Terra Moderate Resolution Imaging Spectroradiometer Research Articles

Estimation of Climatologies of Average Monthly Air Temperature over Mongolia Using MODIS Land Surface Temperature (LST) Time Series and Machine Learning Techniques

The objective of this research was to develop a robust statistical model to estimate climatologies (2002–2017) of monthly average near-surface air temperature (Ta) over Mongolia using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) time series products and terrain parameters. Two regression models were analyzed in this study linking automatic weather station data (Ta) with Earth observation (EO) images: Partial least squares (PLS) and random forest (RF). Both models were trained to predict Ta climatologies for each of the twelve months, using up to 17 variables as predictors. The models were applied to the entire land surface of Mongolia, the eighteenth largest but most sparsely populated country in the world. Twelve of the predictor variables were derived from the LST time series products of the Terra MODIS satellite. The LST MOD11A2 (collection 6) products provided thermal information at a spatial resolution of 1 km and with 8-day temporal resolution from 2002 to 2017. Three terrain variables, namely, elevation, slope, and aspect, were extracted using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and two variables describing the geographical location of weather stations were extracted from vector data. For training, a total of 8544 meteorological data points from 63 automatic weather stations were used covering the same period as MODIS LST products. The PLS regression resulted in a coefficient of determination (R2) between 0.74 and 0.87 and a root-mean-square error (RMSE) from 1.20 °C to 2.19 °C between measured and estimated monthly Ta. The non-linear RF regression yielded even more accurate results with R2 in the range from 0.82 to 0.95 and RMSE from 0.84 °C to 1.93 °C. Using RF, the two best modeled months were July and August and the two worst months were January and February. The four most predictive variables were day/nighttime LST, elevation, and latitude. Using the developed RF models, spatial maps of the monthly average Ta at a spatial resolution of 1 km were generated for Mongolia (~1566 × 106 km2). This spatial dataset might be useful for various environmental applications. The method is transparent and relatively easy to implement.

Global Cloud Detection for CERES Edition 4 Using Terra and Aqua MODIS Data

The Clouds and Earth’s Radiant Energy System (CERES) has been monitoring clouds and radiation since 2000 using algorithms developed before 2002 for CERES Edition 2 (Ed2) products. To improve cloud amount accuracy, CERES Edition 4 (Ed4) applies revised algorithms and input data to Terra and Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) radiances. The Ed4 cloud mask uses 5–7 additional channels, new models for clear-sky ocean and snow/ice-surface radiances, and revised Terra MODIS calibrations. Mean Ed4 daytime and nighttime cloud amounts exceed their Ed2 counterparts by 0.035 and 0.068. Excellent consistency between average Aqua and Terra cloud fraction is found over nonpolar regions. Differences over polar regions are likely due to unresolved calibration discrepancies. Relative to Ed2, Ed4 cloud amounts agree better with those from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). CALIPSO comparisons indicate that Ed4 cloud amounts are more than or as accurate as other available cloud mask systems. The Ed4 mask correctly identifies cloudy or clear areas 90%–96% of the time during daytime over nonpolar areas depending on the CALIPSO–MODIS averaging criteria. At night, the range is 88%–95%. Accuracy decreases over land. The polar day and night accuracy ranges are 90%–91% and 80%–81%, respectively. The mean Ed4 cloud fractions slightly exceed the average for seven other imager cloud masks. Remaining biases and uncertainties are mainly attributed to errors in Ed4 predicted clear-sky radiances. The resulting cloud fractions should help CERES produce a more accurate radiation budget and serve as part of a cloud property climate data record.

