Moderate Resolution Imaging Spectroradiometer Instrument Research Articles

Sea Ice Examination for Ice Class Ships visiting Horseshoe Island, Antarctica, Using MODIS in 2000-2022

Sea ice has a significant impact on numerous areas, ranging from the reflection of the sun rays back on the albedo scale, to spurring intense phytoplankton blooms that help maintain the marine ecosystem and making the navigation of some maritime routes challenging. The formation of sea ice in the Southern Ocean affects maritime logistics and scientific operations on Antarctica, especially on the Antarctic Peninsula, which hosts half of the scientific stations. Ships needed to supply British and Turkish scientific bases on Horseshoe Island in the southernmost part of the Antarctic Peninsula are in high demand. Therefore, the analysis of the oldest trends of sea ice that affect maritime operations is crucial. The goal of the study was to examine the formation of sea ice, and especially ice-free days, by using images collected between 2000 and 2022 by over 8000 NASA’s Terra/MODIS Moderate Resolution Imaging Spectroradiometer instruments (MODIS). An analysis of maritime traffic gives a better understanding of maritime operation needs and usage of natural ports, including bays within the research area. Therefore, the study focused on four sites: western approach to Horseshoe Island, Sally Cove (hosting a British station), Lystad Bay (hosting a Turkish station) and Gaul Cove. The results indicate that there has been no multiyear sea ice around Horseshoe Island. The most suitable months for the safe navigation of any type of ship in the area, due to the greatest number of ice-free days, are March, February, April and January. The study reveals that, in accordance with the Polar Code, lower ice class ships are required for safe navigation during scientific expeditions to Horseshoe Island. Also, as the average number of days with clear skies was 38, and average cloudiness very high, the use of optical satellites for long-term monitoring studies on Horseshoe Island is not recommended.

Radiometric assessment of OLCI, VIIRS, and MODIS using fiducial reference measurements along the Atlantic Meridional Transect

High quality independent ground measurements that are traceable to metrology standards, with a full uncertainty budget, are required for validation over the lifetime of ocean-colour satellite missions. In this paper, we used radiometric Fiducial Reference Measurements (FRM) collected during four Atlantic Meridional Transect (AMT) field campaigns from 2016 to 2019 to assess the performance of radiometric products from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3A (S-3A) and 3B (S‐3B), the Moderate Resolution Imaging Spectroradiometer instrument aboard Aqua (MODIS-Aqua), and the Visible Infrared Imaging Radiometer Suite instrument aboard Suomi NPP and NOAA-20 (Suomi-VIIRS and NOAA-20 VIIRS). The AMT provides one of the few sampling platforms that make high-quality in situ radiometric measurements in oligotrophic, low chlorophyll-a oceanic waters for ocean colour satellite validation. In situ data were acquired and processed following established FRM protocols, calibrated to metrology standards, referenced to inter-comparison exercises and with a full uncertainty budget. From these we selected an uncertainty threshold, which we used as part of a matchup procedure that takes into account the temporal and spatial variability of both the in situ and satellite data. Three atmospheric correction models were compared for S-3A and S‐3B OLCI radiometric products; the standard OLCI IPF-OL-2, POLYMER and NASA SeaDAS l2gen. Based on the round-robin comparison, POLYMER provided the best performance in the retrieval of water-leaving radiances. The analysis showed that Suomi-VIIRS and MODIS-Aqua performed better than NOAA-20 VIIRS, and comparably with S‐3B OLCI standard products. The S-3A OLCI standard product outperformed the NASA products. The S-3A OLCI and S‐3B OLCI instruments were also compared during their tandem phase, which showed that S‐3B OLCI radiances were systematically higher than S-3A OLCI across the spectrum.

On the Identification of Agroforestry Application Areas Using Object-Oriented Programming

The detection of possible areas for the application of agroforestry is essential and involves the usage of various technics. The recognition of forest types using satellite or aerial imagery is the first step toward this goal. This is a tedious task involving the application of remote sensing techniques and a variety of computer software. The overall performance of this approach is very good and the resulting land use maps can be considered of high accuracy. However, there is also the need for performing high-speed characterization using techniques that can determine forest types automatically and produce quick and acceptable results without the need for specific software. This paper presents a comprehensive methodology that uses Normalized Difference Vegetation Index (NDVI) data derived from the Moderate Resolution Imaging Spectroradiometer instrument (MODIS) aboard the TERRA satellite. The software developed automatically downloads data using Google Earth Engine and processes them using Google Colab, which are both free-access platforms. The results from the analysis were exported to ArcGIS for evaluation and comparison against the CORINE land cover inventory using the latest update (2018).

