Moderate Imaging Spectroradiometer Research Articles

Evaluating the relationship of vegetation dynamics with rainfall and land surface temperature using geospatial techniques in South Wollo zone, Ethiopia

This study was conducted in the South Wollo zone to assess vegetation dynamics and their relationship with rainfall and land surface temperature. To that end, 16-day 250-meter resolution Moderate Imaging Spectro-radiometer (MODIS) Normalized difference vegetation Index (NDVI), 5-kilometer resolution MODIS Land Surface Temperature (LST), 5-kilometer CHIRPS data were used. A Mann-Kendall (MK) trend test was used to determine the trend of each dataset using seasonal and annual time series. In addition, the Pearson correlation coefficient was used to estimate the relationship between NDVI and climatic elements. The results of the study revealed that there was no significant change in the annual and seasonal rainfall and LST during the period 2000 to 2021, except LST in the autumn season (p = 0.01) and NDVI was significant only in the mean annual (p = 0.027) and summer seasons (p = 0.03). The correlation between NDVI and rainfall was positive (r = 0.84, 0.78, 0.72 & 0.79) and obtained for the summer, autumn, winter, and spring season respectively. Similarly, the correlation between NDVI and LST was negative (r= -0.79, -0.75, -0.72, and -0.78) in the summer, autumn, winter, and spring seasons respectively. The outcome of the study indicates precipitation is the most significant factor in the distribution of vegetation and LST

Long-term climate change and anthropogenic activities together with regional water resources and agricultural productivity in Uganda using Google Earth Engine

Uganda with its fragile ecosystem, large-scale human activities, and increasing population pressure, all of which combined, make this region increasingly susceptible to climate variation. This study examined the long-term trends of annual, seasonal, and monthly distributions of rainfall and temperature from 2001 to 2021 together with crop-wise agricultural productivity. For the analysis, we obtained CHIRPS-V2.0 (Climate Hazards Group InfraRed Precipitation with Station Data version 2.0) rainfall, Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), DMSP nighttime lights, ESA land cover attribution, and international crop production assessment records. Subsequently, several non-parametric statistical applications were applied to check the long-term spatio-temporal trends of climate parameters and their inter-relationship at higher significance using the Google Earth Engine platform. The investigation reveals an annual increase in LST, averaging 0.01 °C/year along with decreasing rainfall (1.89 mm/year). However, regional climate trends are largely elevation-dependent, which are predominantly subjected to the northern part of the study area witnessing a slight decrease in LST and thereby increased rainfall. Moreover, the long-term spatial nexus estimation divulges a potent inverse association between rainfall and temperature in the north and northeastern regions of the study area. Concurrently, changing patterns also have led to a decline in crop production and deterioration in water availability, which is accompanied by various other abnormalities, including the scarcity of water resources and anthropogenic activities. Changing climate has had significant implications on crop production, largely on corn and soybean as long-term shifts influence it in average rainfall and temperature, yearly fluctuations, and disturbances during various growth stages.

Quantifying climate variation and associated regional air pollution in southern India using Google Earth Engine

Climate change and regional air pollution have had significant proportional coherence and are collectively hazardous for the regional ecosystem. To conduct this present investigation, we obtained high-resolution remotely sensed datasets from 2001 to 2022. To estimate climate variation, we utilized Climate Hazard Group InfraRed Precipitation with Station Data Version 2.0 (CHIRPS) and Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). Additionally, we used Sentinel-5P datasets to collect spatio-temporal information for regional CO (Carbon Monoxide), NO2 (Nitrogen Dioxide), SO2 (Sulfur Dioxide), and UV Aerosol index for Coimbatore city. Numerous non-parametric and descriptive statistical applications were then employed to check the spatial integrity of satellite data products and spatio-temporal trends using Google Earth Engine algorithms. The study reveals most of the southern parts of Coimbatore city witnessed increased LST (0.10 °C/year) together with decreased rainfall (21.5 mm/year). Moreover, regional concentration of air pollutants exhibits spatio-temporal variability at annual and seasonal scales, where maximum engrossment is occupied by CO during the pre-monsoon and monsoon season. However, other pollutants are also dominant in the northern parts of the city, whereas NO2 and absorbing Aerosol during pre-monsoon season experienced significant increase throughout the years. Understanding the fluctuations in air pollution levels across different weather situations might help in developing targeted pollution reduction methods.

