Moderate Resolution Imaging Spectroradiometer Satellite Research Articles

Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires

Abstract. Extreme biomass burning (BB) events, such as those seen during the 2019–2020 Australian bushfire season, are becoming more frequent and intense with climate change. Ground-based observations of these events can provide useful information on the macro- and micro-physical properties of the plumes, but these observations are sparse, especially in regions which are at risk of intense bushfire events. Satellite observations of extreme BB events provide a unique perspective, with the newest generation of geostationary imagers, such as the Advanced Himawari Imager (AHI), observing entire continents at moderate spatial and high temporal resolution. However, current passive satellite retrieval methods struggle to capture the high values of aerosol optical thickness (AOT) seen during these BB events. Accurate retrievals are necessary for global and regional studies of shortwave radiation, air quality modelling and numerical weather prediction. To address these issues, the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm has used AHI data to measure extreme BB plumes from the 2019–2020 Australian bushfire season. The sensitivity of the retrieval to the assumed optical properties of BB plumes is explored by comparing retrieved AOT with AErosol RObotic NETwork (AERONET) level-1.5 data over the AERONET site at Tumbarumba, New South Wales, between 1 December 2019 at 00:00 UTC and 3 January 2020 at 00:00 UTC. The study shows that for AOT values > 2, the sensitivity to the assumed optical properties is substantial. The ORAC retrievals and AERONET data are compared against the Japan Aerospace Exploration Agency (JAXA) Aerosol Retrieval Product (ARP), Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue over land, MODIS MAIAC, Sentinel-3 SYN and VIIRS Deep Blue products. The comparison shows the ORAC retrieval significantly improves coverage of optically thick plumes relative to the JAXA ARP, with approximately twice as many pixels retrieved and peak retrieved AOT values 1.4 times higher than the JAXA ARP. The ORAC retrievals have accuracy scores of 0.742–0.744 compared to the values of 0.718–0.833 for the polar-orbiting satellite products, despite successfully retrieving approximately 28 times as many pixels over the study period as the most successful polar-orbiting satellite product. The AHI and MODIS satellite products are compared for three case studies covering a range of BB plumes over Australia. The results show good agreement between all products for plumes with AOT values ≤ 2. For extreme BB plumes, the ORAC retrieval finds values of AOT > 15, significantly higher than those seen in events classified as extreme by previous studies, although with high uncertainty. A combination of hard limits in the retrieval algorithms and misclassification of BB plumes as cloud prevents the JAXA and MODIS products from returning AOT values significantly greater than 5.

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.

Dust storms from the Taklamakan Desert significantly darken snow surface on surrounding mountains

Abstract. The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of ∼ 2160, ∼ 610, and ∼ 640 km2 in the Tien Shan, Kunlun, and Qilian mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust deposition is up to 0.028–0.079 (11–31.5 W m−2), 0.088–0.136 (31–49 W m−2), and 0.092–0.153 (22–38 W m−2) across the Tien Shan, Kunlun, and Qilian mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust events. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. This study provides a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote sensing to monitor large-scale snow darkening.

Sensitivity of surface downward longwave radiation to aerosol optical depth over the Lake Taihu region, China

Downward longwave radiation (DLR) is an important component of the global radiation budget, significantly influencing surface energy dynamics. Accurate quantification of aerosol impacts on surface DLR is crucial for refining numerical weather predictions and comprehending surface energy balance. While the impact of aerosols on downward shortwave radiation has garnered substantial attention, investigation into the longwave radiation effect of aerosols remains relatively limited. In this study, the impact of aerosols on surface DLR is quantified by using continuous radiation data, conventional meteorological observations, and aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. These datasets span the period from 2011 to 2018 and are obtained from six stations of the Lake Taihu Flux Observational Network situated in Eastern China. The sensitivity of DLR to AOD (dDLR/dAOD) is determined through multiple linear regressions using both observational data and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Additionally, a radiative transfer model is employed to derive the dDLR/dAOD. A three-variable linear regression of the observational data shows that DLR increases by 4.66 ± 1.99 W m−2 per unit increase in AOD (mean ± 1 standard deviation) under clear-sky conditions. The radiative transfer model and the three-variable linear regression of MERRA-2 reanalysis yield similar sensitivity values of 4.69 ± 1.23 W m−2 AOD−1 and 4.10 ± 0.25 W m−2 AOD−1, respectively. However, these sensitivity values are considerably smaller than those reported in previous studies. Plausible factors contributing to the observed variance in sensitivity values are discussed. These results help to improve our understanding on the aerosol longwave radiation effect over the Lake Taihu region in Eastern China.