The association between the incidence of Lyme disease in the USA and indicators of greenness and land cover

Lyme disease (LD) is the most common vector-borne illness in the USA. Incidence is related to specific environmental conditions such as temperature, metrics of land cover, and vertebrate species diversity. To determine whether greenness, as measured by the Normalized Difference Vegetation Index (NDVI), and other selected indices of land cover were associated with the incidence of LD in the northeastern USA for the years 2000–2018, we conducted an ecological analysis of incidence rates of LD in counties of 15 “high” incidence states and the District of Columbia for 2000–2018. Annual counts of LD by county were obtained from the US Centers for Disease Control and values of NDVI were acquired from the Moderate Resolution Imaging Spectroradiometer instrument aboard Terra and Aqua Satellites. County-specific values of human population density, area of land and water were obtained from the US Census. Using quasi-Poisson regression, multivariable associations were estimated between the incidence of LD, NDVI, land cover variables, human population density, and calendar year. We found that LD incidence increased by 7.1% per year (95% confidence interval: 6.8–8.2%). Land cover variables showed complex non-linear associations with incidence: average county-specific NDVI showed a “u-shaped” association, the standard deviation of NDVI showed a monotonic upward relationship, population density showed a decreasing trend, areas of land and water showed “n-shaped” relationships. We found an interaction between average and standard deviation of NDVI, with the highest average NDVI category; increased standard deviation of NDVI showed the greatest increase in rates. These associations cannot be interpreted as causal but indicate that certain patterns of land cover may have the potential to increase exposure to infected ticks and thereby may contribute indirectly to increased rates of LD. Public health interventions could make use of these results in informing people where risks may be high.

Evaluation of aerosol optical depths and clear-sky radiative fluxes of the CERES Edition 4.1 SYN1deg data product

Abstract. Aerosol optical depths (AODs) used for the Edition 4.1 Clouds and the Earth's Radiant Energy System (CERES) Synoptic 1∘ (SYN1deg) product are evaluated. AODs are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations and assimilated by an aerosol transport model (the Model for Atmospheric Transport and Chemistry – MATCH). As a consequence, clear-sky AODs closely match with those derived from MODIS instruments. AODs under all-sky conditions are larger than AODs under clear-sky conditions, which is supported by ground-based AErosol RObotic NETwork (AERONET) observations. When all-sky MATCH AODs are compared with Modern-Era Retrospective analysis for Research and Applications (Version 2; MERRA-2) AODs, MATCH AODs are generally larger than MERRA-2 AODs, especially over convective regions (e.g., the Amazon, central Africa, and eastern Asia). This variation is largely due to the differing methods of assimilating the MODIS AOD data product and the use of quality flags in our assimilation. Including AODs with larger retrieval uncertainty makes AODs over the convective regions larger. When AODs are used for clear-sky irradiance computations and computed downward shortwave irradiances are compared with ground-based observations, the computed instantaneous irradiances are 1 %–2 % larger than observed irradiances. The comparison of top-of-atmosphere clear-sky irradiances with those derived from CERES observations suggests that AODs used for surface radiation observation sites are 0.01–0.03 larger, which is within the uncertainty of instantaneous MODIS AODs. However, the comparison with AERONET AODs suggests that AODs used for computations over desert sites are 0.08 larger. The cause of positive biases in downward shortwave irradiance and in AOD for the desert sites is possibly due to the dust particle size and distribution, as defined by the MATCH transport model, and the transfer of that information into the radiative transfer model.

