A method for compositing MODIS satellite images to remove cloud cover

The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI–LST, EVI–LST, NDVI–LST–NDWI and EVI–LST–NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.

Satellite aerosol remote sensing entered a new era with the deployment of advanced satellite imaging instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Terra and Aqua satellites. These new instruments provide the opportunity to learn more about aerosol properties than was possible using the simpler NOAA Advanced Very High Resolution Radiometer (AVHRR), which has been used to retrieve aerosol optical thickness for more than 20 years. Combining historical AVHRR and the more advanced MODIS aerosol retrievals to form a long‐term aerosol data record is critical for studying aerosol climate forcing. To achieve this objective, it is necessary to build a connection and establish consistency between the two retrievals through a careful evaluation of the two retrieval methods applied to the same data. As a first step in this effort, this paper exploits the potential of the Clouds and the Earth's Radiant Energy System (CERES) Single‐Scanner Footprint (SSF) data set that includes aerosol products derived from Terra MODIS data at the same locations using both the multichannel MODIS and the two‐channel AVHRR aerosol retrieval algorithms. The analysis examines the differences in the results seen over oceans on a global scale. It was found in a global mean sense that advancement in the aerosol retrieval over ocean from the MODIS algorithm relative to the AVHRR method is realized mostly in the improvement of the aerosol size parameter (ASP) rather than in the aerosol optical thickness (AOT). However, regional differences were observed in both AOT and ASP retrieved from the MODIS and AVHRR algorithms. These are examined further in the second part of this two‐part paper. Cloud contamination and surface roughness appear to affect both aerosol retrievals, effects that need further investigation.

Vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI) are widely used for assessing vegetation cover and condition. One of the NDVI's significant disadvantages is its sensitivity to aerosols in the atmosphere, hence several atmospherically resistant VIs were formulated using the difference in the radiance between the blue and the red spectral bands. The state‐of‐the‐art atmospherically resistant VI, which is a standard Moderate Resolution Imaging Spectroradiometer (MODIS) product, together with the NDVI, is the Enhanced Vegetation Index (EVI). A different approach introduced the Aerosol‐free Vegetation Index (AFRI) that is based on the correlation between the shortwave infrared (SWIR) and the visible red bands. The AFRI main advantage is in penetrating an opaque atmosphere influenced by biomass burning smoke, without the need for explicit correction for the aerosol effect. The objective of this research was to compare the performance of these three VIs under smoke conditions. The AFRI was applied to the 2.1 µm SWIR channel of the MODIS sensor onboard the Earth Observing System (EOS) Terra and Aqua satellites in order to assess its functionality on these imaging platforms. The AFRI performance was compared with those of NDVI and EVI. All VIs were calculated on images with and without present smoke, using the surface‐reflectance MODIS product, for three case studies of fires in Arizona, California, and Zambia. The MODIS Fire Product was embedded on the images in order to identify the exact location of the active fires. Although good correlations were observed between all VIs in the absence of smoke (in the Arizona case R 2 = 0.86, 0.77, 0.88 for the NDVI–EVI, AFRI–EVI, and AFRI–NDVI, respectively) under smoke conditions a high correlation was maintained between the NDVI and the EVI, while low correlations were found for the AFRI–EVI and AFRI–NDVI (0.21 and 0.16, for the Arizona case, respectively). A time series of MODIS images recorded over Zambia during the summer of 2000 was tested and showed high NDVI fluctuations during the study period due to oscillations in aerosol optical thickness values despite application of aerosol corrections on the images. In contrast, the AFRI showed smoother variations and managed to better assess the vegetation condition. It is concluded that, beneath the biomass burning smoke, the AFRI is more effective than the EVI in observing the vegetation conditions.

The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua satellites conducts continuous monitoring of the Earth's land surface and oceans. Recently, a sharp discontinuity (averaging 1.9°C) has been noticed at 60° N in both MODIS daytime and night-time land surface temperature (LST) products. This linear artefact arises because the CO2 high cloud test in the operational code for the generation of the MODIS cloud mask product is used only in the non-polar region (between 60° N and 60° S). The resulting discontinuity clearly has negative implications for any statistical applications of these temperature data. In this technical note we present a new algorithm, which minimizes this discontinuity. The method uses edge detection and elimination based on a mixture of Sobel and non-linear Laplacian filters (edge detection and quantification), cubic splines (edge modelling), and a controllable power function for image restoration. The implementation of this algorithm is demonstrated on an image of average minimum night-time LST between 2001 and 2008.

