Terra Moderate Resolution Imaging Spectroradiometer Research Articles

Drought Monitoring Using Moderate Resolution Imaging Spectroradiometer-Derived NDVI Anomalies in Northern Algeria from 2011 to 2022

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans.

Spatiotemporal aerosol prediction model based on fusion of machine learning and spatial analysis

This study examined long-term aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify aerosol conditions on the Korean Peninsula. Time-series machine learning (ML) techniques and spatial interpolation methods were used to predict future aerosol trends. This investigation utilized AOT data from Terra MODIS and meteorological data from Automatic Weather System (AWS) in eight selected cities in Korea (Gangneung, Seoul, Busan, Wonju, Naju, Jeonju, Jeju, and Baengyeong) to assess atmospheric aerosols from 2000 to 2021. A machine-learning-based AOT prediction model was developed to forecast future AOT using long-term observations. The accuracy analysis of the AOT prediction results revealed mean absolute error of 0.152 ± 0.15, mean squared error of 0.048 ± 0.016, bias of 0.002 ± 0.011, and root mean squared error of 0.216 ± 0.038, which are deemed satisfactory. By employing spatial interpolation, gridded AOT values within the observation area were generated based on the ML prediction results. This study effectively integrated the ML model with point-measured data and spatial interpolation for an extensive analysis of regional AOT across the Korean Peninsula. These findings have substantial implications for regional air pollution policies because they provide spatiotemporal AOT predictions.

Integrating satellite and model data to explore spatial-temporal changes in aerosol optical properties and their meteorological relationships in northwest India

This study aims to analyze the temporal and spatial distribution of Aerosol Optical Properties across Northwest India using aerosol data from MODIS (Moderate Resolution Imaging Spectroradiometer) and OMI (Ozone Monitoring Instrument) sensors from 2003 to 2022. Therefore, this study investigated the decadal, interannual, and seasonal changes in aerosol optical properties, vegetation index, and meteorological parameters in the northwest Indian region (8 boxes). Using GIOVANNI (Goddard Earth Sciences Data and Information Services Center (GES DISC) Online Visualization and Analysis Infrastructure), we retrieved daily and monthly Aqua and Terra MODIS products of aerosol optical depth (AOD), Angstrom exponent (AE), normalized difference vegetation index (NDVI), and OMI aerosol index (AI) to examine the spatiotemporal variations by using statistical approaches. The results demonstrated that the decadal averages of aerosol properties showed values of AOD 0.35 (Aqua) and 0.34 (Terra) and AE 1.20 (Aqua) and 1.10 (Terra) with the highest levels during the post-monsoon. Notably, the mean interannual concentrations of AOD and NDVI consistently surpass 0.3, and AE and AI exceed 1 in most locations, underscoring the persistence of high aerosol loading. Also, the study revealed a negative decadal change in AOD of about −8.24 %, while AE, AI, and NDVI showed positive decadal changes of about 9.24 %, 15.09 %, and 12.67 %, respectively. In addition, aerosol optical properties and local meteorology strongly correlated (−0.8 to +0.8). Principal Component Analysis (PCA) identifies meteorological parameters as significant drivers, with the first three components explaining over 70 % of the variation in aerosol optical properties. The NOAA HYSPLIT trajectory model suggests that the long-distance dust transport from the Arabian Peninsula frequently penetrates Gujarat province and then to northwest India. The results contributed to air quality management strategies and provided valuable insights into regional climate and air quality with the influence of meteorology.