PM2.5 Air Pollution Prediction through Deep Learning Using Multisource Meteorological, Wildfire, and Heat Data

Air pollution is a lethal global threat. To mitigate the effects of air pollution, we must first understand it, find its patterns and correlations, and predict it in advance. Air pollution is highly dependent on spatial and temporal correlations of prior meteorological, wildfire, and pollution structures. We use the advanced deep predictive Convolutional LSTM (ConvLSTM) model paired with the cutting-edge Graph Convolutional Network (GCN) architecture to predict spatiotemporal hourly PM2.5 across the Los Angeles area over time. Our deep-learning model does not use atmospheric physics or chemical mechanism data, but rather multisource imagery and sensor data. We use high-resolution remote-sensing satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the NASA Terra+Aqua satellites and remote-sensing data from the Tropospheric Monitoring Instrument (TROPOMI), a multispectral imaging spectrometer onboard the Sentinel-5P satellite. We use the highly correlated Fire Radiative Power data product from the MODIS instrument which provides valuable information about the radiant heat output and effects of wildfires on atmospheric air pollutants. The input data we use in our deep-learning model is representative of the major sources of ground-level PM2.5 and thus we can predict hourly PM2.5 at unparalleled accuracies. Our RMSE and NRMSE scores over various site locations and predictive time frames show significant improvement over existing research in predicting PM2.5 using spatiotemporal deep predictive algorithms.

Prediction of Solar Irradiance over the Arabian Peninsula: Satellite Data, Radiative Transfer Model, and Machine Learning Integration Approach

Data from a moderate resolution imaging spectroradiometer instrument onboard the Terra satellite along with a radiative transfer model and a machine learning technique were integrated to predict direct solar irradiance on a horizontal surface over the Arabian Peninsula (AP). In preparation for building appropriate residual network (ResNet) prediction models, we conducted some exploratory data analysis (EDA) and came to some conclusions. We noted that aerosols in the atmosphere correlate with solar irradiance in the eastern region of the AP, especially near the coastlines of the Arabian Gulf and the Sea of Oman. We also found low solar irradiance during March 2016 and March 2017 in the central (~20% less) and eastern regions (~15% less) of the AP, which could be attributed to the high frequency of dust events during those months. Compared to other locations in the AP, high solar irradiance was recorded in the Rub Al Khali desert during winter and spring. The effect of major dust outbreaks over the AP during March 2009 and March 2012 was also noted. The EDA indicated a correlation between high aerosol loading and a decrease in solar irradiance. The analysis showed that the Rub Al Khali desert is one of the best locations in the AP to harvest solar radiation. The analysis also showed the ResNet prediction model achieves high test accuracy scores, indicated by a mean absolute error of ~0.02, a mean squared error of ~0.005, and an R2 of 0.99.

Long-term trends of satellite-based fine-mode aerosol optical depth over the Seto Inland Sea, Japan, over two decades (2001–2020)

Air pollution over the Seto Inland Sea (SIS) is among the most severe of any region in Japan and is considered to be affected by both long-range and local pollution. To unravel the long-term trends of aerosol pollution over this region, in this study, measurements from the moderate resolution imaging spectroradiometer instrument onboard the Terra satellite were analyzed over two decades, from 2001 to 2020. Fine-mode aerosol optical depth (AODf) was calculated to estimate the amount of aerosol produced by anthropogenic emissions. The results showed that the AODf over the SIS increased from 2001 to 2004, had a flat trend from 2005 to 2009, and decreased from 2010 to 2020. To clarify the impact of long-range transport from the Asian continent to the SIS, the AODf over the Yellow Sea was also investigated and was found to increase and level off during the 2000s, after which it decreased, especially after 2014. This decrease can be attributed to emission regulations in China. The above analysis suggests that the aerosol pollution status in the SIS during the late 2010s was similar to that during the early 2000s. Over the SIS, the lowest AODf value was found in 2020, with the values in January–March and June–July approximately 30% and 30%–60% lower than the average values during the same periods in 2018–2019, respectively. The reduction found in January–March could be related to the decline in the long-range transport with restrictions on human activity due to the COVID-19 pandemic. Meanwhile, the reduction during June–July could be related to the decline of local emission sources. Considering the large SO2 decline in 2020, regulations on SO2 emitted from ships that started from 1 January 2020 are one possible factor for the improvement of aerosol pollution over the SIS in 2020.