Orbital Path and Spacecraft Attitude Correction for the MODIS Lunar Spatial Characterization

For the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua platforms, regularly scheduled lunar observations using spacecraft roll maneuvers have been used extensively for sensor characterization. While the primary purpose of these observations is for radiometric calibration of the reflective solar bands, they have also been leveraged for a number of other sensor performance assessments, such as the band-to-band spatial registration (BBR) and detector-to-detector spatial registration (DDR). The spatial registration calculations are complicated by the fact that the Moon does not move in a straight path across the sensor field of view (FOV). This path is determined by the relative orbital motion between the spacecraft and the Moon and the instrument attitude error that results from the roll maneuver. In this article, we develop a correction for the MODIS lunar spatial characterization measurements by calculating the predicted path of the Moon across the sensor FOV using spacecraft and lunar ephemeris data to model the relative orbital motion between the spacecraft and the Moon along with spacecraft attitude error data acquired during the roll maneuver. The difference between the measured and predicted positions of the Moon in the MODIS FOV can be used to calculate the BBR and DDR results. Since the predicted path across the FOV will be the same for each band, the BBR results will be minimally affected. However, we will show that the along-scan spread in the DDR can be significantly reduced, which results in a much greater consistency throughout the full mission for both Aqua and Terra MODIS.

Satellite data cloud detection using deep learning supported by hyperspectral data

ABSTRACTDeep learning methods can play an important role in satellite data cloud detection. The number and quality of training samples directly affect the accuracy of cloud detection based on deep learning. Therefore, selecting a large number of representative and high-quality training samples is a key step in cloud detection based on deep learning. For different satellite data sources, choosing sufficient and high-quality training samples has become an important factor limiting the application of deep learning in cloud detection. This paper presents a fast method for obtaining high-quality learning samples, which can be used for cloud detection of different satellite data with deep learning methods. AVIRIS (Airborne Visible Infrared Imaging Spectrometer) data, which have 224 continuous bands in the spectral range from 400–2500 nm, are used to provide cloud detection samples for different types of satellite data. Through visual interpretation, a sufficient number of cloud and clear sky pixels are selected from the AVIRIS data to construct a hyperspectral data sample library, which is used to simulate different satellite data (such as data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Operational Land Imager (OLI) satellites) as training samples. This approach avoids selecting training samples for different satellite sensors. Based on the Keras deep learning framework platform, a backpropagation (BP) neural network is employed for cloud detection from Landsat 8 OLI, National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and Terra MODIS data. The results are compared with cloud coverage results interpreted via artificial vision. The results demonstrate that the algorithm achieves good cloud detection results for the above data, and the overall accuracy is greater than 90%.

Heterogeneous spring phenology shifts affected by climate: supportive evidence from two remotely sensed vegetation indices

The Northern Hemisphere spring greenup (SG) has advanced between 0–12 days per decade since early 1980s as inferred from multiple satellite time series. The wide range of SG shifts is mainly due to the fact that these studies cover different periods and regions, and using different satellite records. Assessing the spatial heterogeneity of SG trends associated with different satellites is essential for robustly interpreting phenological dynamics and their responses to climate. We investigated the heterogeneity of the SG trends and their responses to climate variability with two satellite products (1) Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and (2) Advanced Very High Resolution Radiometer (AVHRR) over the period 2001–2013. Both MODIS and AVHRR agreed in showing the spatial distribution of mean SG, and SG advancement in northern Canada, the eastern United States, and Russia, and SG delay in western North America, parts of Baltic Europe, and East Asia. However, we identified contrasting MODIS and AVHRR SG trends in the northern high latitudes. Our analyses of correlations between SG and preseason climate drivers indicated that temperature dominated the interannual variability of SG. Preseason, the period preceding SG and highly correlated with the timing of SG has experienced much stronger warming than the spring season. MODIS and AVHRR indicated consistent temperature sensitivity of SG across biomes, even though the MODIS inferred SG is better correlated and more sensitive to temperature across biomes as compared to AVHRR. The sensitivities of SG to temperature across biomes is stable but with a slight increase over 2001–2013, in comparison with that over 1988–2000. The increased SG-temperature sensitivity is associated with increased precipitation during the spring season, which regulated the sensitivity of SG to spring temperature.

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.