Comparing Phenology of a Temperate Deciduous Forest Captured by Solar-Induced Fluorescence and Vegetation Indices

A shifting phenology in deciduous broadleaf forests (DBFs) can indicate forest health, resilience, and changes in the face of a rapidly changing climate. The availability of satellite-based solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) promises to add to the understanding of the regional-level DBF phenology that has been developed, for instance, using proxies of gross primary productivity (GPP) from the Moderate Imaging Spectroradiometer (MODIS). It is unclear how OCO-2 and MODIS metrics compare in terms of capturing intra-annual variations and benchmarking DBF seasonality, thus necessitating a comparison. In this study, spatiotemporally matched OCO-2 SIF metrics (at footprint level) and corresponding MODIS GPP, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) products within a temperate DBF were used to compare the phenology captured by the productivity metrics. Additionally, an estimate of the SIF yield (SIFy), derived from OCO-2 SIF measurements, and a MODIS fraction of photosynthetically active radiation (fPAR) were tested. An examination of the trends and correlations showed relatively few qualitative differences among productivity metrics and environmental variables, but it highlighted a lack of seasonal signal in the calculation of SIFy. However, a seasonality analysis quantitatively showed similar seasonal timings and levels of seasonal production in and out of the growing season between SIF and GPP. In contrast, NDVI seasonality was least comparable to that of SIF and GPP, with senescence occurring approximately one month apart. Taken together, we conclude that satellite-based SIF and GPP (and EVI to a smaller degree) provide the most similar measurements of forest function, while NDVI is not sensitive to the same changes. In this regard, phenological metrics calculated with satellite-based SIF, along with those calculated with GPP and EVI from MODIS, can enhance our current understanding of deciduous forest structures and functions and provide additional information over NDVI. We recommend that future studies consider metrics other than NDVI for phenology analyses.

Regional climate trend analyses for Aqua MODIS land surface temperatures

ABSTRACT Land surface temperature (LST) is the skin surface temperature of the Earth’s land surface and is an essential climate variable (ECV) that critically characterizes the Earth’s climate and surface energy processes. This is the first regional trend analysis for LST with uncertainties, using a stable LST climate data record suitable for climate trend analyses: the Aqua Moderate Imaging Spectroradiometer (MODIS) dataset (MYDCCI) produced in the European Space Agency (ESA) LST Climate Change Initiative (LST_cci) Project. The quality of the trends is verified here for Western Europe, for which consistent trends per decade of 0.5 K and 0.57 K across daytime and night-time data, respectively. Eight regions representative of a range of biomes and, including the Amazon, Western U.S.A., Greenland, the Sahel, Siberia, China, India, and Australia, have been analysed. Our most significant findings show substantial LST increases for Siberia and the Amazon, as well as evidence of recent increasing trends for the Western U.S.A. Siberia showed the most substantial daytime and night-time changes per decade of +0.87 K and +0.93 K, respectively. Furthermore, the Amazon and Western U.S.A. showed significant daytime LST trend increases of +0.82 K and +0.51 K, respectively. Western Europe and Western U.S.A. show the largest night-time change of +0.57 K and +0.54 K, respectively. India presents an atypical time series with a decreasing daytime and increasing night-time trend per decade of −0.62 K and +0.44 K, respectively, consistent with reported air temperatures. Overall the results show significant positive regional LST trends, against a background of both strong seasonal cycles and intermittent disruptive events, which demonstrate the importance of continued LST observations. The results also incentivize remote sensing science to derive ever more rigorous LST datasets and to continue to investigate emissivity and error correlations to allow improved trend analyses for smaller regions and individual locations.