Agricultural Drought Monitoring by MODIS Potential Evapotranspiration Remote Sensing Data Application

The current Polish Agricultural Drought Monitoring System (ADMS) adopted Climatic Water Balance (CWB) as the main indicator of crop losses caused by drought conditions. All meteorological data needed for CWB assessment are provided by the ground meteorological stations network. In 2018, the network consisted of 665 stations, among which in only 58 stations full weather parameters were registered. Therefore, only these stations offered a possibility to estimate the exact values of potential evapotranspiration, which is a component of the CWB algorithm. This limitation affects the quality of CWB raster maps, interpolated on the basis of the meteorological stations network for the entire country. However, the interpolation process itself may introduce errors; therefore, the adaptation of satellite data (that are spatially continuous) should be taken into account, even if the lack of data due to cloudiness remains a serious problem. In this paper, we involved the remote sensing data from MODIS instrument and considered the ability to integrate those data with values determined by using ground measurements. The paper presents results of comparisons for the CWB index assessed using ground station data and those obtained from potential evapotranspiration as the product from Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing instrument. The comparisons of results were performed for specific points (locations of ground stations) and were expressed by differences in means values. Analysis of Pearson’s correlation coefficient (r), Mann–Kendal trend test (Z-index), mean absolute error (MAE) and root mean square error (RMSE) for ten years’ series were evaluated and are presented. In addition, the basic spatial interpretation of results has been proposed. The correlation test revealed the r coefficient in the range from 0.06 to 0.68. The results show good trend agreement in time between two types of CWB with constantly higher values of this index, which is estimated using ground measurement data. In results for 34 (from 43 analyzed) stations the Mann–Kendal test provide the consistent trend, and only nine trends were inconsistent. Analyses revealed that the disagreement between the two considered indices (determined in different ways) increased significantly in the warmer period with a significant break point between R7 and R8 that falls at the end of May for each examined year. The value of MAE varied from 80 mm to 135 mm.

Terra MODIS: 20 years of on-orbit calibration and performance

Since its launch in December 1999, Terra Moderate Resolution Imaging Spectroradiometer (MODIS) has successfully operated for more than 20 years, with its observations generating a broad range of science data products that have greatly enabled the remote sensing community and users worldwide in their studies of many key geophysical parameters of the Earth’s systems. MODIS collects data in 36 spectral bands, covering wavelengths from 0.41 to 14.4 μm, which are calibrated by a set of onboard calibrators (OBCs). Also contributing to the sensor’s mission-long on-orbit calibration and characterization are near-monthly scheduled lunar observations and multiple time series of the sensor’s responses over select ground targets at a variety of scan angles. To a large extent, the quality of MODIS data products relies strongly on the dedicated efforts to operate and calibrate the instrument, to derive and update calibration parameters, and to develop and implement new calibration strategies and algorithms in response to on-orbit changes of the sensor’s characteristics and its OBC functions. We provide an overview of the Terra MODIS on-orbit operation and calibration activities over the last 20 years, including changes made to extend and preserve the instrument and OBC functions and their operation strategies. It also illustrates the sensor’s on-orbit performance with results derived from its OBC, lunar observations, and select ground targets and discusses major changes in sensor characteristics and corrections applied to the L1B algorithms as well as calibration lookup table updates. To date, the Terra MODIS instrument and its OBCs continue to operate and function normally. Except for those identified prelaunch, most spectral bands and detectors continue to meet their specified calibration requirements. Also discussed in our paper are lessons learned from Terra MODIS operation and calibration, as well as future efforts to further extend and maintain the quality of its long-term data records.

