize AOD distribution over Mohal. However, to eliminate systematic errors, the existing MODIS algorithm needs to be modified in view of the changing aerosol optical properties, especially during the biomass-burning period. To investigate the influence of aerosol transport, a multi-sensor approach in conjunction with back-trajectory analysis was used. The observed higher values of AOD during dust-loading days with simultaneous study of the space-borne lidar measurements as well as back-trajectory analysis suggest the influx of desert aerosols. Transport of dust aerosols on 12 April, 27 April and 1 June caused a significant reduction in surface-reaching solar irradiance by 43, 40 and 39 W m–2, respectively. Atmospheric forcing during these days increased by 33.8, 33.0 and 33.2 W m–2, which translates into atmospheric heating rates of 0.95, 0.93 and 0.93 K day–1, respectively. This indicates significant climatic implications due to arriving aerosols in north-western Indian Himalaya.

Litter carbon inputs to the mineral soil of Japanese Brown forest soils: comparing estimates from the RothC model with estimates from MODIS

The level of organic carbon found in soil is the result of the balance between litter input to the soil and decomposition. Litter input to the soil is closely related to net primary production (NPP); at equilibrium, the NPP is equal to the litter input to soil. Plant litter input to a depth of 30 cm in the mineral soil was estimated for Japanese forest using the Rothamsted Carbon model (RothC) and an average value of soil organic carbon (SOC) content, and was compared with estimated litter inputs from the NPP dataset from Moderate Resolution Imaging Spectroradiometer (MODIS). A Monte Carlo uncertainty analysis of the input SOC was also conducted in order to reveal the sensitivity and uncertainty of the model to input SOC. The litter carbon input calculated using RothC and that derived from MODIS NPP were positively correlated, but the mean estimated litter input from RothC was 17.2% smaller than that estimated from MODIS. Mapping the normalized difference revealed spatial biases in the difference. The discrepancy was probably because of the different temperature controls for the MODIS algorithm and the RothC model, and also our simple assumption in the RothC calculation. This comparison reveals a close link between litter inputs estimated from SOC data and litter inputs estimated from satellite-based NPP data. The discrepancies between the estimates merit further study.

Site-level evaluation of MODIS-based primary production in an old-growth forest in Northeast China

To improve the accuracy of the moderate resolution imaging spectroradiometer (MODIS) gross primary production (GPP) algorithm, it is critical to evaluate MODIS GPP production for different land cover types using ground-based measurements. In this paper the MODIS primary production products (MOD17) is evaluated by using site-specific input parameters to the algorithm and compared to the results of eddy covariance measurements over an old-growth forest. Direct comparisons suggest that 8-day GPP predicted by the standard MODIS algorithm was highly correlated with flux tower based GPP (r2 = 0.77, P<0.001), while with an average underestimation of 39%. The difference is substantial in magnitude mainly because the inputs of underestimated MODIS biome-specific parameters, maximum light use efficiency emax and MODIS derived fraction of photosynthetically active radiation. The modified MODIS algorithm GPP calculated with site-specific input parameters compares favorably with ground flux tower observations (r2 = 0.92, relative error = 7%). These results suggest that the MODIS GPP production is most likely underpredicted in forest sites with high primary production, and site-specific input parameters could help to improve the accuracy of MODIS GPP algorithm.

Evaluation of Collections 4 and 5 of the MODIS Gross Primary Productivity product and algorithm improvement at a tropical savanna site in northern Australia

In this study, we assessed the accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) GPP (gross primary productivity) Collections 4.5, 4.8 and 5 along with Leaf Area Index (LAI), fraction of absorbed Photosynthetically Active Radiation (fPAR), light use efficiency (LUE) and meteorological variables that are used to estimate GPP for a northern Australian savanna site. Results of this study indicated that the MODIS products captured the seasonal variation in GPP, LAI and fPAR well. Using the index of agreement (IOA), it was found that Collections 4.5 and 4.8 (IOA 0.89 respectively) agreed reasonably well with flux tower measurements between 2001 and 2006. It was also found that MODIS Collection 4.5 predicted the dry season GPP well (Relative Predictive Error (RPE) 4.17%, IOA 0.72 and Root Mean Square Error (RMSE) of 1.05 g C m − 2 day − 1 ), whilst Collection 4.8 performed better in capturing wet season dynamics (RPE 1.11%, IOA 0.80 and RMSE of 0.91 g C m − 2 day − 1 ). Although the wet season magnitude of GPP was predicted well by Collection 4.8, an examination of the inputs to the GPP algorithm revealed that MODIS fPAR was too high, but this was compensated by PAR and LUE that was too low. Although LAI and fPAR estimated by Collection 5 were more accurate, GPP for this Collection resulted in a much lower value (RPE 25%) due to errors in other factors. Recalculation of MODIS GPP using site specific input parameters indicated that MODIS fPAR was the main reason for the differences between MODIS and tower derived GPP followed by LUE and meteorological inputs. GPP calculated using all site specific values agreed very well with tower data on an annual basis (IOA 0.94, RPE 6.06% and RMSE 0.83 g C m − 2 day − 1 ) but the early initiation of the growing season calculated by the MODIS algorithm was improved when the vapor pressure deficit (VPD) function was replaced with a soil water deficit function. The results of this study however, reinforce previous findings in water limited regions, like Australia, and incorporation of soil moisture in a LUE model is needed to accurately estimate the productivity.