Relationship between aerosol and cloud characteristics over Delhi in North India during the dry and wet season

The study investigates the spatio-temporal variations of aerosol and cloud properties retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite at an urban megacity Delhi in the northern part of India during the dry and wet season for 15 years period from November 2004 to October 2019. This research comprises the study of crucial aerosol optical properties such as aerosol optical depth (AOD), Angstrom exponent (AE) along with the cloud properties such as water vapor (WV), cloud liquid water path (CLWP), cloud effective radius liquid (CERL), cloud fraction (CF), cloud optical depth (COD), cloud top pressure (CTP), and cloud top temperature (CTT). High AOD was observed across the Indo-Gangetic Plain (IGP), including Delhi and exhibited a range of 0.49–0.89 during the dry season and 0.73 to 0.98 during the wet season with a mean value of 0.73 ± 0.05 during the entire study period. High AE value (mean 1.40 ± 0.05) indicates dominance of fine-mode aerosols over the station, associated with high anthropogenic activities. A significantly large increasing trend in AOD was observed over the station during the wet season as compared to the dry season. The seasonal studies represent the maximum value of WV and CTP in the wet season. In addition, CF, CERL, COD and CLWP values were also high during the wet season. Furthermore, a correlation analyses between the aerosol and cloud parameters were carried out during both the seasons. From the analysis, AOD shows a positive correlation with WV, CF, COD and CLWP in both the seasons whereas a negative correlation was observed with CTP in wet season, while positive in dry season. On the other hand, a poor correlation was observed between AOD verses AE and CTT in both the seasons. These findings can contribute to advancing our understanding of aerosol-cloud interactions and inflate air quality assessments at an urban megacity like Delhi.

Seasonal variations in chlorophyll–a and sea surface temperature in the exclusive economic zone of Sri Lanka

Oceanic phytoplankton plays a crucial role in regulating the biogeochemical cycle in marine ecosystems. Light harvesting pigments such as chlorophyll–a (chl–a) can be used to understand marine primary productivity, especially phytoplankton abundance. Sea surface temperature is closely linked with phytoplankton abundance and climatic changes. The objective of the current study is to determine spatial and temporal variations of chl–a and sea surface temperature and their co-variation, over 12 years using statistical methodologies and Remote Sensing, Geographic Information System (GIS) and Cloud Computing Approach. In this study, the available sea surface temperature and chl–a data acquired from a Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor were proceeded through Google Earth Engine and Earth Science Data System. Data visualization was carried out using ArcMap™ software. Results show a weak negative linear relationship (−0.40, P < 0.05) between sea surface temperature and chl–a. Therefore, additional favorable environmental factors such as coastal upwelling, and nutrient discharge through river run-off can control the primary productivity of phytoplankton. We also illustrated spatial and temporal maps for chl–a and sea surface temperature distribution in the exclusive economic zone of Sri Lanka. Chlorophyll–a concentration is enhanced during the southwest monsoon, and the lowest concentration is observed during the first inter-monsoon period. Next, the highest sea surface temperature is recorded during the first inter-monsoon period, and the lowest sea surface temperature is observed during the northeast monsoon. Seasonal behavior of chl–a concentration and sea surface temperature are changed as chl–a (SWM) > chl–a (NEM) > chl–a (SIM) > chl–a (FIM) and SST (FIM) > SST (SIM) > SST (SWM) > SST (NEM), respectively. In addition, both trend lines for each variable show a positive gradient, which may be attributed to global warming. Finally, we observed the alteration of chl–a and sea surface temperature that linked to changes in ocean chemistry during the global COVID–19 pandemic.