Improving the evapotranspiration estimation by coupling soil moisture and atmospheric variables in the relative evapotranspiration parameterization

Accurate monthly evapotranspiration (ET) estimation is essential for many forest, climate, and hydrological applications, as well as for some agricultural uses. In this study, the relationship between ET and relative evapotranspiration (F) using land surface, and atmospheric variables was assessed with 17 FLUXNET sites data in savanna, cropland, and forest land covers, distributed all over the world. A sigmoid (Fs) and a logarithmic (Fl) F expression were included in Walker et al.’s (2019a,b) equations to evaluate their impact on the accuracy of ET estimations. The new parameterizations of ET outperformed the original expression, showing root mean square errors lower than 24% of the mean observed ET. The results presented here suggest that atmospheric parameters, coupled with land explanatory variables included in F estimates, produce more precise ET estimations. In addition, Soil Moisture Active Passive (SMAP) products were used to obtain global maps of ET and compared with Global Landsurface Evaporation Amsterdam Methodology (GLEAM) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16 products, displaying the flexibility of these new parametrizations with different sources of data.

Spatiotemporal variation of snow cover days and influencing factors on the Loess Plateau during 2000–2019

Identifying the large-scale teleconnection is of great practical significance to comprehend the formation mechanism of snow cover days (SCDs) anomalies and improve the disaster risk management of ecological construction on the Loess Plateau. Based on Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover days datasets from 2000 to 2020, we analyzed the spatiotemporal variation of SCDs on the Loess Plateau. Meanwhile, we identified the variability in large-scale atmospheric patterns associated with SCDs on the Loess Plateau. The results show that: in the past 20 years have revealed an interdecadal fluctuations of SCDs on the Loess Plateau. Spatially, the SCDs experienced a significant decreasing trend (p < 0.05) in only 13.1% of the regions, which were distributed in the Northern Earth-Rock Mountain and Hully and Gully Loess Region. The effects of climate-related factor variations on SCD dynamics showed obvious spatial and monthly differences. Specifically, SCDs dominated by precipitation were significantly positively correlated in the north-eastern part of the Loess Plateau. Temperature plays a decisive role in SCD dynamics in the southern part of the Loess Plateau. Among the influencing factors, the East Asian Trough Intensity (EATI), the Asian Polar Vortex Area index (APVA) and the sea surface temperature (SST) anomalies in the NINO W region are three effective indictors of SCDs anomaly. In January, with the deepening of East Asian Trough and low SST in the NINO W region (0°∼10°N, 140°E∼180°E), East Asian winter monsoon is relatively strengthening, temperature is lower on the Loess Plateau, leading to more SCDs in the southern study region. The bigger APVA, corresponding to more snow accumulation in December, could effectively prolong the SCDs dominated by precipitation on the Loess Plateau. The conclusions of this study have implications for excavating early warning information of snow cover anomalies on the Loess Plateau.

Diurnal time representation of MODIS, VIIRS, MISR, and AHI over Asia and Oceania

Retrievals of satellite aerosol optical depth (AOD) products are used for studies of air pollution, energy balance, and climate change. However, few studies have evaluated the diurnal time representation (DTR) of satellite AOD products. In this study, we proposed a statistically valid threshold method to investigate the DTR of Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Multi-angle Imaging Spectroradiometer (MISR), and Advanced Himawari Imager (AHI) AOD products by comparing their retrievals to the daily AERONET AOD values and explored the ability of AHI and AERONET to capture daily aerosol variability at 54 sites in Asia and Oceania from July 2015 to December 2019. For polar-orbiting satellites, based on collocations with AERONET measurements, MISR-derived AOD showed high consistency with daily average AERONET AOD, with a correlation coefficient (R) of 0.94 and 80.85% matchups falling into the envelopes of expected error (EE, ± (0.05 + 0.15 × AODAERONET)). Compared with single MODIS products, a combination of satellite data from the Terra and Aqua MODIS dark target (DT) algorithm might obtain a dataset with better DTR (R = 0.94 and %Within EE = 72.10). The satellite-retrieved AOD did a good job to represent the daily mean AERONET AOD, because AERONET AOD averaged within a ± 30 min time window of different satellite overpasses closely mirrored the daily mean AERONET AOD. Furthermore, the seasonal average diurnal variations (SADV) of AERONET AOD were changing insignificant, except for the urban coverage area in the Southern Hemisphere and equatorial climate. For geostationary satellite, the DTR of AHI was relatively poorer in comparison to the other sensors (R = 0.84, %Within EE = 58.00), and AHI AOD was seriously underestimated in South Asia and overestimated in Oceania. Besides, the SADVs of AHI AOD presented several patterns summarized as wave type, unimodal type, stable fluctuation type, and other types, which meant that AHI AOD retrievals did not accurately capture the diurnal variability of aerosols. This study provides a reference for the aerosol community to select satellite AOD products with appropriate DTR.