Improved Lunar Irradiance Model Using Multiyear MODIS Lunar Observations

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the Terra and Aqua spacecrafts were launched on December 18, 1999, and May 4, 2002, respectively. One of the features of the MODIS instruments is the ability to perform observations of the lunar surface from its space view (SV) port. This event is scheduled approximately once a month via a spacecraft roll maneuver, which enables the lunar phase to be confined to within 1° for each instrument. The Moon is considered to be an extremely stable reference to monitor the long-term radiometric stability of the reflective solar bands (RSB). Each MODIS instrument can also view the Moon for about four months in a year without a roll maneuver. This is caused by the intrusion of the Moon in the SV. The lunar phase angles of these unscheduled lunar observations are distributed over a wide range varying from approximately 50° to 80° for Terra MODIS and from about -80° to -50° for Aqua MODIS, where the positive phase angle refers to a waning Moon, while the negative phase angle corresponds to a waxing Moon. Together, the scheduled and unscheduled lunar observations are used to monitor the long-term radiometric stability of the RSB. Of the several challenges involved in the modeling of the lunar optical properties, such as its absolute brightness, a number of optical and view geometry effects need to be considered. These effects are much easier to characterize for the scheduled observations due to confinement of the lunar phase angles compared to those for the unscheduled intrusions of the Moon in the SV. Nevertheless, it is still a challenge to remove the view geometry effect in the calibration coefficients derived from the scheduled lunar observations and even more challenging for the unscheduled lunar intrusions. In this work, the lunar absolute irradiance is modeled using known attributes and from the lunar observations by the two MODIS instruments from the time period between the years 2005 and 2012. The model developed here attempts to mitigate for the deficiencies in the lunar irradiance measurements by the RObotic Lunar Observatory (ROLO) model, developed by the United States Geological Survey. Overall, the relative uncertainty of the ROLO model for MODIS calibration has been assessed to be about 4% for the shortest wavelength (a center wavelength of 412 nm) in the phase angle range mentioned above. With our new established lunar model, the calibration coefficients derived from the lunar observations, especially those from the unscheduled lunar observations, for the RSB of the two MODIS instruments are significantly improved for the entire mission. A good agreement is observed between the calibration coefficients derived from the scheduled and unscheduled lunar observations. Both the absolute uncertainty of the new lunar irradiance model and its relative uncertainty due to view geometry variation are much smaller than those of the current ROLO model which has been widely used for most remote sensors' lunar calibrations. Our newly developed lunar irradiance model can be applied to other remote sensors for their lunar calibrations as well. Finally, the significant improvement in the measurement of the lunar irradiance led to a polarization effect in the Moon response for MODIS to be identified. In this article, the impact of the polarization of the moonlight for the MODIS RSB is quantified. This is extremely vital as polarization effect for remote sensors such as MODIS and the follow-on suite of the Joint Polar Satellite System Visible Infrared Imaging Radiometer Suite has been found to significantly increase the calibration uncertainty, especially at short wavelengths.

Cross-Calibration of MODIS Reflective Solar Bands With Sentinel 2A/2B MSI Instruments

Moderate resolution imaging spectroradiometer (MODIS) is a 36-band spectroradiometer that measures the Earth's surface from 0.4 to 14.4 μm at three spatial resolutions, 250 m (two bands), 500 m (five bands), and 1000 m (29 bands). The wide-scale use of the science products derived from the two MODIS instruments on the Terra and Aqua spacecrafts is a result of their excellent on-orbit performance, calibration stability, and accuracy through the life of the mission, making them a benchmark against which the performance of newer instruments is frequently evaluated. The recently launched multispectral instrument (MSI) aboard the Sentinel 2A and Sentinel 2B spacecrafts are part of the European Union's Copernicus program designed to acquire high spatial resolution imagery in the reflective spectrum from 0.4 to 2.2 μm. One of the popular techniques to evaluate the on-orbit calibration is by comparing the top-of-atmosphere (TOA) reflectance with an independent (well-calibrated) sensor while viewing a pseudoinvariant desert target, such as Libya 4. In this work, the TOA reflectances from Terra and Aqua MODIS and Sentinel 2A and Sentinel 2B MSI are compared using the same-day scenes from Libya 4. The corrections for spectral response function mismatch and the bidirectional reflectance distribution function (BRDF) are formulated and applied to obtain an effective TOA reflectance difference between the spectrally matching bands of these sensors. The availability of off-nadir MODIS overpass pairs with MSI facilitates the comparison across the entire MODIS scanangle range and in turn an on-orbit evaluation of the response versus scan-angle (RVS) corrections is performed. Additionally, each MODIS instrument is used as a transfer mechanism to evaluate the calibration differences between the two MSIs, with an agreement to within 1% observed between the two MSIs. The radiometric calibration differences between Terra MODIS and the two MSIs at nadir is generally within 4%, with only the red-band pair (Terra MODIS band 1 and MSI band 4) showing disagreement beyond 4%. In general, the reflectance ratios are in better agreement with Aqua MODIS than with Terra MODIS. A better agreement between MODIS and the two MSIs is observed at nadir, indicating some residual effects associated with the BRDF correction that are observed in the off-nadir scene pairs. Also included in this article is a description of the various uncertainties associated with this cross-calibration.