China's oases have expanded by nearly 40% over the past 20 years

AbstractOases are supporters of biological, social, and economic activities in arid regions, which guides their prosperity or decay. Given that China has the largest oases area in the world, determining their evolution is of substantial importance for sustainable development and the ecological protection of these systems. Accordingly, the spatiotemporal evolution of China's oases between 2000 and 2020 was analyzed. This was based on the normalized difference vegetation index derived from moderate imaging spectroradiometer data, combined with superpixel image segmentation, the region adjacency graph merging method, and the genetic algorithm. The results were as follows. Between 2000 and 2020: (1) the oases in China expanded significantly from 207,812 to 289,751 km2; with a total increase of 39.43%; and the number of oases decreased from 135 to 93, which mainly occurred in micro and small oases; (2) the areas of oases in stable, expanding, and shrinking states accounted for 96.77%, 42.65%, and respectively 3.23% of the total oasis area in 2000. Oases have become more concentrated and continuously expanded; (3) the increase in glacial meltwater and precipitation caused by rising temperatures increased the availability of water resources in oases, which provides the substantial basis for expansion; and (4) the primary anthropogenic drivers of oasis expansion and stabilization regions were cultivation and construction, respectively.

Aerosol Distributions and Transport over Southern Morocco from Ground-Based and Satellite Observations (2004–2020)

The present study investigates the optical properties of aerosols on daily and seasonal scales with the use of the aerosol optical depth (AOD) and Angström exponent (AE) data retrieved from AErosol RObotic NETwork (AERONET) and collected at four stations in Southern Morocco—Saada (31.63° N; 8.16° W), Ouarzazate (30.93° N; 6.91° W), Oukaïmeden (31.21° N; 7.86° W) and Ras-El-Aïn (31.67° N; 7.60° W). An evaluation of the aerosol volumetric size distribution (AVSD) is also obtained for Saada and Ouarzazate. An AOD inter-comparison is performed between AERONET data and satellite sensors (MODerate resolution Imaging Spectroradiometer—MODIS), as well as assimilation products (Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)), by the means of a linear regression. Regardless of site location and elevation, the results show the prevalence of the annual cycle of AOD, with a maximum in summer and a minimum in winter. In association with this seasonal variation, the variations in AE and AVSD showed an increase in coarse mode over Ouarzazate and Saada during summer (July to August), underlining that Southern Morocco is prone to the regular transport of desert dust on a seasonal basis. The inter-comparison reveals that the MERRA-2 dataset is slightly more appropriate for the study region, since it shows correlation coefficients (r) ranging from 0.758 to 0.844 and intercepts ranging from 0.021 to 0.070, depending on the study site. The statistical analysis of the back-trajectories simulated by the HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model were consistent with the observations and confirmed the dominance of desert dust aerosols during the summer over the study region. On the other hand, the winter season reveals a predominance of anthropogenic and oceanic aerosols originating from the north and the west of the study site.

Hubungan Klorofil-a dan Suhu Permukaan Laut (SPL) Terhadap Hasil Tangkapan Ikan Lemuru Sardinella lemuru di Selat Bali Menggunakan Citra Aqua MODIS Tahun 2009-2018

Oceanographic parameters such as sea surface temperature (SST) and chlorophyll-a in the water can influence the productivity of pelagic fish such as Lemuru. Many studies have been conducted on the relationship between SST and chlorophyll-a and the fluctuations in the catch of Lemuru fish, especially using satellite databased sensing technology. Most of the research using satellite data is done in a short-term period (not climatology), so there are still a lot of unanswered questions. One of them is: how the correlation between the fluctuation in the number of Lemuru fish catches is related to SST and chlorophyll-a in the ENSO and IOD periods. The purpose of this study is to determine the distribution of SST and chlorophyll-a concentrations and their relationship with the yield of Lemuru fish catches in the Bali Strait from 2009 to 2018. Aqua Moderate Imaging Spectroradiometer (MODIS) satellite data level 2 from Oceancolor and data on the number of fish caught in the Bali Strait from the National Fisheries Port (PPN) Pengambengan and UPT PPP Muncar. The method is used with correlation analysis between SST and chlorophyll-a to the number of Lemuru fish caught. The monthly average SST in Bali Strait waters from 2009 to 2018 was approximately 24,76 0C-30, 11 0C, with an estimated monthly chlorophyll-a concentration of 0,10 mg/m3-3,68 mg/m3. The spatial distribution of SST tends to be low in the southern Bali Strait and tends to be high in the northern Bali Strait. The spatial distribution of chlorophyll-a appears to have a high value in the near land area. The cross-correlation test between chlorophyll-a and the number of Lemuru catches showed a positive correlation yield at the 4th month lag. While the correlation of SST to the number of Lemuru fish catches shows a positive correlation at lag (-1).

Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area

Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVIMccL and EVI2MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.

Evaluation and comparison of MODIS and VIIRS aerosol optical depth (AOD) products over regions in the Eastern Mediterranean and the Black Sea

Aerosol loading has direct and indirect impacts on radiative forcing and climate; additionally, exposure to high aerosol concentrations has adverse effects on human health. Several inversion algorithms are used to retrieve aerosol properties, primarily aerosol optical depth (AOD). Satellite aerosol products are available from several moderate spatial resolution sensors and provide information on the spatial and temporal dynamics of aerosol concentration. This study provides an evaluation and inter-comparison of multiple aerosol products from Terra and Aqua Moderate Resolution and Imaging Spectroradiometer (MODIS) and Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible-Infrared Imager Radiometer Suite (VIIRS) between 2014 and 2018 in the Eastern Mediterranean and the Black Sea. This evaluation focuses on four MODIS aerosol products including Dark Target (DT) AOD retrievals at 10-km and 3-km spatial resolutions, Deep Blue (DB) AOD retrievals at10-km spatial resolution, Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals at 1-km spatial resolution, and VIIRS DB AOD retrievals at 6-km spatial resolution. Satellite-based AOD retrievals are validated against in-situ AOD measurements from three AERONET ground stations located in urban/land, rural/coastal, and ocean surfaces.The results show that Pearson's correlation coefficient (r) between the satellite AOD retrievals and AERONET AOD measurements range between 0.45 and 0.93 whilst the Root-Mean-Squared Errors (RMSE) varies from 0.047 to 0.159 which highlights the reliability of satellite AOD retrievals' in the region. The best performing products over the urban/land surface are the MODIS MAIAC and VIIRS DB aerosol products with the RMSE of 0.048–0.061 and a higher percentage (81%–89%) of retrievals falling within expected error for land. Over the ocean, the MODIS DT aerosol products have the lowest errors (RMSE 0.047–0.061) and the highest percentage (70%–80%) of retrievals falling within the expected error. The performances of all algorithms over the rural/coastal region are poorer in terms of over- and under-estimations of AOD with a small percentage of retrievals (48%–87%) within expected error. Among the satellite aerosol products, the VIIRS aerosol product performs better over the coastal area in terms of high correlation coefficient (r = 0.88), low error (RMSE = 0.059), and the large percentage of retrievals within expected error (88%). The poor performance found over the more complex coastal regions suggests further improvements are needed in land-water-sediment masks, surface reflectance estimation, and aerosol model assumptions over mixed surface types.The aerosol products show the highest agreement (r ∼ 0.77–0.98) over the ocean surface whilst the coastal site aerosol products verified the greatest dissimilarity in aerosol loading variations (r ∼ 0.50–0.91). Lastly, the good correlation (r > 0.73) between the VIIRS and MODIS AOD products illustrates the potential of VIIRS to provide aerosol data continuity with MODIS.