The response and sensitivity of global vegetation to water stress: A comparison of different satellite-based NDVI products

Normalized Difference Vegetation Index (NDVI) is an effective proxy for vegetation activity and terrestrial productivity, and the widely used global NDVI products include the third generation Global Inventory Modelling and Mapping Studies Advanced Very High-Resolution Radiometer NDVI (NDVI3g), Satellite Pour l’Observation de la Terre VEGETATION NDVI (NDVIsp) and Terra Moderate Resolution Imaging Spectroradiometer NDVI (NDVItr). Here, for the first time, we investigated the differences between the three NDVIs in response and sensitivity to water stress represented by root zone soil moisture, vapor pressure deficit, self-calibrating Palmer Drought Severity Index and Standardized Precipitation Evapotranspiration Index across climate zones, humidity gradients, vegetation types and tree cover gradients. Over the temperate and arid climates region, the three NDVIs show higher consistent response fractions (CRFs) to water stress in the water-limited regions, with their sensitivity being in the order of NDVI3g < NDVIsp < NDVItr. The three NDVIs tend to exhibit inconsistent responses to water stress in the areas with higher humidity and tree cover, and their sensitivities to water stress fall as humidity and tree cover increase. In addition, the three NDVIs display higher CRFs and sensitivities to water stress over the non-forested areas. Our results also show that, in the cold climates region, the three NDVIs respond in the opposite way to water stress as that in water-limited areas, and NDVI3g has higher sensitivity to water stress than NDVIsp and NDVItr. This study highlights the differences between three NDVIs products for qualifying and quantifying their response and sensitivity to water stress.

Sentinel-3 SLSTR active fire (AF) detection and FRP daytime product - Algorithm description and global intercomparison to MODIS, VIIRS and landsat AF data