Exploring the factors responsible for aerosol asymmetric trends over Indo-Gangetic Plain using remote sensing observations

The present study investigates the influencing factors responsible for the asymmetry in aerosol optical depth (AOD) trends using long-term datasets (2003–2019) over western and eastern Indo-Gangetic Plain (IGP) regions during the pre-monsoon season. Analysis from Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) for different aerosols illustrates that dust aerosols dominate over the western IGP (W-IGP), while sulphate and carbonaceous aerosols (black carbon (BC) and organic carbon (OC)) majorly contributed to the total AOD over the eastern IGP (E-IGP). Our study reveals a significant decline in AOD over the W-IGP, while a rising trend over E-IGP from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. A dipole pattern in AOD trends over IGP indicates the aerosol loading from combined effects of various natural and anthropogenic emissions under favourable meteorological conditions over the W-IGP and E-IGP, respectively. Furthermore, the declining AOD trend over W-IGP is mainly attributed to increased pre-monsoonal rainfall, which supports the wet deposition, increases soil moisture, thus reducing soil erodibility, and correlates strongly with meteorological factors. The rising AOD trend over the E-IGP appears to be influenced by increased anthropogenic emissions (i.e., BC, OC, and sulphate) from the industrialization of the region, decreased rainfall, and enhanced westerly-induced advection of aerosols from W-IGP. Our study indicates that the regional meteorological variables and anthropogenic sources influence changes in the AOD trends over the IGP region.

Spatial and temporal patterns of land surface temperature in Greenland from 2000-2019

Temperature dynamics on the island of Greenland are an important factor in shaping ecological events. Investigating the land surface temperature (LST) patterns is critical for understanding ecological dynamics across different regions. Further melting of the Greenland ice sheet could deva state marine and terrestrial ecosystems. This study used data from Moderate Resolution Imaging Spectroradiometer satellites to understand the seasonal patterns and patterns of LST over the entire island. Focusing on the period between 2000 and 2019, this study used a natural cubic spline model to identify seasonal patterns for all sub-regions. The data were seasonally adjusted and filtered with a second-order autocorrelation component. The spline was fitted again to identify the LST pattern, and a multivariate regression model was then used to adjust for spatial correlation. We illustrate that most of the land surface of Greenland hasstable temperature trends. These observed patterns in LST in Greenland during the study period suggest that the observed ice-sheet melting in Greenland within the last two decades could be due to other factors, not necessarily LST patterns.

QGIS-based modeling and analysis of urban dynamics affecting land surface temperature towards climate hazards in coastal zones of Portugal

Climate risk creates considerable concern due to the density of natural and socio-economic assets in coastal areas. Monitoring land use/cover changes, detecting population growth, and analyzing their impact on land surface temperature (LST) are necessary for effective urban management. In this study, land use/land cover (LULC), population, and LST changes in coastal regions of Portugal. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery was examined using World Bank population and CORINE data. Changes in land use types and LST values from 1990 to 2018 were analyzed. At the same time, LULC predictions were made using the Modules for Land Use Change Simulation (MOLUSCE) plug-in included in the QGIS software, and population projections were analyzed with LULC predictions in 2046. The results show the significant impact of land use on temperatures. It has been demonstrated that green and water areas can effectively cool cities. In the LULC changes between 1990 and 2018, the Leiria region stands out, with an annual increase of 4.04% in built areas from 121.58 to 259.06 km2. According to the simulations between 2018 and 2046, it was predicted that 18.74% of agricultural areas and 14.43% of forest areas would be transformed into built environments. The study is also essential as it confirms that the MOLUSCE plug-in can be effectively applied to land cover simulation on a large regional scale.