A new fog and low stratus retrieval for tropical South America reveals widespread fog in lowland forests

Fog-driven epiphyte-rich tropical cloud forests were long believed to be restricted to mountainous regions. Recent studies have shown the occurrence of such forest types in the lowlands of French Guiana, where early morning radiation fog was shown to modify the water and energy cycles at the canopy level of the tropical lowland rain forest (Gehrig-Downie et al., 2012; Obregon et al., 2011). Since this newly discovered Tropical Lowland Cloud Forest (TLCF) harbours a unique biodiversity comparable to mountain cloud forests, knowledge of its spatial distribution across tropical lowland forests is of large ecological interest, but so far fully unknown. A prerequisite to detect potential TLCF stands is knowledge on the spatial occurrence of fog/low stratus clouds (FLS) over longer time scales, indicating fog frequency. We address this need on a continental scale by presenting the first spatially-explicit, high-resolution product on nocturnal FLS occurrence over the entire tropical lowland rain forest area of South America. The product is based on a new FLS retrieval scheme that uses brightness temperature (BT) information of night-time satellite images acquired by the Aqua Moderate Imaging Spectroradiometer (Aqua MODIS). Since landforms substantially influence small-scale FLS development, we used a dynamic threshold technique depending on the spatially variable subpixel information on landform suitability for FLS generation. The product was successfully validated against in-situ visibility measurements combined with a plausibility analysis based on the published but scarce fog observations over the Amazon. The independence of the new algorithm from measured total precipitable water (TPW) is an improvement over existing procedures that are limited to the availability of these ancillary data. The FLS frequency map for the entire tropical lowland forest of South America derived from over 52,000 scenes (18 years from 2003 to 2020 inclusive) indicates that FLS is widespread, though spatially differentiated, throughout the tropical lowland forests of South America, providing suitable environmental conditions for the TLCF. This knowledge on the spatial distribution of potential TLCF is of major importance for conservation efforts. The new FLS scheme is applicable to all tropical lowland forests and can therefore support global conservation efforts of the valuable TLCF ecosystems.

Eutrophication and lakes dynamic conditions control the endogenous and terrestrial POC observed by remote sensing: Modeling and application

The sources of particulate organic carbon (POC) determine its conversion, thereby playing an important role in the carbon cycle of lakes. Accurate estimation of the sources and dynamic characteristics of POC is important for understanding the migration and transformation of organic carbon. However, the synchronous observation of POC sources with large areas through remote sensing is still challenging because of the complex composition of POC and the optical conditions of inland lakes. In this study, a three-band (1/ Rrs (689)–1/ Rrs (717)) × Rrs (697)) empirical algorithm of POC sources was constructed based on remote sensing reflectance (Rrs(λ)) and the proportion of endogenous POC estimated from the field-measured stable isotope (δ13CPOC) values. The validation and calibration results of the three-band algorithm showed robust performance, with MAPE and RMSE of estimated values and measured values of 10% and 0.07, respectively. The three-band algorithm had good simulation results for the Ocean and Land Color Instrument (OLCI), Moderate Imaging Spectroradiometer (MODIS), Geostationary Ocean Color Imager (GOCI), and Medium Resolution Imaging Spectrometer (MERIS) spectra. The POC sources estimated by the three-band algorithm suggest that the endogenous POC of Taihu Lake in August showed a decreasing trend from 2006 to 2019. The variation in terrestrial POC was slow and stable for both annual and monthly variations. The analysis of POC sources with total phosphorus (TP), total nitrogen (TN), water temperature, and wind speed indicated that terrestrial POC was closely related to wind speed (r = 0.33, P < 0.001), while endogenous POC was significantly associated with TP (r = 0.6, P < 0.001), TN (r = 0.56, P < 0.001), and water temperature (r = 0.49, P < 0.001). The use of remote sensing algorithms to evaluate POC from different sources is convenient and effective; furthermore, it helps to better understand the carbon cycle in lacustrine ecosystems.