This paper presents a method of validating the MODIS (Moderate-Resolution Imaging Spectroradiometer) surface reflectance (MOD09) based on ground spectral measurements and high-resolution remote sensing images. Given that the spatial resolution of the MODIS image is too low to directly compare ground measurements with the MODIS pixels, registration is first made between MODIS and a high-resolution image. Afterwards, the same sizable sample area to one MODIS pixel is chosen in a high resolution image (a pixel in MODIS corresponds to many pixels in a high-resolution image), after which image classification is conducted in each sample area. Finally, reflectance is measured for each class in each area. Taking the ratio of every class area to each sample area as the weight, the weighted mean reflectance is then calculated for each sample area (a pixel for MODIS), which is treated as the measured reflectance of the corresponding pixel in the MODIS image. A contradistinctive analysis is carried out between measured reflectance and MODIS retrieval reflectance from the space in order to determine the accuracy of the MODIS land surface reflectance product and correct its error. The experiment indicates that a linear positive correlativity between the MODIS pixel retrieval reflectance from space and the measured (calculated) surface reflectance exists. From this, we can build an error correction model of the MODIS surface reflectance product by linear regression. The accuracy of the MODIS surface reflectance products after error correction is remarkably improved. Likewise, the relative error can be controlled by 10%, thereby satisfying the requirements of various remote sensing applications.

Knowledge of the variation and regulation of the diffuse attenuation coefficient is important for management of lake water quality and ecological environments. Data were collected seven times (378 samples) from 2006 to 2011 in Lake Taihu to improve the existing semianalytical model of diffuse attenuation coefficient at 490 nm (Kd(490)) by measuring in situ Kd(490) and remote sensing reflectance (Rrs). This semianalytical model was applied to determine the long‐term variation of Kd(490) and its driving factors from Moderate Resolution Imaging Spectroradiometer (MODIS) images. Our semianalytical model of Kd(490) improves the retrieval accuracy of Kd(490) from 29.60% to 22.58% (root‐mean‐square percentage error) and 24.28 ± 18.54% (mean value ± standard deviation) to 19.29 ± 14.34% (mean absolute relative error). The Kd(490) from maps produced from MODIS images clearly present the temporal and spatial variations of Kd(490). This indicates the suitability of using the improved semianalytical model and MODIS to observe Kd(490) in turbid shallow inland waters. Long‐term MODIS observation of Kd(490) shows distinct seasonal and spatial variations. Temporally, the highest and the lowest Kd(490) were found in winter and in summer, respectively. Spatially, high Kd(490) values were primarily distributed in the southwestern and northwestern Lake Taihu, and relatively low Kd(490) values were distributed in Meiliang Bay and Gonghu Bay (the eastern part of the lake and East Bay were excluded in this study due to invalid retrieval results of Kd(490)). Many covarying points were found between Kd(490) and maximum wind speed from long‐term observations, suggesting that wind‐induced sediment resuspension is the principal driver of high Kd(490). Correlation was also observed between Kd(490) and heavy rainfall events, suggesting the regulating effect of heavy rainfall.

First results of operational retrieval of atmospheric ozone profile from the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared radiances over Siberian Region are presented. Nearly identical copies of MODIS are operating onboard the Earth Observing System (EOS) Tena and Aqua satellites. MODIS images have been collected using EOScan receiving station located in Barnaul. The modified Product Generation Executable (PGE) 03 code, including synthetic regression retrieval algorithm, PGE 02 and the International MODIS/AIRS Processing Package (IMAPP) have been used to retrieve the vertical ozone distributions under clear-sky conditions. Evaluation of retrieved ozone profiles is performed by a comparison with retrievals from lidar observations, the Stratospheric Aerosol and Gas Experiment III (SAGE 111)/Meteor-3M and the Atmospheric Infrared Sounder (AIRS)/Aqua sensors. We demonstrate, that the MODIS mixing ozone ratio (in ppmv) and additionally measured temperature information will be useful tools in regional stratospheric ozone studies. The main role of MODIS will be the monitoring of the ozone trends in the stratosphere at the scale with 5 km x 5 km resolutions.