Examination of Radiometric Deviations in Bands 29, 31, and 32 of MODIS Collection 6.0 and 6.1

The radiometric stability of selected thermal emissive bands (TEBs) of the Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites is examined via a direct intersensor comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The analysis adapts a “nadir-only” refinement of the simultaneous nadir overpasses (SNOs) analysis to evaluate radiometric deviations in MODIS Collection 6.0 and 6.1 data releases, the latter having been implemented with a version of crosstalk effect mitigation for Terra MODIS bands 27–30. The comparison time series result, with an average precision of 0.4%, shows radiometric deviation in Terra MODIS band 29 ( $8.55~\mu \text{m}$ ) up to 6%, or 3 K in brightness temperature (BT), in Collection 6.0, and correspondingly −1.5%, or −0.7 K in BT, in Collection 6.1, in particular significantly worsened by the safe mode event of February 2016. This paper includes the inter-TEB comparisons of MODIS bands B31 and B32 with SNPP VIIRS as two representative cases of nonideal spectral match demonstrating various key features and new information as revealed under the “nadir-only” framework, and also describes a procedure using both radiance-to-radiance comparison and BT-difference time series to estimate the BT-difference uncertainty specifications as well as the radiance-to-radiance time series baselines that are shifted away from unity. The finding for Terra MODIS band 29 highlights the continual nontrivial impact of crosstalk effect on numerous TEBs of MODIS Collection 6.1 not fully mitigated, which can further impact cloud masks, sea surface temperatures, and other product retrievals.

New Approach for Temporal Stability Evaluation of Pseudo-Invariant Calibration Sites (PICS)

Pseudo-Invariant Calibration Sites (PICS) are one of the most popular methods for in-flight vicarious radiometric calibration of Earth remote sensing satellites. The fundamental question of PICS temporal stability has not been adequately addressed. However, the main purpose of this work is to evaluate the temporal stability of a few PICS using a new approach. The analysis was performed over six PICS (Libya 1, Libya 4, Niger 1, Niger 2, Egypt 1 and Sudan 1). The concept of a “Virtual Constellation” was developed to provide greater temporal coverage and also to overcome the dependence limitation of any specific characteristic derived from one particular sensor. TOA reflectance data from four sensors consistently demonstrating “stable” calibration to within 5%—the Landsat 7 ETM+ (Enhanced Thematic Mapper Plus), Landsat 8 OLI (Operational Land Imager), Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and Sentinel-2A MSI (Multispectral Instrument)–were merged into a seamless dataset. Instead of using the traditional method of trend analysis (Student’s T test), a nonparametric Seasonal Mann-Kendall test was used for determining the PICS stability. The analysis results indicate that Libya 4 and Egypt 1 do not exhibit any monotonic trend in six reflective solar bands common to all of the studied sensors, indicating temporal stability. A decreasing monotonic trend was statistically detected in all bands, except SWIR 2, for Sudan 1 and the Green and Red bands for Niger 1. An increasing trend was detected in the Blue band for Niger 2 and the NIR band for Libya 1. These results do not suggest abandoning PICS as a viable calibration source. Rather, they indicate that PICS temporal stability cannot be assumed and should be regularly monitored as part of the sensor calibration process.

Integrating Traditional Ecological Knowledge and Remote Sensing for Monitoring Rangeland Dynamics in the Altai Mountain Region.

Integrating traditional ecological knowledge (TEK) with remote sensing capabilities to monitor rangeland dynamics could lead to more acceptable, efficient, and beneficial rangeland management schemes for stakeholders of grazing systems. We contrasted pastoralists' perception of summer pasture quality in the Altai Mountains of Central Asia with normalized difference vegetation index (NDVI) metrics obtained from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. The spatial relationship between satellite-based assessment of the grassland quality and on-the-ground evaluation by local herders was first assessed for a single year using 49, 1 × 1 km grassland blocks sampled in July 2013. Herder-derived forage value was positively and strongly (63% of variance explained) related to satellite-derived NDVI values (MODIS 1 km monthly data, MOD13A3) as well as field estimates of % vegetation cover (62% explained) and to a lesser degree to vegetation height (28% explained). Herders' multi-year perception (i.e., recall ability) was also contrasted with satellite observations of their herding areas over the period of 2006-2016 during which NDVI temporal anomaly explained >11% of variance in estimates of pasture quality recalled. Few herders in Kazakhstan could recall pasture conditions, most herders in Russia and China could but inconsistently (4 and 7% variation explained, respectively), whereas most herders in Mongolia could recall pasture conditions in strong agreement with NDVI anomaly (30% variation explained), patterns reflecting herders' regional dependence on herding as a livelihood. Corroboration of herder-derived estimates and satellite-derived vegetation indices creates opportunity for re-expression of satellite data in map form as TEK-derived indices more compatible with herder perceptions.