Response Versus Scan-Angle Assessment of MODIS Reflective Solar Bands in Collection 6.1 Calibration

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Aqua and Terra satellites have been operated for nearly two decades, producing high-quality earth observation data sets suitable for a broad range of scientific studies regarding the earth’s land, ocean, and atmospheric processes. The high radiometric accuracy of MODIS reflective solar band (RSB) calibration has also served as benchmark measurements for on-orbit cross-calibration studies. As the two MODIS instruments have operated well beyond their design lifespan of six years, the measurements from the onboard calibrators alone become inadequate to characterize the sensor’s response at all scan angles, as evinced by long-term drifts observed at certain scan positions of the Aqua-MODIS 0.64- and 0.86- $\mu \text{m}$ bands in Collection 6 (C6) data set. The latest MODIS Level 1B C6.1 data set incorporates earth-view response trending from invariant desert sites as supplemental inputs to characterize the scan-angle calibration dependencies for all RSB. This article presents a deep convective cloud (DCC)-based calibration approach for an independent evaluation of the MODIS RSB response versus scan-angle (RVS) performance in C6.1. The long-term calibration stability and RVS differences in C6.1 have been significantly improved for Aqua-MODIS RSB. The observed RVS differences of more than 2% in Aqua-MODIS C6 bands 1 and 2 have been reduced to within 1% in C6.1. Some RSBs of Terra-MODIS have suffered temporal drifts up to ~2% and calibration shifts up to 3%, particularly around 2016 when the Terra satellite entered into safe mode. The DCC approach has been found very effective in tracking the on-orbit RVS changes over time.

MODIS Reflective Solar Bands On-Orbit Calibration and Performance

The design of the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument was driven by the scientific community's desire to have near-daily global coverage at moderate resolution (~1 km) with comprehensive spectral coverage from visible to long-wave infrared wavelengths. Since their launches in 1999 and 2002, respectively, the Terra and Aqua MODIS instruments have made continuous global observations and generated numerous data products to help users worldwide with their studies of the Earth's system and its shortand long-term changes. The 20 reflective solar bands (RSBs) with wavelengths from 0.41 to 2.2 μm collect data at three nadir spatial resolutions: 250 m, 500 m, and 1 km. The solar diffuser (SD) coupled with the SD stability monitor (SDSM) provides a reflectance-based calibration on-orbit. In addition, lunar observations and response trends from pseudoinvariant desert sites are used to characterize the response versus scanangle changes on-orbit. This paper provides a brief overview of MODIS RSB calibration algorithms, as implemented in the latest Level 1B version 6.1, operational activities, on-orbit performance, remaining challenges, and potential improvements. Results from the SD and SDSM measurements show a wavelength and mirrorside-dependent degradation in RSB responses, with the largest degradation at the shortest wavelengths, particularly for Terra MODIS. Aqua MODIS has experienced far less degradation of its optics and on-board calibrators compared with Terra MODIS, resulting in an overall better performance. With the exception of Aqua band 6, there have been no new noisy or inoperable detectors in the RSB of either instrument during postlaunch operations. As the instruments age and continue to endure the space environment, the detectors and the optical systems degrade. The challenges associated with incorporating these onorbit changes to ensure a production of high-quality calibrated L1B data products are also discussed in this paper.

Comparison of Diurnal Variation of Land Surface Temperature From GOES-16 ABI and MODIS Instruments

Land surface temperature (LST) and its diurnal variation are the critical factors in many aspects of climate study, surface energy balance, and environmental applications. Several satellite-based LST products are available for retrievals from regional to a global scale. However, due to the differences in sensor configurations and retrieval algorithms, these products may not necessarily be consistent. In this letter, the consistency of spatial and temporal skin temperature variations from two infrared satellite platforms has been evaluated over the contiguous United States (CONUS). Comparisons are made between the LST products from the newly launched Geostationary Operation Environmental Satellite R Series (GOES-R) advanced baseline imager (ABI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both Aqua and Terra satellites which are polar orbiting. Overall, both products show a general agreement in their diurnal variations with differences mostly under 2 K. However, a temperature-dependent inconsistency has been detected. The MODIS LST product seems to estimate higher temperatures in the summer months while the GOES product estimates higher temperatures during the winter months. Moreover, the maximum observed diurnal differences could reach up to 10 K in mountainous regions. The results suggest that the corresponding temperature differences should be accounted for when LST diurnal variations are compared or generated from satellite observations.

