Search Results (425)

Wet rainfall pulses control vegetation growth through evapotranspiration in most dryland areas. This topic has not been extensively analyzed with respect to the vast semi-arid ecosystems of Central Australia. In this study, we investigated vegetation water responses to in situ root zone soil moisture (SM) variations in savanna woodlands (Mulga) in Central Australia using satellite-based optical and thermal data. Specifically, we used the Land Surface Water Index (LSWI) derived from the Advanced Himawari Imager on board the Himawari 8 (AHI) satellite, alongside Land Surface Temperature (LST) from MODIS Terra and Aqua (MOD/MYD11A1), as indicators of vegetation water status and surface energy balance, respectively. The analysis covered the period from 2016 to 2021. The LSWI increased with the magnitude of wet pulses and showed significant lags in the temporal response to SM, with behavior similar to that of the Enhanced Vegetation Index (EVI). By contrast, LST temporal responses were quicker and correlated with daily in situ SM at different depths. These results were consistent with in situ relationships between LST and SM, with the decreases in LST being coherent with wet pulse magnitude. Daily LSWI and EVI scores were best related to subsurface SM through quadratic relationships that accounted for the lag in vegetation response. Tower flux measures of gross primary production (GPP) were also related to the magnitude of wet pulses, being more correlated with the LSWI and EVI than LST. The results indicated that the vegetation response varied with SM depths. We propose a conceptual model for the relationship between LST and SM in the soil profile, which is useful for the monitoring/forecasting of wet pulse impacts on vegetation. Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands. Full article

This study examined spatial–temporal variations in air pollutant levels across four provinces on Sumatra Island, Indonesia, utilizing data from the Sentinel-5P satellite equipped with TROPOMI and MODIS aboard NASA’s Terra and Aqua satellites from 2019 to 2021. Sentinel-5P data, with a spatial resolution of 3.5 × 5.5 km² and near-daily temporal coverage, were used to analyze the nitrogen dioxide (NO²), carbon monoxide (CO), and Aerosol Optical Depth (AOD) in North Sumatra, West Sumatra, Jambi, and Riau—regions selected for their distinct industrial, agricultural, and urban characteristics. The purpose of this study was to investigate seasonal trends, regional differences, and the impact of the COVID-19 pandemic on air pollution, aiming to provide insights for improved air quality management and policy development. The satellite data were validated using zonal statistics to ensure consistency and reliability. The findings revealed significant seasonal fluctuations in pollution, with elevated levels during the dry season, primarily due to land clearing and forest fires. Urban and industrial areas such as Medan, Pekanbaru, Jambi, and Padang consistently exhibited high levels of NO₂, primarily due to vehicular and industrial emissions. The regions affected by biomass burning and agriculture, particularly Jambi and Riau, displayed notably higher CO and AOD levels during the dry season. The COVID-19 pandemic provided a unique opportunity to observe potential improvements in air quality, with significant reductions in NO₂, CO, and AOD levels during the 2020 lockdowns. The NO₂ levels in urban centers decreased by over 20%, while the reductions in CO and AOD reached up to 29% and 64%, respectively, reflecting diminished human activities and biomass burning. This study underscores the need for enhanced air quality monitoring and targeted management strategies in Sumatra, Indonesia. Future research should aim to improve the resolution and validation of data with ground-based measurements and broaden the number of pollutants studied to better understand air quality dynamics and support effective policy development. Full article

This study examined the seasonal variations and influencing factors for black carbon (BC) concentrations and aerosol optical depth (AOD) at the Socheongcho Ocean Research Station (SORS) on the Korean Peninsula from July 2019 to December 2020. An AOD algorithm was developed and validated using the Geo-KOMPSAT-2A (GK-2A) satellite. The GK-2A AOD demonstrated comparable performance to that of Low Earth Orbit satellites, including the Terra/MODIS (R² = 0.86), Aqua/MODIS (R² = 0.83), and AERONET AODs (R² = 0.85). Multi-angle absorption photometry revealed that seasonal average BC concentrations were the highest in winter (0.91 ± 0.80 µg·m⁻³), followed by fall (0.80 ± 0.66 µg·m⁻³), wet summer (0.75 ± 0.55 µg·m⁻³), and dry summer (0.52 ± 0.20 µg·m⁻³). The seasonal average GK-2A AOD was higher in wet summer (0.45 ± 0.37 µg·m⁻³) than in winter. The effects of meteorological parameters, AERONET AOD wavelength, and gaseous substances on GK-2A AOD and BC were investigated. The SHapley Additive exPlanations-based feature importance analysis for GK-2A AOD identified temperature, relative humidity (RH), and evaporation as major contributors. BC concentrations were increased, along with PM_2.5 and CO levels, due to the effects of combustion processes during fall and winter. Analysis of high-aerosol-loading cases revealed an increase in the fine-mode fraction, emphasizing the meteorological effects on GK-2A AOD. Thus, long-range transport and local BC sources played a critical role at the SORS. Full article

Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Terra and Aqua spacecraft have been in orbit for over 24 and 22 years, respectively, providing continuous observations of the Earth’s surface. Among the instrument’s 36 bands, 16 of them are thermal emissive bands (TEBs) with wavelengths that range from 3.75 to 14.24 μm. Routine post-launch calibrations are performed using the sensor’s onboard blackbody and space view port, the moon, and vicarious targets that include the ocean, Dome Concordia (Dome C) in Antarctica, and quasi-deep convective clouds (DCC). The calibration consistency between the satellite measurements from the two instruments is essential in generating a multi-year data record for the long-term monitoring of the Earth’s Level 1B (L1B) data. This paper presents the Terra and Aqua MODIS TEB comparison for the upcoming Collection 7 (C7) L1B products using measurements over Dome C and the ocean, as well as the double difference via simultaneous nadir overpasses with the Infrared Atmospheric Sounding Interferometer (IASI) sensor. The mission-long trending of the Terra and Aqua MODIS TEB is presented, and their cross-comparison is also presented and discussed. Results show that the calibration of the two MODIS sensors and their respective Earth measurements are generally consistent and within their design specifications. Due to the electronic crosstalk contamination, the PV LWIR bands show slightly larger drifts for both MODIS instruments across different Earth measurements. These drifts also have an impact on the Terra-to-Aqua calibration consistency. This thorough assessment serves as a robust record containing a summary of the MODIS calibration performance and the consistency between the two MODIS sensors over Earth view retrievals. Full article

Time series satellite data, coupled with available ground-based observations, enable geophysicists to survey earthquake precursors in areas of strong geotectonic activity. This paper is focused on pre-seismic atmospheric disturbances resulting from the stress accumulated during the seismogenic process related to the 6 February 2023 Kahramanmaras doublet earthquake sequence in Türkiye. We investigated the pre- and post-seismic anomalies of multiple precursors of different spatiotemporal patterns from MODIS Terra/Aqua and NOAA-AVHRR satellite data (air temperature at 2 m height—AT, air relative humidity—RH, and air pressure—AP, surface outgoing long-wave radiation—OLR, and land surface temperature—LST). Pre-seismic recorded anomalies of AT within seven months and OLR within one month before the main shocks suggested the existence of the preparatory process of the Kahramanmaras doublet earthquake. The 8-Day LST_Day and LST_night data evidenced pre-seismic and post-seismic thermal anomalies for both the Pazarcik and Elbistan earthquakes. The results of this study highlight that the spatiotemporal evolution of earthquake precursors can be important information for updating the seismic hazard in geotectonic active areas. Full article

Estimating daily continuous evapotranspiration (ET) can significantly enhance the monitoring of crop stress and drought on regional scales, as well as benefit the design of agricultural drought early warning systems. However, there is a need to verify the models’ performance in estimating the spatiotemporal continuity of long-term daily evapotranspiration (ET_d) on regional scales due to uncertainties in satellite measurements. In this study, a thermal-based two-surface energy balance (TSEB) model was used concurrently with Terra/Aqua MODIS data and the ERA5 atmospheric reanalysis dataset to calculate the surface energy balance of the soil–canopy–atmosphere continuum and estimate ET at a 1 km spatial resolution from 2000 to 2022. The performance of the model was evaluated using 11 eddy covariance flux towers in various land cover types (i.e., savannas, woody savannas, croplands, evergreen broadleaf forests, and open shrublands), correcting for the energy balance closure (EBC). The Bowen ratio (BR) and residual (RES) methods were used for enforcing the EBC in the EC observations. The modeled ET was evaluated against unclosed ET and closed ET (ET_BR and ET_RES) under clear-sky and all-sky observations as well as gap-filled data. The results showed that the modeled ET presented a better agreement with closed ET compared to unclosed ET in both Terra and Aqua datasets. Additionally, although the model overestimated ET_d across all different land cover types, it successfully captured the spatiotemporal variability in ET. After the gap-filling, the total number of days compared with flux measurements increased substantially, from 13,761 to 19,265 for Terra and from 13,329 to 19,265 for Aqua. The overall mean results including clear-sky and all-sky observations as well as gap-filled data with the Aqua dataset showed the lowest errors with ET_RES, by a mean bias error (MBE) of 0.96 mm.day⁻¹, an average mean root square (RMSE) of 1.47 mm.day⁻¹, and a correlation (r) value of 0.51. The equivalent figures for Terra were about 1.06 mm.day⁻¹, 1.60 mm.day⁻¹, and 0.52. Additionally, the result from the gap-filling model indicated small changes compared with the all-sky observations, which demonstrated that the modeling framework remained robust, even with the expanded days. Hence, the presented modeling framework can serve as a pathway for estimating daily remote sensing-based ET on regional scales. Furthermore, in terms of temporal trends, the intra-annual and inter-annual variability in ET can be used as indicators for monitoring crop stress and drought. Full article