The Sea and Land Surface Temperature Radiometer (SLSTR) senses the Earth from onboard two concurrently operating European Copernicus Sentinel-3 (S3) satellites. As the Terra platform carrying the Moderate Resolution Imaging Spectroradiometer (MODIS) is reaching its end of life, the S3 Active Fire Detection and FRP products generated from data captured by S3 SLSTR are expected to soon become the main global active fire (AF) product for the mid-morning and evening low Earth orbit timeslots. The S3 night-time AF product issued by the European Space Agency (ESA) has been operational since March 2020, and here we report on the significant adjustments made to enable the generation of a complimentary daytime product. Similar to MODIS, SLSTR possesses two middle infrared channels, both a ‘standard’ (normal gain; S7) channel and a ‘fire’ (low-gain; F1) channel - but in contrast to MODIS by day even the ambient background land surface is often saturated in the SLSTR standard gain MIR (S7) channel. This saturation necessitates far greater use of the F1 channel data by day for active fire detection than by night, even though F1 has characteristics which make its data more challenging to combine with that from the other SLSTR thermal infrared channels. Here we report on the approaches used to combine S7 and F1 data for optimized daytime AF detection, and also detail the other algorithm adjustments found necessary to include in the daytime AF product algorithm. We compare the resulting daytime SLSTR AF product data to that generated from near-simultaneous views provided by MODIS onboard Terra. When both sensors detect the same active fire cluster at similar time, there is minimal bias shown between the two FRP retrievals (the ordinary least squares linear best fit between matched SLSTR and MODIS per-fire FRP matchups has a slope of 0.97). At the regional scale, the S3 product detects 70% of the AF pixels that the matching MODIS product reports, but also provides a further (16%) set of unique AF pixel detections. Regional FRP totals derived from SLSTR appear slightly lower than those from MODIS, and the OLS linear best fit between these regional FRP matchup datasets has a slope of 0.91. This is largely due to SLSTR performing less well in detecting the lowest FRP fires by day, whereas by night the S3 product performs a little better than MODIS due to the increased night-time use of S7 in the earlier AF pixel detection stages. Global fire mapping at a 0.25° grid cell resolution shows very similar daytime fire patterns and FRP totals from S3 and Terra MODIS, with SLSTR detecting around twice the number of AF pixels due to the algorithm being more effective at identifying low FRP pixels at the edges of fire clusters. Regional time series case studies also show very similar temporal patterns between S3 and Terra MODIS. Longer-term intercomparisons such as these will provide the knowledge necessary to use MODIS and SLSTR AF products together to analyse long-term AF trends. Comparing near simultaneous observations of fires by SLSTR and from the 30 m spatial resolution Landsat Operational Land Image (OLI) data, we find that once there are around 150 OLI active fire pixels detected within the area of an SLSTR pixel, the chances of that SLSTR pixel being classed as an active fire by the daytime algorithm rises to almost 100%. The daytime SLSTR AF Detection and FRP product based on the algorithm described herein has been fully operational since March 2022 and is available from the Sentinel-3 Science Hub (https://scihub.copernicus.eu/).

JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP

AbstractA three‐dimensional ensemble‐variational global aerosol data assimilation system based on the Joint Effort for Data assimilation Integration (JEDI) was developed for the Global Ensemble Forecast System‐Aerosols (GEFS‐Aerosols) coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model at the National Centers for Environmental Prediction. Aerosol mass mixing ratios in GEFS‐Aerosols were selected as control or analysis variables and were adjusted by assimilating 550 nm Aerosol Optical Depth (AOD) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite produced by the National Environmental Satellite, Data, and Information Service (NESDIS) at National Oceanic and Atmospheric Administration (NOAA). The original NOAA/NESDIS S‐NPP VIIRS Level 2.0 550 nm AOD retrievals were converted to JEDI Interface for Observation Data Access format. AOD forward operator and its tangent‐linear and adjoint models were implemented based on GOCART in JEDI Unified Forward Operator. A stochastically perturbed emission (SPE) approach was developed in the Common Community Physics Package‐based GEFS‐Aerosols to account for aerosol emission uncertainty. One‐month retrospective and three‐month near‐real‐time experiments consistently showed improved GEFS‐Aerosols analyses and forecasts from assimilating VIIRS 550 nm AOD retrievals against independent NASA Aqua and Terra Moderate Resolution Imaging Spectroradiometer AOD retrievals, Aerosol Robotic Network AOD, and independent AOD and aerosol analyses from NASA and European Centre for Medium‐Range Weather Forecasts. Through scaling and perturbing aerosol emissions, SPE enhanced ensemble error‐spread consistency and further improved AOD assimilation. The valid‐time‐shifting ensemble approach in a cost‐effective manner of populating background ensembles showed positive impacts on AOD assimilation.

Investigation of the Efficiency of Satellite-Derived LST Data for Mapping the Meteorological Parameters in Istanbul