Biases and Abrupt Shifts of Monthly Precipitable Water from Terra MODIS

Monthly atmospheric precipitable water (PW) from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite was assessed over land at 60°S–60°N. MODIS provides two PW products by using infrared (IR) and near-IR (NIR) algorithms, respectively. An assessment was performed for both MODIS PW data from 2000 to 2014, comparing them with the measurements at international stations of the global positioning systems and with a reanalysis to detect abrupt changes through monthly variations. It is noted that MODIS IR systematically underestimated PW in over 75% of stations, and that PW estimation declines with time. MODIS NIR significantly overestimated PW for tropical land and experienced two abrupt shifts. These data defects result in large spurious decreasing trends in MODIS IR and increasing trends in MODIS NIR. The two MODIS PW products are currently not suitable for a climatic-trend analysis, highlighting the need for data reprocessing and calibration.

Estimation of biomass-burning emissions by fusing the fire radiative power retrievals from polar-orbiting and geostationary satellites across the conterminous United States

Biomass burning is an important source of atmospheric greenhouse gases and aerosols, and its emissions can be estimated using Fire Radiative Power (FRP) retrievals from polar-orbiting and geostationary satellites. Accurate and timely estimation of biomass-burning emissions (BBE) requires high-spatiotemporal-resolution FRP that is characterized by accurate diurnal FRP cycle. This study is to estimate hourly reliable BBE in a 0.25° × 0.3125° grid across the conterminous United States (CONUS) to be used in chemical transport models for air quality forecast. To do this, this study for the first time fused FRP retrievals from the Geostationary Operational Environmental Satellite (GOES) with those from Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 after GOES FRP was angularly adjusted and was further calibrated against MODIS FRP. The FRP data was obtained from Terra and Aqua MODIS 1 km active fire products with fire observations of four times a day and from 4 km GOES WF_ABBA (WildFire Automated Biomass Burning Algorithm) fire products for GOES-W (GOES-11 and 15) and GOES-E (GOES-13) with observations every 5–15 min across the CONUS from 2011 to 2015. The diurnal FRP cycles at an interval of 15 min for a grid were reconstructed using the ecosystem-specific diurnal FRP climatology and actually available MODIS-GOES fused FRP, which were applied to estimate hourly BBE across the CONUS. The results indicate that the reconstructed diurnal FRP cycle varied significantly in magnitude and shape among 45 CONUS ecosystems. The biomass burning released 717 Gg particulate matter smaller than 2.5 μm in diameter (PM2.5) in the CONUS each year; however, it presented significant temporal (diurnal, seasonal, and interannual) and spatial variations. Finally, the BBE estimates were evaluated using available data sources and compared well (a difference of ∼4%) with emissions derived from Landsat burned areas in the western CONUS and with hourly carbon monoxide emissions simulated using a biogeochemical model over the Rim Fire in California (difference < 1%). The BBE estimates showed similar seasonal variation to six available BBE inventories but with variable magnitude.