Регрессионные прогнозы урожайности орошаемых озимых культур с использованием спутниковыx вегетационных индексов: модели, предикторы и эксперименты

The technologies of agrometeorological crop forecasts (ACF), based on data on the normalized vegetation index (NDVI) are an important element of the modern agricultural industry. Aim. To establish the heuristic yield model and approaches to the development of models of regression forecasts, including the ACF predictors selection procedure by exploring satellite Moderate Imaging Spectroradiometer (MODIS) NDVI data and conduct experimental forecasting. Materials and methods. The official yield statistics of irrigated winter wheat and barley in Diwaniyah province of Iraq and the NDVI MODIS observation for 2001–2019 are used. It is proposed to choose a two-component heuristic yield model containing a yield trend, due to a relatively slow change in crop cultivation technology and a climatic component associated with fluctuations in biological productivity due to the effects of weather conditions. Results. Using of heuristic model as background, an object-oriented approach to the choice of ACF regression model and predictor selection is developed. Firstly, we use NDVI semi-quantitative connection with crop coverage and crop leaf indexes to determine NDVI evolution according with the wheat and barley growing stages. Then, it is shown that in the province level ACF, as the original predictors should choose the NDVI time-series derived on the first and second half of February for three distinct grain-producing regions of the pro-vince. Experiments have shown that the satisfactory quality of the regressive ACF of both cultures can be achieved with 2–3 different original non-collinear predictors by their combination with the last year's yield or by inclusion of linear or quadratic dependencies. Conclusion. Wheat forecast with a relative error of 10% is obtained only by special selecting of time interval to train model and by control the parameters of the auto-regressive predictor. The high quality of the barley forecasting models is due to the fact that the variability of barley yields is dominated by the climatic component. The developed object-oriented approach can be adapted to the conditions of rainfed agriculture and to forecast of yield of other crops.

Mapping satellite-derived thermal parameters of canopy onset and assessing their temperature dependency for temperate forests in Korea

Growing degree days (GDD) and chilling requirements (CR) of canopy phenological onset are popular thermal indicators showing forest adaptation to local temperature regimes. This study proposes a satellite-based approach for mapping the canopy-onset thermal traits and analyzing their controlling factors in temperate forests. GDD and CR from 2003 to 2017 were estimated with canopy onset dates and air temperature data retrieved from Moderate Resolution and Imaging Spectroradiometer (MODIS) images. Results revealed that GDD and CR showed distinct geographic patterns with latitude and altitude but with low inter-annual variations of less than 30% of the coefficient of variation. The satellite-based spatial patterns of GDD and CR were well explained by temperature with R-squares of 0.70 and 0.58, which were comparable to 0.63 and 0.59 from the field observation data, respectively. As well, satellite-derived sensitivities of GDD and CR to unit changes in temperature were +52.7 and −21.3°CoC−1, respectively, which were again comparable to the sensitivities from the field data (+50.9 and −29.9°CoC−1, respectively). The patterns and temperature dependency of thermal parameters seem to be relevant to species-level thermal acclimation and community-level plant composition changes with temperature.

Long Range Transport of Southeast Asian PM2.5 Pollution to Northern Thailand during High Biomass Burning Episodes

This paper aims to investigate the potential contribution of biomass burning in PM2.5 pollution in Northern Thailand. We applied the coupled atmospheric and air pollution model which is based on the Weather Research and Forecasting Model (WRF) and a Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). The model output was compared to the ground-based measurements from the Pollution Control Department (PCD) to examine the model performance. As a result of the model evaluation, the meteorological variables agreed well with observations using the Index of Agreement (IOA) with ranges of 0.57 to 0.79 for temperature and 0.32 to 0.54 for wind speed, while the fractional biases of temperature and wind speed were 1.3 to 2.5 °C and 1.2 to 2.1 m/s. Analysis of the model and hotspots from the Moderate Imaging Spectroradiometer (MODIS) found that biomass burning from neighboring countries has greater potential to contribute to air pollution in northern Thailand than national emissions, which is indicated by the number of hotspot locations in Burma being greater than those in Thailand by two times under the influence of two major channels of Asian Monsoons, including easterly and northwesterly winds that bring pollutants from neighboring counties towards northern Thailand.