Since 2007, Ulva prolifera (U. prolifera) originating in northern Jiangsu (NJ) has consistently expanded to the southern coast of the Shandong Peninsula. However, the underlying reasons for the 2007 sudden bloom of U. prolifera on a large scale remain unknown. This study uses remote sensing data from MODIS/AQUA spanning the period 2003–2022 to investigate the sea surface temperature (SST) structure changes in the southern Yellow Sea (SYS) over the past 20 years. The results demonstrate the following. (1) Since 2007, the NJ northward current and the Yangtze estuary warm current have exhibited higher temperatures, earlier northward intrusions, and larger influence areas, leading to a faster warming rate in NJ before mid-May. This rapid increase in SST to a level suitable for early U. prolifera growth triggers large-scale blooms. (2) The change in temperature structure is primarily induced by a prolonged and intense La Niña event in 2007–2008. However, since 2016, under stable global climate conditions, the temperature structure of the SYS has returned to the pre-2007 state, corresponding to a decrease in the scale of U. prolifera blooms. Full article

The spatial resolution (250–1000 m) of the FY-3D MERSI is too coarse for agricultural monitoring at the farmland scale (20–30 m). To achieve the winter wheat yield (WWY) at the farmland scale, based on FY-3D, a method framework is developed in this work. The enhanced deep convolutional spatiotemporal fusion network (EDCSTFN) was used to perform a spatiotemporal fusion on the 10 day interval FY-3D and Sentinel-2 vegetation indices (VIs), which were compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). In addition, a BP neural network was built to calculate the farmland-scale WWY based on the fused VIs, and the Aqua MODIS gross primary productivity product was used as ancillary data for WWY estimation. The results reveal that both the EDCSTFN and ESTARFM achieve satisfactory precision in the fusion of the Sentinel-2 and FY-3D VIs; however, when the period of spatiotemporal data fusion is relatively long, the EDCSTFN can achieve greater precision than ESTARFM. Finally, the WWY estimation results based on the fused VIs show remarkable correlations with the WWY data at the county scale and provide abundant spatial distribution details about the WWY, displaying great potential for accurate farmland-scale WWY estimations based on reconstructed fine-spatial-temporal-resolution FY-3D data. Full article

Satellite remote sensing is the best way to derive sea surface skin temperature (SST_skin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SST_skin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SST_skin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SST_skin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SST_skin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SST_skin data to study surface ocean processes and climate change in the Arctic. Full article

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval. Full article

The objective of this article is to investigate the possible correlations between vegetation indices and surface temperature in the Cananéia–Iguape Coastal System (CICS), in São Paulo (Brazil). Vegetation index data from MODIS orbital products were used to carry out this work. The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) were acquired from the MODIS/Aqua sensor (MYD13Q1) and the leaf area index (LAI) from the MODIS/Terra (MOD15A2H). Surface temperature data were acquired from MODIS/Aqua (MYD11A2). The data were processed using Google Earth Engine and Google Colab. The data were collected, and spatial and temporal correlations were applied. Correlations were applied in the annual and seasonal period. The annual temporal correlation between vegetation indices and surface temperature was positive, but statistically significant for the LAI, with r = 0.43 (90% significance). In the seasonal period, positive correlations occurred in JFM for all indices (95% significance). Spatially, the results of this research indicate that the largest area showed a positive correlation between VI and LST. The hottest and rainiest periods (OND and JFM) had clearer and more significant correlations. In some regions, significant and clear correlations were observed, such as in some areas in the north, south and close to the city of Iguape. This highlights the complexity of the interactions between vegetation indices and climatic attributes, and highlights the importance of considering other environmental variables and processes when interpreting changes in vegetation. However, this research has significantly progressed the field, by establishing new correlations and demonstrating the importance of considering climate variability, for a more accurate understanding of the impacts on vegetation indices. Full article