Planning lunar observations for satellite missions in low-Earth orbit

For Earth-observing satellites in low-Earth orbit, radiometric calibration of the sensors on-orbit is critical for maintaining consistent Earth-view (EV) retrievals as the mission progresses. Many of these satellite instruments use on-board calibration targets, EV sites, and observations of celestial targets in order to perform the sensor characterization. Among the celestial targets, the Moon is widely used across a range of Earth-observing instruments in order to perform radiometric calibration, spatial characterization, and sensor intercomparison. Since many of these instruments use satellite maneuvers in order to bring the Moon into view at a desired time, calculating the time and geometric parameters of the observations is vital for mission planning purposes. We develop a simple tool for planning such observations of the Moon and other celestial targets for instruments in low-Earth orbit based on the SPICE toolkit developed by the Navigation and Ancillary Information Facility at NASA. Given a set of satellite orbital data along with a definition of the instrument coordinates, the tool is designed to provide the timing of observations for an arbitrary view-port direction and a maneuver along an arbitrary axis relative to the spacecraft. The tool can be tested versus known lunar observations for the Aqua and Terra moderate resolution imaging spectroradiometer and the Suomi-NPP and NOAA-20 visible infrared imaging radiometer suite instruments for both roll and pitch maneuvers. We also perform simulations of lunar observations for different instrument configurations, orbits, and maneuver types in order to analyze the change in the potential lunar observations. Finally, we show a simple extension of the tool which can be used for identifying planet and star observations.

Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites

This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) MultiSpectral Instrument (MSI) and Sentinel 2B (S2B) MultiSpectral Instrument (MSI) from their respective launch date through 2018. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision.

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.

Northern Hemisphere contrail properties derived from Terra and Aqua MODIS data for 2006 and 2012

Abstract. Linear contrail coverage, optical property, and radiative forcing data over the Northern Hemisphere (NH) are derived from a year (2012) of Terra and Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) imagery and compared with previously published 2006 results (Duda et al., 2013; Bedka et al., 2013; Spangenberg et al., 2013) using a consistent retrieval methodology. Differences in the observed Terra-minus-Aqua screened contrail coverage and patterns in the 2012 annual-mean air traffic estimated with respect to satellite overpass time suggest that most contrails detected by the contrail detection algorithm (CDA) form approximately 2 h before overpass time. The 2012 screened NH contrail coverage (Mask B) shows a relative 3 % increase compared to 2006 data for Terra and increases by almost 7 % for Aqua, although the differences are not expected to be statistically significant. A new post-processing algorithm added to the contrail mask processing estimated that the total contrail cirrus coverage visible in the MODIS imagery may be 3 to 4 times larger than the linear contrail coverage detected by the CDA. This estimate is similar in magnitude to the spreading factor estimated by Minnis et al. (2013). Contrail property retrievals of the 2012 data indicate that both contrail optical depth and contrail effective diameter decreased approximately 10 % between 2006 and 2012. The decreases may be attributed to better background cloudiness characterization, changes in the waypoint screening, or changes in contrail temperature. The total mean contrail radiative forcings (TCRFs) for all 2012 Terra observations were −6.3, 14.3, and 8.0 mW m−2 for the shortwave (SWCRF), longwave (LWCRF), and net forcings, respectively. These values are approximately 20 % less than the corresponding 2006 Terra estimates. The decline in TCRF results from the decrease in normalized CRF, partially offset by the 3 % increase in overall contrail coverage in 2012. The TCRFs for 2012 Aqua are similar, −6.4, 15.5, and 9.0 mW m−2 for shortwave, longwave, and net radiative forcing. The strong correlation between the relative changes in both total SWCRF and LWCRF between 2006 and 2012 and the corresponding relative changes in screened contrail coverage over each air traffic region suggests that regional changes in TCRF from year to year are dominated by year-to-year changes in contrail coverage over each area.