Land surface temperature (LST) is an essential parameter for studying environmental and ecological processes and climate change at various scales. It is also valuable for studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. Since meteorological station data can provide a limited number of point data, satellite images that provide high temporal and spatial resolution LST data in large areas are needed to be used in all these applications. In this study, the usage of satellite-derived LST images was investigated in comparison with meteorological station data measurements in Istanbul, which has heterogeneous urban structures. LST data were obtained from Landsat 5 TM, Landsat 8 OLI/TIRS, and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images using the Google Earth Engine (GEE) cloud platform. The linear correlation analysis performed between Landsat LST and MODIS LST images gave a high correlation (r = 0.88). In the correlation analysis, hourly air temperature and soil temperature meteorology station data provided by the State Meteorological Service and LST values obtained from images taken from Landsat TM/TIRS and Terra MODIS were used. The correlations between air temperatures and Landsat LST ranged from 0.47–0.95 for 1987–2017 to 0.44–0.80 for MODIS LST for 2000–2017. The correlations between 5 cm soil temperatures and Landsat LST ranged from 0.76–0.93 for 2009–2017 to 0.22–0.61 for MODIS LST 2000–2017. In addition, linear regression models produced with meteorological parameters and LST values were applied to 2022 LST maps to show the spatial distribution of these parameters, and then, accuracy analyses were made.

An Integrated Method for the Generation of Spatio-Temporally Continuous LST Product With MODIS/Terra Observations

Land surface temperature (LST) is a crucial parameter in the study of Land Surface processes. Currently, there are great progresses in LST retrieval based on thermal infrared (TIR) remote sensing. However, TIR-based LST suffers from serious spatial discontinuities due to clouds. Although there are methods developed to address this issue, the methods show high uncertainty in days with extremely clouds. Therefore, this study proposed an integrated method to reconstruct cloudy LSTs using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the China Land Data Assimilation System (CLDAS) LST. This method was separated into two parts according to the ratio of clear-sky pixels (RCP). On days with RCP more than 30%, a random forest reconstruction method was used to establish the complicated relationship between LST and its predicting variables, including solar radiation factor, vegetation index, water index, topographic information and latitude, and then applied to cloudy pixels to derive LSTs. For the rest days, the CLDAS LST was selected to assist the reconstruction via downscaling it to 1 km and then merged with clear-sky data to generate spatially continuous results. The proposed method was applied to the Southwest China and generate daily LST product in 2019. Validation with ground measurements demonstrated a high accuracy with the correlation coefficient changing from 0.73 to 0.88. Additionally, the reconstructed LST dataset exhibits similar temporal variability as existing all-weather satellite-based and reanalysis LST products. The findings reveal that this method shows good potential in generating gap-free LST dataset, especially for the mountain regions with heavy clouds.

Combining Remote Sensing and Space-Time Analysis for Desertification Monitoring in the Semiarid Dryland of Nigeria

Desertification has been identified as the resultant effect of dryland loss. Desertification is catalysed by anthropogenic modifications and variations in environmental/climatic conditions. The situation in Nigeria is further exacerbated by the growing demand for land by the population. To this effect, this study performed a space-time analysis of vegetative cover between 2001 and 2020 to unravel patterns and trends across the semiarid region of the dryland system in Nigeria. The dynamics during the rainy season (May and September) were examined using the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) dataset subjected to space-time analysis. Generalised Difference Vegetation Index (GDVI) was computed to the power of 2 to quantify vegetative cover across the study area. The results showed that the average of the GDVI ranges between -0.40 and 0.94, with a standard deviation of 0.11. Time series cluster analysis revealed that there are two temporal clusters: (1) no statistically significant trend (Statistics= 1.33, p-value = 0.18) and (2) statistically significant downtrend (Statistics = -2.37, p=0.02), with cluster 1 covering 95% of the areas examined. The most dominant (97% of the area) emerging space-time pattern was cold-spots (persistent, diminishing, sporadic, oscillating, and historical types). In conclusion, most of the areas showed no definite temporal pattern of vegetation pattern during the period, while more than 90% of the areas have witnessed a decline in vegetative cover. There is a need for a more coordinated approach to desertification control, constant monitoring is pertinent while new approaches to restoring degraded land are recommended.