Dynamics of Peatland Fires in South Sumatra in 2019: Role of Groundwater Levels

During the dry season, extensive peatland fires in South Sumatra and another peatland in Indonesia result in environmental damage and pose health risks to humans. The Indonesian Government has implemented several measures to prevent the recurrence of these fires. One such measure involves the establishment of observation stations to monitor hydrometeorological parameters in different peatlands across Indonesia, including those in South Sumatra. To effectively control fires in South Sumatra’s peatland and minimize hotspot occurrences, it is essential to determine hydrometeorological parameters that can serve as fire control indicators. Therefore, this study aimed to investigate the relationship between groundwater levels and hotspot occurrences by analyzing groundwater level data collected from six Peat Restoration Agency stations in South Sumatra’s peatland, along with hotspot data obtained from Moderate Resolution Imaging Spectroradiometer satellite measurements. The findings reveal a significant correlation between groundwater levels and hotspots at the six stations. As the GWL increased, the number of hotspots tended to decrease, and vice versa. This means that GWL can be used as a controlling variable for hotspot emergence. To effectively minimize hotspot occurrences, it is recommended to maintain a minimum groundwater level of −0.45 ± 0.09 m in the peatland of South Sumatra.

Can land-use and land-cover change explain reduced resilience in forests?

Generating signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such systems are still limited due to the lack of high-frequency data and uncertainties associated with predicting complex systems such as ecosystems. This study aims to investigate the potential of time series analysis of remote sensing data in detecting signals of reduced resilience in mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land-cover change. To identify signals of reduced resilience, trend analyses of indicators such as autocorrelation (acf (1)) and standard deviation (SD) are applied. The findings revealed a notable decrease in resilience, signaled by significant upward trends in NDVI statistical metrics for Qeshm Island (Kendall’s τ of acf (1): 0.50 and SD: 0.90), contrasting with the pattern observed in Gabrik (Kendall’s τ of acf (1): −0.19 and SD: −0.19). These results align with our expectations derived from previous studies. Despite MNDWI significantly indicating reduced resilience in Qeshm Island (Kendall’s τ of acf (1): 0.86 and SD: 0.90), it also signaled decreased resilience in Gabrik (Kendall’s τ of acf (1): 0.79 and SD: 0.90). Moreover, MVWR failed to indicate signals of reduced resilience in both sites, specifically in Qeshm (Kendall’s τ of acf (1): −0.10 and SD: −0.07) and in Gabrik (Kendall’s τ of acf (1): −0.72 and SD: −0.12). These findings may be explained through quantitative analyses of land-use and land-cover change. While Qeshm Island and Gabrik share similarities in climate, geography, and annual rainfall, the analysis of land-use and land-cover change revealed significant differences between the two study areas. Qeshm Island underwent drastic increases in the built-up class by a 64.40% change between 1996 and 2014, whereas the built-up class expanded modestly by a 4.04% change in the Gabrik site. This study contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts. This is the first report of the successful application of remote sensing in generating signals of reduced resilience within mangrove forests in the Middle East.

Spatiotemporal trends and evapotranspiration estimation using an improvised SEBAL convergence method for the semi-arid region of Western Rajasthan, India

Abstract The study demonstrates how to estimate evapotranspiration (ET) for the Western Rajasthan region of India utilizing remotely sensed images with the Surface Energy Balance Algorithm for Land (SEBAL). Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite inputs were used to compute seasonal and annual ET on the Google Earth Engine platform. The assessment utilizing the SEBAL algorithm, in combination with the Food and Agriculture Organization (FAO) Penman–Monteith (PM) and Hargreaves methods, demonstrates that SEBAL has adequate reliability for estimating ET for a spatially large extent in semi-arid regions when evaluated with the Hargreaves method. The values of R2, root-mean-square error (RMSE), and mean bias error (MBE) for FAO-PM were 0.63, 1.65 mm/day, and 1.28 mm/day, respectively. For the Hargreaves method, the values of R2, RMSE, and MBE were 0.96, 0.41 mm/day, and −0.31 mm/day, respectively. The study showed that the northern region witnessed the highest ET due to the availability of abundant surface water for irrigation. Overall, the results demonstrate the SEBAL model's effectiveness in estimating evapotranspiration. A downward trend in ET is observed in the region, mainly due to changing climatic conditions.