Earth as an Exoplanet. I. Time Variable Thermal Emission Using Spatially Resolved Moderate Imaging Spectroradiometer Data

Abstract Among the more than 4000 exoplanets known today, some terrestrial planets have been detected in the so-called habitable zone of their host stars and their number is expected to increase in the near future, energizing a drive to understand and interpret the eagerly awaited wealth of data to identify signs of life beyond our solar system. So far, Earth remains the best and only example of a habitable (and inhabited) world. Although, it seems extremely unlikely that any other exoplanets will be true Earth twins, it is important to explore and understand the full range of spectral signatures and variability of Earth in order to inform the design of future instruments and missions, and understand their diagnostic power as well as potential limitations. In this work we use Earth observation data collected by the MODIS instrument aboard the Aqua satellite. The complete data set comprises 15 years of thermal emission observations in the 3.66–14.40 μm range for five different locations on Earth (Amazon Rainforest, Antarctica, Arctic, Indian Ocean, and the Sahara Desert). We then determine flux levels and variations as a function of wavelength and surface type (i.e., climate zone and surface thermal properties) and investigate whether periodic signals indicating Earth’s tilted rotation axis can be detected. Our findings suggest that (1) viewing geometry plays an important role when thermal emission data is analyzed as Earth’s spectrum varies by a factor of three and more depending on the dominant surface type underneath; (2) typically strong absorption bands from CO2 (15 μm) and O3 (9.65 μm) are significantly less pronounced and partially absent in data from the polar regions implying that estimating correct abundance levels for these molecules might be challenging in these cases; and (3) the time-resolved thermal emission spectrum encodes information about seasons/planetary obliquity, but the significance depends on the viewing geometry and spectral band considered.

Biofuel Burning Influences Refractory Black Carbon Concentrations in Seasonal Snow at Lower Elevations of the Dudh Koshi River Basin of Nepal

When deposited on snow and ice, light absorbing impurities (LAIs) such as dust and black carbon (BC) reduce surface albedo and enhance melt. BC comes from incomplete combustion of fossil fuels and biomass. Local and regional sources of BC exist in High Mountain Asia, such as dry-dung burning for heat and fuel, which occurs in close proximity to snow and glaciers. Local dust or dust transported from the Indo-Gangetic Plain is also present. In the Ganges River Basin, meltwater is dominated by seasonal snow, yet relatively few observations of impurities in seasonal snow exist. To understand sources of impurities and their concentrations for seasonal snow on land, we evaluated multiple lines of evidence to scale up from ground-based measurements in the Dudh Koshi River Basin, a remote headwater basin within the Khumbu Region of Nepal. We obtained ground-based in-situ observations of refractory black carbon (rBC) measured by Single Particle Soot Photometer (SP2), including size distributions in snow on land. We interpreted these results in the context of concurrent Moderate Imaging Spectroradiometer (MODIS) satellite observations and speciated aerosol optical depth derived from reanalysis products modeled with the Navy Aerosol Analysis Prediction System (NAAPS) global aerosol model. We collected snow samples, mostly in the Gokyo Valley, at varying distances from local tea houses along a 2000 meters above sea level (m.a.s.l) elevation transect from 3250 to 5299 m.a.s.l . rBC concentrations ranged from 3.9 to 76.8 µg-rBC/L-H2O. Although previous data do not exist at these lower elevations, our findings are higher than previous surface snow results at higher elevations in the nearby Khumbu Valley. In general, rBC concentrations were lower in fresh snow than aged snow; concurrent MODIS satellite observations of snow albedo also show smaller impacts from LAIs in visible wavelengths in fresh snow. rBC decreased with elevation in aged snow samples, as did concurrent MODIS albedo observations. rBC-particle size distributions also shifted to a larger mode for aged snow samples. Results from NAAPS indicate anthropogenic and biogenic fine aerosols from biofuel (dry-dung burning) are primary aerosol species for the study period, at ~thrice the concentration of dust and smoke.