Evaluation of earth observation datasets for LST trends over India and its implication in global warming

Land surface temperature (LST) is crucial in surface energy balance, urban climatology, intensifying global change, ecological and environmental concerns. The present study examined the LST trends and spatio-temporal variation over India from 2002 to 2022. This includes comparison of LST for the summer and winter seasons over two decades. Secondly, the present study examined the LULC category wise LST variability during the day and night-time using MODIS (The Terra Moderate Resolution Imaging Spectroradiometer) derived products. This study explored the feasibility of cloud computing for big data analysis for LST distribution in seven landuse categories over India, providing a conceptual response to global warming. Results showed the existence of spatial LST variation due to changes in land-use patterns and MODIS derived vegetation indices- NDVI. Daytime LST for the summer and winter seasons of 2002 was found to be 45.17 °C and 39.13 °C, respectively. Outcomes illustrate declining trends in range (LSTmin-LSTmax) for winter seasons, initially, it was observed as 56.29 °C for 2002 while later on it was observed to be 20.21 °C and 20.87 °C for 2021 and 2022 respectively. The LSTmin (summer) has shown an increasing trend towards upper LST values, from −3.01 °C to 12.21 °C from 2002 TO 2022. LSTmin_winter has shown a rising trend towards the upper LST values from −17.16 °C to 9.15 °C in 2022. The maximum LST for the DRs was observed to be 61.56 °C, followed by UR as 56.24 °C. The findings demonstrate that daytime LSTmin and LSTmax are found to be 19.50 °C to 56.24 °C for UR, 29.5 °C to 61.56 °C for DR, 19.24 °C to 54.08 °C for SAR, −21.1 °C to 0.05 °C for SCR and 15 °C to 32.18 °C for FHR. NDVI-LST (daytime, nighttime and diurnal temperature range) feature space generates an obtuse triangle and depicts a negative correlation of vegetation for a few LULC categories. The outcomes indicated that desert and snow regions have highest LSTmax followed by urban and semiarid regions during daytime. During the nighttime, desert and urban regions have the highest temperature followed by semi-arid and forest regions. The outcomes support the efficacy of earth observation datasets and help to facilitate a better understanding of LULC and its impact on regional climate.

Leveraging remotely-sensed vegetation indices to evaluate crop coefficients and actual irrigation requirements in the water-stressed Maipo River Basin of Central Chile

Assessment of irrigation needs for agricultural production has commonly relied on reference crop coefficients derived using limited climatic conditions. This work focused on evaluating actual crop coefficients (Kc) by leveraging available satellite data from Landsat 8 Operational Land Imager (OLI) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). This method built on the linear relationship and variability between reference Kc from the Food and Agricultural Organization (FAO) and specific site response in Normalized Difference Vegetation Index (NDVI). A regional model was developed for the water-stressed Maipo River Basin of Central Chile during the growing seasons of maize from 2018 to 2022. Throughout model validation, NDVI-based Kc predictions of high quality were found with R-squared scores ranging from 0.77 to 0.97. Relying on Earth observations, this approach provided site-specific Kc values and estimates of actual evapotranspiration without requiring site instruments and/or particular knowledge of farming schedules. Overall, this study has the potential to assist individuals and organizations around the world in improving irrigation management approaches, and advance precision agriculture in the context of water scarcity.