Evaluation of climate change impact on plants and hydrology

Climate change (CC) is the menace of the hour impacting every facet of human existence. Regional CC and its impact studies are crucial in that they contribute to global change. The current study aims to investigate the prevalence of CC in Charsadda, Pakistan and its impact on vegetation and hydrology of the region to understand microclimate variability contribution to global CC. Utilizing local climate data for 20 years (2001–2020), Modified Mann-Kendall and Sen’s Slope statistics were employed to determine monthly and seasonal trends in climate variables. Significant changing climate variables were regressed on Moderate resolution Imaging Spectroradiometer (MODIS) satellite dataset viz. normalized difference vegetation index (NDVI). Due to the prominent climate factor impacting vegetation, NDVI was further correlated to MODIS land surface temperature (LST). Floods being the conspicuous climate calamity were mapped for 2005 and 2010 using satellites Landsat 5 and 7 dataset viz. normalized difference water index (NDWI) with flood risk assessment by watershed delineation. The findings revealed significant (p &amp;lt; 0.05) variability in climate variables (average monthly and summer maximum temperature, and average monthly and summer precipitation) that are driving CC and impacting vegetation and hydrology in the region. Temperature and solar radiation affect NDVI adversely while precipitation and relative humidity has positive impact on vegetation. NDVI varied greatly spatiotemporally, often increasing but worsening in some areas (Shabqadar, Abazai, Palai and Charsadda city with NDVI = 0.1–0.3) of the study region as a result of extreme weather events. Temporally, NDVI improved with an overall positive trend with a stage (2007–2016) of noticeable zigzag fluctuation. Spatial grids with higher LST (&amp;gt;40°C) were either devoid of or with sparse NDVI (&amp;lt;0.3) presenting global warming as peril to vegetation. NDWI maps (2005, 2010 floods) indicate that after floods wreaked havoc on the region altering the vegetation pattern revealing heavy irregular precipitation as the next to temperature in jeopardizing vegetation of the region. Lower elevation regions along the Swat and Kabul Rivers with a greater risk of flooding were identified by watershed delineation. The study suggests that local governments and stakeholders implement CC mitigation strategies and plans for vegetation restoration, flood alerts with post-flood management for regional sustainable development.

First Observational Perspectives of “Millipede Clouds” Over the Eastern Pacific Ocean

AbstractThe fundamental features of one kind of rarely known stratocumulus, which was termed as “Millipede Cloud,” occurred over the Eastern Pacific Ocean in 2017 were first documented by using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. These clouds had long and meandering “central axes” extending from several hundreds to thousands kilometers, and a number of “radical cloud arms” extending several tens of kilometers in its two sides. Total 59 “Millipede Clouds,” 4 and 55 of them, were formed over the Northern and the Southern Hemispheres, respectively. Their environmental backgrounds were analyzed by using ERA5 reanalysis data and MODIS sensor Level‐2 data. The cloud top pressures of these “Millipede Clouds” were between 850 and 800 hPa, and their top heights were about 1–2 km. There existed “inversion layer” of air temperature near the cloud tops at 800 hPa, which strongly suggested that these clouds were lower stratocumulus in essence.

Fabrication and investigation of agricultural monitoring system with IoT & AI

Artificial intelligence (AI) can be used in a variety of fields and has the potential to alter how we currently view farming. Due to its emphasis on effectiveness and usability artificial intelligence has the largest impact on agriculture of all industries. We highlight the automation-supporting technologies such as Artificial Intelligence (AI), Machine Learning, and Long-Range (LoRa) technology which provides data integrity and protection. We also offer a structure for smart farming that depends on the location of data processing after a comprehensive investigation of numerous designs. As part of our future study we have divided the unresolved difficulties in smart agriculture into two categories such as networking issues and technology issues. Artificial Intelligence and Machine Learning are examples of technologies whereas the Moderate Resolution Imaging Spectroradiometer satellite and LoRa are used for all network-related jobs. The goal of the research is to deploy a network of sensors throughout agricultural fields to gather real-time information on a variety of environmental factors including temperature, humidity, soil moisture and nutrient levels. The seamless data transmission and communication made possible by these sensors’ integration with Internet of Things technologies. With the use of AI techniques and algorithms the gathered data is examined. The technology may offer practical insights and suggestions for improving agricultural practices because the AI models are trained to spot patterns, correlations, and anomalies in the data. We are also focusing on indoor farming by supplying Ultra Violet radiation and artificial lighting in accordance with plant growth. When a pest assault is detected using AI and LoRa even in poor or no network coverage area and notifies the farmer’s mobile in any part of the world. The irrigation system is put to the test with various plants at various humidity and temperature levels in both dry and typical situations. To keep the water content in those specific regions soil moisture sensors are used.