Turbidity serves as a crucial indicator of coastal water health and productivity. Twenty years of remote sensing data (2003–2022) from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were used to analyze the spatial and temporal variations in turbidity, as measured by total suspended matter (TSM), in the Berau Coastal Shelf (BCS), East Kalimantan, Indonesia. The BCS encompasses the estuary of the Berau River and is an integral part of the Coral Triangle, renowned for its rich marine and coastal habitats, including coral reefs, mangroves, and seagrasses. The aim of this research is to comprehend the seasonal and interannual patterns of turbidity and their associations with met-ocean parameters, such as wind, rainfall, and climate variations like the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The research findings indicate that the seasonal spatial pattern of turbidity is strongly influenced by monsoon winds, while its temporal patterns are closely related to river discharge and rainfall. The ENSO and IOD climate cycles exert an influence on the interannual turbidity variations, with turbidity values decreasing during La Niña and negative IOD events and conversely increasing during El Niño and positive IOD events. Furthermore, the elevated turbidity during negative IOD and La Niña coincides with rising temperatures, potentially acting as a compound stressor on marine habitats. These findings significantly enhance our understanding of turbidity dynamics in the BCS, thereby supporting the management of marine and coastal ecosystems in the face of changing climatic and environmental conditions. Full article

This study discusses the issue of determining the direction and strength of changes taking place in the structure of agricultural land in the mountain and foothill areas of the Carpathians in Poland in comparison with Slovakia, Romania and Ukraine. The most important financial institutional measures dedicated to the protection of the natural environment in Polish agriculture in the Areas facing Natural and other specific Constraints (ANCs) mountain and foothill in the first year of the CAP 2023–2027 were also established. Satellite data from 2001 to 2022 were used. The analyses used the land use classification MCD12Q1 provided by NASA and were made on the basis of satellite imagery collections from the MODIS sensor placed on two satellites: TERRA and AQUA. In EU countries, a decreasing trend in agricultural areas has been observed in areas below 350 m above sea level. In areas above 350 m, this trend weakened or even turned into an upward trend. Only in Ukraine was a different trend observed. It was found that in Poland, the degree of involvement of farmers from mountain and foothill areas in implementing financial institutional measures dedicated to protecting the natural environment during the study period was not satisfactory. Full article

The focus on aerosol analysis in the Levantine Region is driven by climate-change impacts, the region’s increasing urban development and industrial activities, and its geographical proximity to major dust-source areas. This study conducts a comparative analysis of aerosol optical depth data from Aqua/MODIS and AERONET during different periods between 2003 and 2023 at four stations: IMS-METU-ERDEMLI (Mersin/Türkiye) (2004–2019), CUT-TEPAK (Limassol/Cyprus) (2010–2023), Cairo_EMA_2 (Cairo/Egypt) (2010–2023), and SEDE_BOKER (Sede Boker/Israel) (2003–2023). The objective is to evaluate the variability and reliability of AOD measurements between satellite and ground-based observations and to determine how well they represent regional climatology. The highest percentage of measurements within the expected error envelope was observed at the IMS-METU-ERDEMLI station, indicating the best agreement between MODIS and AERONET data at this location. The Seasonal-Trend Decomposition using Loess (STL) method revealed consistent spring and summer peaks influenced by dust transport from the Sahara and the Middle East, with lower values in winter. The study also considers the influence of cloud fraction on MODIS measurements and includes aerosol classification. A statistically significant slight positive trend in AOD values was identified at the IMS-METU-ERDEMLI station. Conversely, no significant trends were detected at the other stations. The results of this study agree with those of previous research on the impact of long-range dust transport on regional aerosol loadings, emphasizing the importance of integrating satellite and ground-based observations. Full article

This study used satellite remote sensing to investigate trends in harmful algal blooms (HABs) over the last 21 years, focusing on four regions within the Laurentian Great Lakes: western Lake Erie, Green Bay, Saginaw Bay, and western Lake Superior. HABs in the water column were identified from remote sensing-derived chlorophyll concentrations, and surface HAB scums were classified based on the Normalized Difference Vegetation Index (NDVI) band ratio index. Using imagery from the Moderate Resolution Imaging Spectroradiometer sensor on the Aqua satellite (MODIS-Aqua) from 2002 to 2022, we generated daily estimates of the HAB and surface scum extents for each region, which were then averaged to generate mean annual extents. We observed a significant decline in the Saginaw Bay mean annual HAB extents over the 21-year study period. Otherwise, no significant changes were observed over this period in any region for either the HAB or surface scum mean annual extents, thus suggesting that HABs are not increasing in the Great Lakes. Despite the lack of increasing trends, the blooms are still recurring annually and causing a negative impact on the nearby communities; thus, we believe that it is crucial to continue studying Great Lakes HABs to monitor the impact of current and future abatement strategies. Full article