A Retrospective Satellite Analysis of the June 2012 North American Derecho

The North American Derecho of 29–30 June 2012 exhibits many classic progressive and serial derecho features. It remains one of the highest-impact derecho-producing convective systems (DCS) over CONUS since 2000. This research effort enhances the understanding of the science of operational forecasting of severe windstorms through examples of employing new satellite and ground-based microwave and vertical wind profile data. During the track of the derecho from the upper Midwestern U.S. through the Mid-Atlantic region on 29 June 2012, clear signatures associated with a severe MCS were apparent in polar-orbiting satellite imagery, especially from the EPS METOP-A Microwave Humidity Sounder (MHS), Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager Sounder (SSMIS), and NASA TERRA Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, morning (descending node) and the evening (ascending node) METOP-A Infrared Atmospheric Sounding Interferometer (IASI) soundings are compared to soundings from surface-based Radiometrics Corporation MP-3000 series microwave radiometer profilers (MWRPs) along the track of the derecho system. The co-located IASI and MWRP soundings revealed a pre-convective environment that indicated a favorable volatile tropospheric profile for severe downburst wind generation. An important outcome of this study will be to formulate a functional relationship between satellite-derived parameters and signatures, and severe convective wind occurrence. Furthermore, a comprehensive approach to observational data analysis involves both surface- and satellite-based instrumentation. Because this approach utilizes operational products available to weather service forecasters, it can feasibly be used for monitoring and forecasting local-scale downburst occurrence within derecho systems, as well as larger-scale convective wind intensity associated with the entire DCS.

Terra and Aqua MODIS collection 7 level 1B algorithm

Moderate resolution imaging spectroradiometer (MODIS) continues to be an important instrument for NASA’s earth observing system. Terra and Aqua MODIS have produced more than 22 and 20 years of global datasets that have significantly helped scientists better understand the Earth’s systems, respectively. The MODIS level-1B (L1B) algorithms use the uncalibrated, geolocated Earth scene observations as input and convert the instrument response into calibrated reflectance and radiance, which are used to generate the downstream science products. The sustained calibration and characterization activities undertaken by the MODIS characterization support team have resulted in several upgrades to the L1B algorithms to maintain accurate calibration in the data products. We present an overview of the L1B algorithm designated as Collection 7. Various algorithm enhancements both in the reflective bands and thermal bands characterization are currently under science testing and evaluation. Once applied in data processing (projected in early 2023), they are expected to manifest in improved science products, both in terms of radiometric accuracy and long-term stability.

Performance of MODIS Deep Blue Collection 6.1 Aerosol Optical Depth Products Over Indonesia: Spatiotemporal Variations and Aerosol Types

This study aims to evaluate the performance of the long-term Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Collection 6.1 (C6.1) in determining the spatiotemporal variation of aerosol optical depth (AOD) and aerosol types over Indonesia. For this purpose, monthly MODIS DB AOD datasets are directly compared with Aerosol Robotic Network (AERONET) Version 3 Level 2.0 (cloud-screened and quality-assured) monthly measurements at 8 sites throughout Indonesia. The results indicate that MODIS DB AOD retrievals and AERONET AOD measurements have a high correlation in Sumatra Island (i.e., Kototabang (r = 0.88) and Jambi (r = 0.9)) and Kalimantan Island (i.e., Palangkaraya (r = 0.89) and Pontianak (r = 0.92)). However, the correlations are low in Bandung, Palu, and Sorong. In general, MODIS DB AOD tends to overestimate AERONET AOD at all sites by 16 to 61% and can detect extreme fire events in Sumatra and Kalimantan Islands quite well. Aerosol types in Indonesia mostly consist of clean continental, followed by biomass burning/urban industrial and mixed aerosols. Palu and Sorong had the highest clean continental aerosol contribution (90%), while Bandung had the highest biomass burning/urban-industrial aerosol contribution to atmospheric composition (93.7%). For mixed aerosols, the highest contribution was found in Pontianak, with a proportion of 48.4%. Spatially, the annual mean AOD in the western part of Indonesia is higher than in the eastern part. Seasonally, the highest AOD is observed during the period of September–November, which is associated with the emergence of fire events.