In north India, agricultural burning adversely affects local and regional air quality during the post-monsoon season (October to November), when the prevailing meteorology is favorable for smog and haze formation. Quantifying the contribution of smoke to air pollution in this region, however, is challenging. While the Moderate Resolution Imaging Spectroradiometer (MODIS), aboard NASA's Terra and Aqua satellites, provides a nearly 20-year record of global fire activity, the sensor cannot adequately capture small, short-lasting agricultural fires due to its moderate spatial resolution (500 m–1 km) and limited overpasses (twice daily for each satellite), as well as the hazy conditions that typically obscure the north India land surface at this time of year. Moreover, current global fire emissions inventories based on MODIS observations can differ by up to an order of magnitude in this region. Here we incorporate household survey data to bridge gaps in MODIS observations and improve estimates of fire emissions over the states of Punjab, Haryana, Uttar Pradesh, and Bihar during the 2003–2018 post-monsoon burning seasons. We develop a novel method that adjusts MODIS Fire Radiative Power (FRP) for: (1) small fires detected by the Visible Infrared Imaging Radiometer Suite (VIIRS) at 375-m spatial resolution, (2) cloud/haze gaps in satellite observations, (3) partial-field burning practices, and (4) the diurnal cycle of fire activity. Adjusting FRP for the fire diurnal cycle yields the largest boost to emissions due to the short lifetime of the fires (~1/2 h) and the brief windows of satellite detection. Using adjusted FRP, we estimate on average 10.4 ± 3 Tg dry matter (DM) burned each year, yielding emissions of 65 ± 18 Gg organic carbon (OC), 5.6 ± 1.6 Gg black carbon (BC), 791 ± 225 Gg CO, and 14.9 ± 4.2 Tg CO2. On average, our OC + BC emissions are 3.4 times (min: 0.6, max: 6.6) the estimates from five widely used global fire emissions inventories. Our estimate for Punjab, which contributes more than two-thirds of emissions in the region, is consistent with our bottom-up validation, which uses burn rates from the household survey and government crop production statistics in 2016 and 2017. We spatially disaggregate the state-level emissions to construct a gridded inventory at daily, 0.25 ° × 0.25 ° resolution over north India from 2003 to 2018. The inventory, SAGE-IGP (Survey Constraints on FRP-based Agricultural Fire Emissions in the Indo-Gangetic Plain), improves modeling assessments of air quality impacts from agricultural burning, thus supporting effective policy development.

We used publicly available digital spatial datasets to study the area extents and their horizontal variations of two water bodies within the Danjiangkou Reservoir, China. Between 2003 and 2005, the water levels varied from 140 to 149 m above mean sea level as measured by the Geoscience Laser Altimeter System (GLAS). Detailed procedures to derive the horizontal extents and variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) coupled with GLAS data and to verify the extents and variations spatially were provided. For the water bodies on the north and west, the surface water extents derived from four MODIS images varied between 174 and 218 km2 and from 96 to 135 km2, respectively. The extents by inundating the DEM using the GLAS data were 178–212 km2 for the water body on the north and 104–118 km2 for the water body on the west. The spatial verifications of surface water extents derived from the MODIS images versus DEM coupled with GLAS data agreed 83–93%. Within the ring areas between water/land boundaries at elevations of 140 and 147 m, and 140 and 149 m, the spatial agreement was 52–75%.

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua platforms provides unique measurements for deriving global and regional cloud properties. MODIS has spectral coverage combined with spatial resolution in key atmospheric bands, which is not available on previous imagers and sounders. This increased spectral coverage/spatial resolution, along with improved onboard calibration, enhances the capability for global cloud property retrievals. MODIS operational cloud products are derived globally at spatial resolutions of 5 km (referred to as level-2 products) and are aggregated to a 1° equal-angle grid (referred to as level-3 product), available for daily, 8-day, and monthly time periods. The MODIS cloud algorithm produces cloud-top pressures that are found to be within 50 hPa of lidar determinations in single-layer cloud situations. In multilayer clouds, where the upper-layer cloud is semitransparent, the MODIS cloud pressure is representative of the radiative mean between the two cloud layers. In atmospheres prone to temperature inversions, the MODIS cloud algorithm places the cloud above the inversion and hence is as much as 200 hPa off its true location. The wealth of new information available from the MODIS operational cloud products offers the promise of improved cloud climatologies. This paper 1) describes the cloud-top pressure and amount algorithm that has evolved through collection 5 as experience has been gained with in-flight data from NASA Terra and Aqua platforms; 2) compares the MODIS cloud-top pressures, converted to cloud-top heights, with similar measurements from airborne and space-based lidars; and 3) introduces global maps of MODIS and High Resolution Infrared Sounder (HIRS) cloud-top products.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is an important data set in global burned-area mapping. The MODIS global burned-area product has a coarse spatial resolution at approximately 500 m, which often introduces errors to the mapped burned areas. In this letter, a novel subpixel mapping (SPM) approach was proposed to produce burned-area maps at the fine spatial resolution similar to Landsat imagery, by exploring the spectral and spatial information provided by the second and fifth bands of MODIS. The proposed SPM approach aims to refine the estimate of burned areas, which have been detected by the MODIS global burned-area product. The performance of the proposed SPM approach was assessed with an experiment area containing six burned areas, by comparing with the MODIS burned-area product MCD45. The result shows that the average omission error decreased from 52.26% for MCD45 to 16.74% for SPM, and the average commission error decreased from 21.76% for MCD45 to 12.54% for SPM. The kappa value increased from 0.5583 for MCD45 to 0.8756 for SPM, indicating that the proposed SPM approach is effective in reducing the influence of the coarse spatial resolution of MODIS imagery in mapping a burned area and refining existing global burned-area products.