Differences in monitoring vegetation dynamics between Moderate Resolution Imaging Spectroradiometer Collection 5 and Collection 6 vegetation index products on the Loess Plateau, China

ABSTRACTAccurately monitoring vegetation dynamics on the Loess Plateau (LP) is critical for evaluating the benefits of ecological restoration projects. The Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) product has been a primary data source for monitoring vegetation dynamics. However, MODIS Collection 5 (C5) VI products are known to be affected by sensor degradation, which has been addressed in the newly released MODIS Collection 6 (C6) VI products. Herein, we compared the spatiotemporal differences in vegetation dynamics between the Terra MODIS C5 and C6 data products and among different annual value retrieval methods for the LP during 2001–2016. Our results indicated a lower magnitude but a greener trend in the normalized difference vegetation index (NDVI), and areas with significant greening (p < 0.050) were found to increase by about 13%–16% from C5 to C6, depending on the retrieval method. Regions with either no particular trend or a downward trend in vegetation derived from the Terra-C5 NDVI mostly showed significant increasing trends based on the Terra-C6 NDVI. Moreover, the different retrieval methods also exhibited differences in the evaluation of vegetation dynamics, with the largest differences in terms of both magnitude and trend being identified with the annual maximum value method. This highlighted a compelling need to choose suitable methods in different regions for the retrieval of annual VI values, in order to facilitate more robust and comparable conclusions. Additionally, discrepancies also existed in the response of vegetation to climate variations between the Terra-C5 and C6 products for all three annual VI retrieval methods. Our findings, based on multiple products and analysis methods, may lead to improved understanding of both vegetation dynamics and their linkage to climate variables. The results suggest that caution be utilized when using only MODIS Terra-C5 products to evaluate vegetation dynamics and calibrate ecosystem models.

Multi-Time Scale Analysis of Regional Aerosol Optical Depth Changes in National-Level Urban Agglomerations in China Using Modis Collection 6.1 Datasets from 2001 to 2017

With the rapid development of China’s economy and industry, characterizing the spatial and temporal changes of aerosols in China has attracted widespread attention from researchers. The national-level urban agglomerations are the most concentrated areas of China’s economic, population and resource. Studying the spatial and temporal changes of aerosol optical depth (AOD) in these regions has practical guiding significance for effective monitoring of atmospheric particulate pollution. This paper analyzed the spatial and temporal variations of AOD in China’s urban agglomerations during 2001–2017 by using Terra Moderate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Level 2 aerosol products (MOD04_L2). Five national-level urban agglomerations were chosen: Yangtze River Delta (YRD), Pearl River Delta (PRD), Beijing-Tianjin-Hebei (BTH), Yangtze River Middle-Reach (YRMR) and Cheng-Yu (CY). We analyzed the change patterns of AOD in different urban agglomerations at multi-time scales and built a time series decomposition model to mine the long-term trend, seasonal variation and abnormal change information of AOD time series. The result indicated that averaged AOD values in the five urban agglomerations were basically increased first and then decreased at the annual time scale during 2001–2017. The averaged AOD showed strong seasonal differences and AOD values in spring and summer were typically higher than those in autumn and winter. At the monthly time scale, the AOD typically varied from low in cold months to high in warm months and then decreased during the rainy periods. Time series decompositions revealed that a notable transition around 2007–2008 dominated the long-term overall trend over the five selected urban agglomerations and an initial upward tendency followed by a downward tendency was observed during 2001–2017. This study can be utilized to provide decision-making basis for atmospheric environmental governance and future development of urban agglomerations.

Evaluation of MODIS and two reanalysis aerosol optical depth products over AERONET sites

This study selects 400 sites from Aerosol Robotic Network (AERONET) over 2002–2015 to develop an aerosol optical depth (AOD) evaluation dataset, which is used to evaluate two reanalysis AOD datasets (MERRAero and MERRA-2) from Modern-Era Retrospective analysis for Research and Applications (MERRA) projects, and satellite AOD datasets from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) products. Each AOD dataset is first evaluated individually, and then, a comparison of the MODIS, MERRAero and MERRA-2 datasets is conducted. The results show that the reanalysis MERRA-2 AOD dataset has a comparable accuracy (RMSE = 0.119, MBE = −0.008) with the satellite MODIS AOD datasets (RMSE = 0.110, MBE = 0.011). The MERRA-2 AOD dataset has similar spatial distributions with MODIS data and can capture the dynamic changes of AOD temporally. Compared to MERRAero, the MERRA-2 product improves, but it still slightly underestimates AERONET AOD in general. The MODIS AOD datasets perform differently compared to AERONET AOD due to different algorithms and spatial resolutions. Compared to AERONET AOD, the MODIS AOD datasets generated by the deep blue algorithm are negative biased, while the MODIS AOD datasets from dark target algorithm and the merged dataset are positively biased. The precisions of the Aqua MODIS AOD datasets are higher than the corresponding Terra MODIS AOD datasets.