Spatio-Temporal Change of Land Use/Land Cover and Vegetation Using Multi-MODIS Satellite Data, Western Ethiopia.

Land use and land cover (LULC) change and variability are some of the challenges to present-day water resource management. The purpose of this study was to determine LULC and Normalized Difference Vegetation Index (NDVI) fluctuations in western Ethiopia during the last 20 years. The first part of the study used MODIS LULC data for the change analysis, change detection, and spatial and temporal coverage in the study region. In the second part, the study analyzes the NDVI change and its spatial and temporal coverage. In this study, The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were applied to determine LULC and NDVI changes over four different periods. Evergreen broadleaf forests, deciduous broadleaf forests, mixed forests, woody savannas, savannas, grasslands, permanent wetlands, croplands, urban and built-up lands, and water bodies are the LULC in the period of analysis. The overall classification accuracy for the classified image from 2001 to 2020 was 85.4% and the overall kappa statistic was 81.2%. The results indicate a substantial increase in woody savannas, deciduous broadleaf, grasslands, permanent wetlands, and mixed forest areas by 119.6%, 57.7% 45.2%, 37%, and 21.3%, respectively, followed by reductions in croplands, water bodies, savannas, and evergreen broadleaf forest by 90.1%, 19.8%, 13.2%, and 4.8%, respectively, for the catchment between 2001 and 2020. The result also showed that the area's vegetation cover increased by 64% from 2001 to 2022. This study could provide valuable information for water resource and environmental management as well as policy and decision-making.

On the Consistency of Ice Cloud Optical Models for Spaceborne Remote Sensing Applications and Broadband Radiative Transfer Simulations

AbstractAqua satellite Moderate Resolution Imaging Spectroradiometer (MODIS) 1‐km observations are collocated with Clouds and the Earth's Radiant Energy System (CERES) fields of view taken during July 2008 afternoon satellite passes over the equatorial western Pacific Ocean. Radiation simulations are compared with collocated CERES observations to better understand the sensitivity of computed fluxes to two ice cloud broadband radiation parameterization schemes and inferred ice cloud characteristics. In particular, the radiation computational schemes and ice cloud property retrievals are based on two respective ice particle models, the MODIS Collection 6 (MC6) aggregate model and a more microphysically consistent two‐habit model (THM). The simulation results show that both MC6 and THM overestimate the shortwave (SW) and longwave (LW) cloud radiative effects at the top of the atmosphere, as compared to the CERES observations; the difference between the MC6 and THM‐based ice cloud retrievals is too small to compensate for the differences between the two model‐based radiation schemes. Therefore, the present finding suggests that broadband radiative simulations are more sensitive to the radiation parameterization scheme than to the input cloud properties retrieved using the corresponding ice cloud particle optical property model.

Above-treeline ecosystems facing drought: lessons from the 2022 European summer heat wave

Abstract. In 2022, a large part of Europe experienced an extremely dry and hot summer. In the Alps, this episode occurred after an unusually low-snowfall winter, which aggravated the dryness of soils. This study examines the impact of this particular year on the canopy greenness of above-treeline ecosystems by comparison with previous heat waves that hit the Alps during the last 2 decades. Normalized difference vegetation index (NDVI) time series derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were processed to extract the temporal variability in yearly maximum NDVI (NDVImax). The responsiveness of NDVImax to snow cover duration and growing-season weather conditions was evaluated in contrasting hydroclimate regions of the Alps using linear mixed-effect models. The year 2022 was unique in that the summer heat wave led to a widespread negative anomaly of NDVImax. The magnitude of this anomaly was unprecedented in the southwestern, driest part of the Alps, where vegetation activity was found to be particularly responsive to snow cover duration and early summer precipitation. In the colder and wetter regions, all warm to very warm summers before 2022 had led to increased canopy greenness, but the combination of a reduced snow cover and low early summer precipitation counteracted this expected beneficial effect in 2022. This study provides evidence that the control of canopy greenness by temperature and water balance differs markedly across regions of the Alps and that the year 2022 bears witness to a shift toward an increasing importance of moisture availability for regulating plant growth at high elevation. This is viewed as a warning sign of what could become the new norm in the years ahead in the context of increasing frequency and intensity of extreme droughts throughout temperate mountain ecosystems.