Global validation of columnar water vapor derived from EOS MODIS-MAIAC algorithm against the ground-based AERONET observations

The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1 km resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000–2017). Overall, the results show a good agreement between MAIAC/AERONET CWV retrievals, with correlation coefficient higher than 0.95 and RMS error lower than 0.250 cm. The binned error analysis revealed an underestimation (~10%) of Aqua CWV retrievals with negative bias for CWV higher than 3.0 cm. In contrast, Terra CWV retrievals show a slope of regression close to unity and a low mean bias of 0.075 cm. While the accuracy is relatively similar between 1.0 and 5.0 cm for both sensor products, Terra dataset is more reliable for applications in humid tropical areas (>5.0 cm). The expected error was defined as ±15%, with >68% of retrievals falling within this envelope. However, the accuracy is regionally dependent, and lower error should be expected in some regions, such as South America and Oceania. Since MODIS instruments have exceeded their design lifetime, time series analysis was also presented for both sensor products. The temporal analysis revealed a systematic offset of global average between Terra and Aqua CWV records. We also found an upward trend (~0.2 cm/decade) in Terra CWV retrievals, while Aqua CWV retrievals remain stable over time. The sensor degradation influences the ability to detect climate signals, and this study indicates the need for revisiting calibration of the MODIS bands 17–19, mainly for Terra instrument, to assure the quality of the MODIS water vapor product. Finally, this study presents a comprehensive validation analysis of MAIAC CWV over land, raising the understanding of its overall quality.

Cloud Heterogeneity in the Marine Midlatitudes: Dependence on Large‐Scale Meteorology and Implications for General Circulation Models

AbstractWe examine the sensitivity of cloud heterogeneity to large‐scale meteorology in the marine midlatitudes using satellite observations from the Multiangle Imaging Spectroradiometer and Moderate Resolution Imaging Spectroradiometer instruments aboard the Terra satellite and nudged simulations from the UK Met Office's Global Atmosphere 7.0 (GA7) for the year 2007. Using Multiangle Imaging Spectroradiometer observations, we quantify several sources of observational uncertainty due to cloud heterogeneity such as finite‐resolution cloud fraction biases and subpixel heterogeneity. With a simple measure of cloud geometry, we show that these sources of observational uncertainty are maximized in post–cold‐frontal conditions, where scattered subpixel clouds are frequent, and are minimized in the warm sector, where subpixel clouds are infrequent. These results demonstrate the greater difficulty in interpreting remote sensing measurements in post–cold‐frontal conditions compared to quiescent or warm‐sector conditions. We show that the neglect of this observational uncertainty can qualitatively alter the interpretation of how cloud properties respond to large‐scale meteorology in GA7 and general circulation models in general. However, conservative application of the satellite data still allows robust evaluation of the simulated clouds. For overcast domains, Moderate Resolution Imaging Spectroradiometer observations show that overcast cloud heterogeneity is independent of large‐scale meteorology. However, heterogeneity increases when moving from warm sector to post‐cold‐frontal conditions in GA7. GA7 overestimates the heterogeneity of the low‐topped clouds that populate these regimes. For overcast domains, this bias is equivalent to a 15% underestimation in the mean cloud optical depth. These results suggest a systematic error in the subgrid‐scale variability parameterization, which, when corrected, will improve the simulation of the midlatitudes in GA7.

Observations of positive sea surface temperature trends in the steadily shrinking Dead Sea