Characterization of the on-orbit response versus scan angle for Terra MODIS SWIR bands in Collection 7

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua platforms has 20 reflective solar bands (0.4 to 2.1 μm), for which the on-orbit detector gain changes are tracked using the on-board solar diffuser (SD) and SD stability monitor. SD and lunar observation data, supplemented by Earth-view (EV) observations for some bands, are used to characterize the scan-mirror response versus scan angle (RVS). The short-wave infrared (SWIR) bands (1.2 to 2.1 μm) have issues, such as electronic crosstalk and an out-of-band optical leak from the thermal bands, which complicate the use of the Moon for reliably characterizing the on-orbit RVS. Before Collection 7 (C7), the pre-launch RVS was deemed sufficient for both Aqua and Terra MODIS SWIR bands throughout the missions. However, recent studies have found that the RVS behavior does change with time in some SWIR bands, up to 2% and 1% for Terra MODIS bands 5 and 26, respectively. We assess the residual RVS effects present in the current MODIS calibration at desert sites and deep convective clouds (DCC). Due to the large uncertainties caused by the water vapor absorption effects from desert sites, especially for band 26 (1.375 μm), EV observations from DCCs are chosen to characterize the on-orbit RVS behavior for the SWIR bands. A time-dependent RVS characterization method for the SWIR bands is described and assessed by comparing the results with Collection 6.1 (C6.1) Level 1B (L1B) reflectance products. This new algorithm will be used in the upcoming C7 L1B reprocessing for Terra MODIS, providing more stable and accurate SWIR band reflectance data over time and across the scan range compared with the C6.1 results. For Aqua MODIS SWIR bands, performance evaluations with desert and DCC data indicate stable performance, so the pre-launch RVS will continue to be used in C7.

Long-term variations and comparison of aerosol optical properties based on MODIS and ground-based data in Thailand

Comparison and analysis of Moderate Resolution Imaging Spectroradiometer (MODIS), Aerosol Robotic Network (AERONET), and SKYNET aerosol optical depth (AOD) products were investigated at four AERONET and two SKYNET stations in Thailand from 2006 to 2020. AERONET Level 2.0 and MODIS Deep Blue (DB) Collection 6.1 data were applied. Aqua and Terra MODIS AODs (satellite AODs) were compared to AERONET and SKYNET AODs (ground based AODs). Ground-based and satellite AODs were in good agreement, giving a high correlation (R2 = 0.75) in Ubon Ratchathani, associated with biomass burning. The long-term trend in AOD was analyzed. Both satellite and ground based AODs demonstrate increasing trends in AOD over Thailand associated with the urbanized area and traffic congestion. Aerosol particles are categorized into dust, sea salt, and biomass burning, containing fine and coarse modes. A variation between satellite data measurements depended on surface reflectances, such as the large water body surrounding (Songkhla) and the impact of surface albedo (Chiang Mai). Higher AOD values were observed between February to April. In addition, meteorological parameters are directly related to aerosol concentrations, while no significant connection could be developed in the observed values between Angstrom exponent (AE) and Meteorological parameters. Meteorological parameters and temperature inversion more influenced AOD concentrations in February. Nevertheless, the hypothesis showed 95% confidence interval of AODs retrieved from satellite data fell within the confidence range. Therefore, satellite aerosol products are sufficiently accurate for estimating aerosol climatology.

Satellites Suggest Rising Tropical High Cloud Altitude: 2002–2021

AbstractTropical upper‐troposphere clouds are expected to rise under global warming, contributing a positive radiative feedback. Here we show that Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals over 2002–2021 from the Terra and Aqua satellites report a tropical‐mean high‐cloud altitude increase of 6.9 ± 2.7 m yr−1 (±2σ), while atmospheric models report 5.6 ± 3.0 m yr−1 over 1979–2014. During their common overlap period, however, the observed trend is greater than that of the models. The atmospheric models also show a tropically confined cloud‐height response to El Nino, whereas the observed cloud height response peak extends into the subtropics. Poleward of the mid‐latitudes, Aqua and Terra MODIS have cloud‐height drifts relative to each other, highlighting the need for deeper investigation of potential time‐dependent biases in MODIS cloud retrievals. Both sensors report significant tropical cloud height increases since 2002, which are statistically consistent with those simulated by climate models.