Assessing the Reliability of Satellite-Derived Evapotranspiration Data Using Numerical Modified Penman Method at Citarum Watershed

Evapotranspiration is an essential part of water availability analysis and crop water needs that are useful to estimate irrigation water demand. Since discharge measurement stations are limited, the analysis of water availability is the most important part of water management planning. Citarum watershed is the biggest watershed in West Java, supplies raw water to Jakarta, the capital city of Indonesia. Modified Penman is the common equation to analyze evapotranspiration, which was developed by Food and Agriculture Organization (FAO) and modified for tropical areas. Evapotranspiration is one term of the water balance equation. To determine water losses, it is necessary to solve this equation. Another source of evapotranspiration data is provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite's standard product, MOD16A2. In order to used the evapotranspiration satelilite data to fullfill the lack of groud station data, the reliability of satelite data is needed. The objective of this study is to compares and analyzes the reliability of satellite evapotranspiration potential images with the numerical Modified Penman method at Citarum Watershed. Modified Penman is one of several methods that calculate the evapotranspiration potential based on climate data. MOD16A2 was used for simulation data, and Modified Penman was used for baseline data. The reliability of the two simulations was analyzed by the skewness percentage of each pixel and period. The distribution of percent skewness indicates the performance of satellite evapotranspiration on the Modified Penman that represents the actual condition. The sensitivity of satellites is greatly affected by local weather conditions.

ENSO effects in the southern Gulf of California estimated from satellite data

This study assessed the impact of ENSO events in the seasonal and interannual variability of the sea surface temperature (SST) and phytoplankton biomass (expressed as chlorophyll-a, Chl-a levels) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products from June 2002 to December 2018, in two contrasting regions of the southern Gulf of California, the connection between the gulf and the Pacific Ocean, and the coastal region off Mazatlán. The generated dataset was compared with the El Niño–Southern Oscillation (ENSO) indices and a local wind-derived coastal upwelling index (CUI) computed in the coastal region off Mazatlán from the ERA5 monthly average wind. In this region, the results showed that the prevailing northwesterly winds reached 4 m s−1; however, southwesterly winds were observed during summer, mainly from June to August. The CUI showed values of up to 35 m3 s−1 per 100 m of coastline. The prevailing winds induce upwelling most of the year, with peaks in winter more intense during La Niña than during the El Niño event. Regarding SST and Chl-a levels, changes were observed with clear seasonal, semiannual, annual, and interannual variability, with 2.8 and 3.7 years of periodicities. In the coastal zone, seasonal values were maximum SST values (30–31 °C) during the summer and minimum values (20–22 °C) during the winter. The Chl-a levels showed an inverse trend, with minimum values (0.1–1.00 mg m−3) during summer and maximum values (>2 mg m−3) during winter. In the P1 site, in the connection between the gulf and the Pacific Ocean, the results showed during summer maximum SST values (29.5–31 °C) and minimum Chl-a (<0.2 mg m−3), while during winter minimum SST values (21–23 °C) and maximum Chl-a (0.3–2.0 mg m−3). Changes were also observed in both parameters owing to El Niño (high SST, low Chl-a levels) and La Niña (low SST, high Chl-a levels) conditions, which were confirmed by spectral and wavelet analyses. During the study period, La Niña conditions occurred during 2005/2006, 2007/2008, a multiyear event in 2010/2012, and 2017/2018, while El Niño conditions occurred during 2002/2003, 2006/2007, 2009/2010, and a multiyear event 2014/2016. Regarding their frequency, La Niña event occurred twice with a frequency corresponding to two years, and El Niño had a recurrence interval of between three and four years.