Atmospheric aerosols affect the weather and climate by changing cloud formation and the energy balance and, depending on their type and concentration, can negatively affect air quality. Important atmospheric aerosols include dust, ash, volcanic sulfate aerosols, sea salt, biogenic particles, urban/industrial pollution, and smoke. For more than a decade, the twin Moderate Resolution Imaging Spectroradiometers (MODIS) aboard NASA's Aqua and Terra satellites have provided regular global assessments of aerosol loading, and now, following its 2011 launch, the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar‐orbiting Partnership (Suomi‐NPP) satellite is ready to contribute to that assessment.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is an Earth-observing sensor currently operational on the Earth Observing System (EOS) Terra and Aqua satellites. Each MODIS instrument has 36 spectral bands, with data acquired using 490 detectors in the reflective solar and thermal emissive spectrum. The 20 reflective solar bands (RSB), covering a wavelength range from 0.4 to 2.2 μm, are calibrated on-orbit using a solar diffuser (SD), solar diffuser stability monitor (SDSM) and regularly scheduled lunar measurements. The instrument gain (1/m1) derived from SD measurements and the response versus scan-angle (RVS) derived using the SD and lunar measurements are the primary look-up-tables (LUT) that are updated on a regular basis. The short wavelength bands of both MODIS instruments have experienced a gain change of up to 50% as observed from the SD calibration. Given the longevity of both MODIS sensors, the detectors have aged, and the sensor’s radiometric characteristics have changed since launch. A strong dependence of the RVS on mirror side, detector, wavelength and time is also observed in visible (VIS) channels. The MODIS Characterization Support Team (MCST) is responsible for deriving and updating the on-orbit calibration coefficients. An accurate and timely update of the LUT is vital for maintaining the quality of the calibrated Level 1B (L1B) product. This paper provides an overview of the MODIS RSB calibration along with the major procedures used to regularly update the LUT. In addition, the various lessons learned and future improvements are also discussed.

The double-sided paddle wheel scan mirror is the key optical component of the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the NASA EOS Terra and Aqua satellites. At a constant rotating speed, the scan mirror continuously reflects the Earth's top-of-atmosphere radiances through the instrument nadir aperture door and onto four focal plane assemblies (FPA), which consist of 36 spectral bands. Of those 36 bands, 16 are thermal emissive bands (TEB) with wavelengths ranging from 3.7 to 14.4μm. While this cross-track scanning system provides the Earth scene observations over a range of ±55° viewing angles from the nadir, the on-orbit calibration for TEB is performed by an On-Board Calibrator Blackbody (OBC BB) at a fixed viewing angle. The response versus scan angle (RVS) of the scan mirror is sensitive to the MODIS radiometric calibration. This paper describes how the pre-launch TEB RVS of the Aqua MODIS was characterized at the instrument system level by using ground support equipment, a Blackbody Calibration Source (BCS). The RVS test setup, test procedure, data analysis, derivation of RVS, and the fitting uncertainty are discussed in the paper. A separate paper that gives similar RVS analysis for the MODIS Reflective Solar Bands (RSB) is presented in this proceeding.

This article presents a comprehensive subpixel water mapping algorithm to automatically produce routinely open water fraction maps in the Tibetan Plateau (TP) with the Moderate Resolution Imaging Spectroradiometer (MODIS). A multi-index threshold endmember extraction method was applied to select the endmembers from MODIS images. To incorporate endmember variability, an endmember selection strategy, called the combined use of typical and neighboring endmembers, was adopted in multiple endmember spectral mixture analysis (MESMA), which can assure a robust subpixel water fractions estimation. The accuracy of the algorithm was assessed at both the local scale and regional scale. At the local scale, a comparison using the eight pairs of MODIS/Landsat 8 Operational Land Imager (OLI) water maps demonstrated that subpixels water fractions were well retrieved with a root mean square error (RMSE) of 7.86% and determination coefficient (R2) of 0.98. At the regional scale, the MODIS water fraction map in October 2014 matches well with the TP lake data set and the Global Lake and Wetland Database (GLWD) in both latitudinal and longitudinal distribution. The lake area estimation is more consistent with the reference TP lake data set (difference of −3.15%) than the MODIS Land Water Mask (MOD44W) (difference of −6.39%).