Investigation of CATS aerosol products and application toward global diurnal variation of aerosols

Abstract. We present a comparison of 1064 nm aerosol optical depth (AOD) and aerosol extinction profiles from the Cloud-Aerosol Transport System (CATS) level 2 aerosol product with collocated Aerosol Robotic Network (AERONET) AOD, Moderate Imaging Spectroradiometer (MODIS) Aqua and Terra Dark Target AOD and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD and extinction data for the period of March 2015–October 2017. Upon quality-assurance checks of CATS data, reasonable agreement is found between aerosol data from CATS and other sensors. Using quality-assured CATS aerosol data, for the first time, variations in AODs and aerosol extinction profiles are evaluated at 00:00, 06:00, 12:00 and 18:00 UTC (and/or 00:00, 06:00, 12:00 and 18:00 local time or LT) on both regional and global scales. This study suggests that marginal variations are found in AOD from a global mean perspective, with the minimum aerosol extinction values found at 18:00 LT near the surface layer for global oceans, for both the June–November and December–May seasons. Over land, below 500 m, the daily minimum and maximum aerosol extinction values are found at 12:00 and 00:00/06:00 LT, respectively. Strong diurnal variations are also found over north Africa, the Middle East and India for the December–May season, and over north Africa, south Africa, the Middle East and India for the June–November season.

Vicarious calibration of COCTS-HY1C at visible and near-infrared bands for ocean color application.

Remote sensing reflectance obtained from space-borne ocean color sensors is of great importance to carbon cycle and ocean-atmospheric interactions by providing biogeochemical parameters on the global scale using specific algorithms. Vicarious calibration is necessary for obtaining accurate remote sensing reflectance that meets the application demands of atmospheric correction algorithms. For ocean color sensors, vicarious calibration must be done prior to atmospheric correction. The third Chinese Ocean Color and Temperature Scanner (COCTS) aboard the HY1C satellite was launched on September 7, 2018, and it will provide essential ocean color data that will complement those of existing missions. We used field measurements from the Marine Optical Buoy (MOBY) and aerosol information provided by the MODerate Imaging Spectroradiometer (MODIS) aboard the Terra satellite to calculate vicarious calibration coefficients, and we further evaluated the applicability of the established vicarious calibration approach by cross-calibration using MODIS data on the global scale. Finally, the established vicarious calibration coefficients were used to retrieve the aerosol optical depth and remote sensing reflectance, which were compared to Aerosol Robotic Network-Ocean Color (AERONET-OC) data and MODIS-Terra and Ocean and Land Color Instrument (OLCI)-Sentinel-3A operational products. The results show that the vicarious calibration coefficients are relatively stable and reliable for all bands ranging from visible to near-infrared and can be used to obtain accurate high-quality data.

Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon)

A first-ever spatially detailed record of ice cover conditions in the Curonian Lagoon (CL), Europe’s largest coastal lagoon located in the southeastern Baltic Sea, is presented. The multi-mission synthetic aperture radar (SAR) measurements acquired in 2002–2017 by Envisat ASAR, RADARSAT-2, Sentinel-1 A/B, and supplemented by the cloud-free moderate imaging spectroradiometer (MODIS) data, are used to document the ice cover properties in the CL. As shown, satellite observations reveal a better performance over in situ records in defining the key stages of ice formation and decay in the CL. Using advantages of both data sources, an updated ice season duration (ISD) record is obtained to adequately describe the ice cover season in the CL. High-resolution ISD maps provide important spatial details of ice growth and decay in the CL. As found, ice cover resides longest in the south-eastern CL and along the eastern coast, including the Nemunas Delta, while the shortest ice season is observed in the northern CL. During the melting season, the ice melt pattern is clearly shaped by the direction of prevailing winds, and ice drift velocities obtained from a limited number of observations range within 0.03–0.14 m/s. The pronounced shortening of the ice season duration in the CL is observed at a rate of 1.6–2.3 days year‒1 during 2002–2017, which is much higher than reported for the nearby Baltic Sea regions. While the timing of the freeze onset and full freezing has not changed much, the dates of the final melt onset and last observation of ice have a clear decreasing pattern toward an earlier ice break-up and complete melt-off due to an increase of air temperature strongly linked to the North Atlantic Oscillation (NAO). Notably, the correlation between the ISD, air temperature, and winter NAO index is substantially higher when considering the lagoon-averaged ISD values derived from satellite observations compared to those derived from coastal records. The latter clearly demonstrated the richness of the satellite observations that should definitely be exploited in regional ice monitoring programs.