Trends in MODIS and AERONET derived aerosol optical thickness over Northern Europe

Long-term Aqua and Terra MODIS (MODerate resolution Imaging Spectroradiometer) Collections 5.1 and 6.1 (c051 and c061, respectively) aerosol data have been combined with AERONET (AERosol RObotic NETwork) ground-based sun photometer observations to examine trends in aerosol optical thickness (AOT, at 550 nm) over Northern Europe for the months April to September. For the 1927 and 1559 daily coincident measurements that were obtained for c051 and c061, respectively, MODIS AOT varied by 86 and 90%, respectively, within the predicted uncertainty of one standard deviation of the retrieval over land (ΔAOT = ±0.05 ± 0.15·AOT). For the coastal AERONET site Gustav Dalen Tower (GDT), Sweden, larger deviations were found for MODIS c051 and c061 (79% and 75%, respectively, within predicted uncertainty). The Baltic Sea provides substantially better statistical representation of AOT than the surrounding land areas and therefore favours the investigations of trends in AOT over the region. Negative trends of 1.5% and 1.2% per year in AOT, based on daily averaging, were found for the southwestern Baltic Sea from MODIS c051 and c061, respectively. This is in line with a decrease of 1.2% per year in AOT at the AERONET station Hamburg. For the western Gotland Basin area, Sweden, negative trends of 1.5%, 1.1% and 1.6% per year in AOT have been found for MODIS c051, MODIS c061 and AERONET GDT, respectively. The strongest trend of –1.8% per year in AOT was found for AERONET Belsk, Poland, which can be compared to –1.5% per day obtained from MODIS c051 over central Poland. The trends in MODIS and AERONET AOT are nearly all statistically significant at the 95% confidence level. The strongest aerosol sources are suggested to be located southwest, south and southeast of the investigation area, although the highest prevalence of pollution events is associated with air mass transport from southwest.

Comparative Accuracy Assessment of Combined MODIS and NAAPS Aerosol Optical Depth with AERONET Data over North Africa

Aerosol is one of the important geophysical parameters that determine the earth’s radiation budget, energy balance and hydrological cycle. The “Deep Blue” Moderate Resolution Imaging Spectro-radiometer (MODIS) Aerosol Optical Depth (AOD) retrieval algorithm was designed to complement existing “Dark Target” Ocean and Land algorithms to be able to retrieve AOD over bright land surface. Using level 2 AOD data from five Aerosol Robotic Network (AERONET) stations over the study location of North Africa (0°S - 40°N, 30°W - 60°E), comparative accuracy assessments are made for combined MODIS AOD aboard Terra and Aqua satellites and US Navy Aerosol Analysis and Prediction System (NAAPS) forecast AOD data. The aerosol transport and vertical mixing over the region are investigated at different altitudes up to 3000 m above ground level using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). The MODIS validation result shows highest correlation in the Sub-Sahel (0.811) followed by Sahel (0.726) and then Sahara region (0.662). Furthermore, the combined retrieval algorithm of Terra and Aqua MODIS shows statistically significant discrepancies from AERONET AOD values in term of mean, t-test value, index of agreement and fractional error. The comparison of NAAPS predicted soil dust to AERONET AOD fared best in December to February (DJF) season for the Sahel region and June to August (JJA) season for the Sahara when the dust emission and transport are at the peak. However, median ratios of NAAPS to AERONET AOD indicated bias in some island sites in the Atlantic Ocean which may be due to the presence of sea salt over the site. The analysis carried out in this study reveals that both MODIS retrieval algorithm and NAAPS model could be improved by incorporating some local aerosol sources from the study area.