Abstract. Increasing warming of steadily shrinking Dead Sea surface water compensates for surface water cooling (due to increasing evaporation) and even causes observed positive Dead Sea sea surface temperature trends. This warming is caused by two factors: increasing daytime heat flow from land to sea (as a result of the steady shrinking) and regional atmospheric warming. Using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), positive trends were detected in both daytime and nighttime Dead Sea sea surface temperature (SST) over the period of 2000–2016. These positive SST trends were observed in the absence of positive trends in surface solar radiation, measured by the Dead Sea buoy pyranometer. We also show that long-term changes in water mixing in the uppermost layer of the Dead Sea under strong winds could not explain the observed SST trends. There is a positive feedback loop between the positive SST trends and the steady shrinking of the Dead Sea, which contributes to the accelerating decrease in Dead Sea water levels during the period under study. Satellite-based SST measurements showed that maximal SST trends of over 0.8 ∘C decade−1 were observed over the northwestern and southern sides of the Dead Sea, where shrinking of the Dead Sea water area was pronounced. No noticeable SST trends were observed over the eastern side of the lake, where shrinking of the Dead Sea water area was insignificant. This finding demonstrates correspondence between the positive SST trends and the shrinking of the Dead Sea indicating a causal link between them. There are two opposite processes taking place in the Dead Sea: sea surface warming and cooling. On the one hand, the positive feedback loop leading to sea surface warming every year accompanied by long-term increase in SST; on the other hand, the measured acceleration of the Dead Sea water-level drop suggests a long-term increase in Dead Sea evaporation accompanied by a long-term decrease in SST. During the period under investigation, the total result of these two opposite processes is the statistically significant positive sea surface temperature trends in both daytime (0.6 ∘C decade−1) and nighttime (0.4 ∘C decade−1), observed by the MODIS instrument. Our findings of the existence of a positive feedback loop between the positive SST trends and the shrinking of the Dead Sea imply the following significant point: any meteorological, hydrological or geophysical process causing the steady shrinking of the Dead Sea will contribute to positive trends in SST. Our results shed light on continuing hazards to the Dead Sea.

Improvements in the On-Orbit Response Versus Scan Angle Characterization of the Aqua MODIS Reflective Solar Bands

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) has been chosen by the Global Space-based Inter-Calibration System operational community as the reference sensor in cross-sensor calibration. A number of geostationary orbit and low-earth orbit sensors use the 0.64- $\mu \text{m}$ band from Aqua MODIS as a calibration reference. After over 15 years on-orbit, the performance characteristics of the MODIS instrument have changed, with effects evident at short wavelengths. MODIS employs a reflectance-based calibration using the solar diffuser measurements with the monthly lunar observations facilitating a response versus scan angle characterization on-orbit. As the instrument continues to operate beyond its design lifetime of 6 years, the on-board calibrators alone are insufficient to accurately characterize the instrument’s response at all scan angles. This results in a long-term reflectance drift, particularly observed in the 0.64- and 0.85- $\mu \text{m}$ bands, while observing the temporally invariant desert sites. Long-term reflectance drifts of up to 2% and 3% are observed at the beginning of scan for the 0.64- and 0.85- $\mu \text{m}$ bands, respectively. An approach using earth-view response to supplement the on-board calibrator measurements has been shown to overcome these inadequacies and is now implemented for the 0.64-, 0.85-, 0.46-, and 0.55- $\mu \text{m}$ land bands of Aqua MODIS. This paper presents the details related to the algorithm implementation and an independent evaluation using the Dome Concordia site and deep-convective clouds. This approach has been reviewed, tested, and approved by the MODIS science team and has been implemented in the forward production of the MODIS L1B Collection 6 starting July 9, 2016. This enhanced approach has also been adopted in the MODIS L1B Collection 6.1 reprocess for the entire mission to facilitate an improved quality of downstream science products. The results with the enhanced approach reduce the reflectance drifts to within 0.5% for most cases.

Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States

High spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST is one of such high spatiotemporal datasets that are widely used. But, it has a large amount of missing values primarily because of clouds, shadows, and other atmospheric conditions. Gapfilling the missing values is an important approach to create seamless high spatiotemporal LST datasets. However, current gapfilling methods have limitations in terms of accuracy and efficiency to assemble the data over large areas (e.g., national and continental levels). In this study, we developed a 3-step hybrid method by integrating daily merging (gapfilling missing values at one overpass using values at the other three overpasses each day), spatiotemporal gapfilling (estimating missing values based on values of their spatial and temporal neighbors), and temporal interpolation (gapfilling missing values based on values of their neighboring days), to create a seamless high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. We applied this method in urban and surrounding areas in the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates its root mean squared error (RMSE) to be 3.3K for mid-daytime (1:30pm) and 2.7K for mid-nighttime (1:30am) observations. The method can be easily extended to other years and regions and is also applicable to other satellite products for large areas. This seamless daily (mid-daytime and mid-nighttime) LST product with 1km spatial resolution is of great value for studying urban climate (e.g., quantifying surface urban heat island intensity, creating seamless high spatiotemporal air temperature dataset) and the related impacts on people (e.g., health and mortality), ecosystems (e.g., phenology), and energy systems (e.g., building energy use).