In California's Central Valley, water management and crop health, particularly in rice cultivation, are critical. This paper details the application of Earth surface Mineral dust source InvesTigation (EMIT) hyperspectral imaging, specifically employing Spectral Correlation Mapper (SCM) and Spectral Information Divergence (SID), for precise phenological analysis. By aligning EMIT data with Hyperion satellite references, we address spectral and geographical discrepancies. Our methodology includes seasonal sampling of spectral curves to capture the phenological stages of rice. Results show a strong correlation (R2= 0.86) between August EMIT and Reference dataset, emphasizing EMIT's utility in enhancing agricultural practices and water efficiency in the region, and highlighting the importance of understanding rice phenology for sustainable farming.

This study evaluates the potential application of hyperspectral Earth Surface Mineral Dust Source Investigation (EMIT) remote sensing for monitoring harmful algal blooms (HABs) and water quality in Clear Lake, California. The research focuses on correlating the chlorophyll-a (Chl-a) concentrations with EMIT spectral signatures, using waterbody-wide statistical analysis of Chl-a and EMIT data sampling at various lake locations. Results demonstrate distinct spectral signatures associated with varying Chl-a levels, highlighting the potential of hyperspectral imaging in differentiating algae levels and assessing water quality variables. It also indicates the EMIT’s utility in filling data gaps and offering high-resolution monitoring. This study underscores the need for further research in hyperspectral imaging for aquatic ecosystems, especially under challenging atmospheric conditions, enhancing our understanding of water quality dynamics.

Vapor Pressure Deficit (VPD) is crucial in meteorology and agriculture for understanding plant-environment interactions. Its application as an indicator in agricultural practices notably advances Sustainable Development Goals such as Zero Hunger (SDG 2) and Climate Action (SDG 13). This research focuses on the impact of climate change on agricultural productivity and food security in the Nile River Basin (NRB), emphasizing the role of VPD, temperature, and precipitation. Utilizing Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets from NEX-GDDP-CMIP6, the study analyzes key climatic variables that influence agricultural conditions. The study applies the Mann-Kendall test to evaluate VPD trends from 2000 to 2060 under two Shared Socioeconomic Pathways (SSPs), SSP2-4.5 and SSP5-8.5. The study's findings on the implications of rising VPD levels in the Nile River Basin (NRB), particularly under the SSP 5-8.5 scenario, highlight a critical challenge for the region's agricultural productivity and food security. The increased VPD, indicative of drier conditions, leads to a moisture deficit for crops, potentially reducing agricultural yields. This scenario poses a significant threat to food security, as lower crop yields can result in food shortages and higher food prices, adversely affecting vulnerable populations. The study underscores the necessity of integrating VPD insights into agricultural and water resource management strategies to uphold food security against climatic variations in support of the SDGs.

Climate change along with scalability in energy demands, urge nations and global organizations to search for scientific sustainable solutions in the energy production industry. Consequently, solar farms are poised to become a principal component in power grids, and especially in areas with favorable climatic (cloud-free) conditions combined with high solar irradiation averages on ground levels. Egypt has vast potential in solar energy production due to climate, landscape, and high mean solar energy yield that can generate energy that suffices to meet substantial portions of the annual energy demand. To maximize the energy production from energy farms in the region parameters and strategies that affect solar farms are investigated. Specifically, overcast conditions which affect greatly the solar farm’s performance have low concentration in Egypt, meaning that the focus on environmental parameters for specific regional phenomena such as aerosols and especially dust storms, hold a key role on these installations’ performance. These parameters are investigated by encompassing earth observation data from remote sensing MODerate (resolution Imaging Specto-radiometer) and Copernicus Atmosphere Monitoring Service (CAMS). Finally, strategies and methodologies are considered to integrate these data on Benban Solar Park a photovoltaic power station located in south of Cairo.

Heatwaves are influenced significantly by El Niño-Southern Oscillation (ENSO), which alters temperature and precipitation patterns throughout the world. Since 2020, we have witnessed a "triple dip" La Niña conditions persisting for three consecutive years resulting in severe weather and climate driven events globally. In this study, we identified the dominant frequency of Niño 3.4 Sea Surface Temperature (SST) signals and correlated them with regions experiencing unprecedented heat waves in 2022, namely, the Indian Ocean, the North Atlantic around England and Spain, and the Mediterranean Sea. The signal's power spectrum and its three highest power components are determined based on the signal's Singular Spectrum Analysis (SSA) for each region. Furthermore, the power spectrum coherence (PSC) of Niño 3.4 and other study regions is obtained to determine whether ENSO and heatwaves are linked. We find that Niño 3.4 and the Indian Ocean have the same dominant frequency for the highest power received, indicating ENSO's influence on the Indian heat wave. Additionally, we discovered that in March 2022, the Jet stream is essential in bringing warm Arctic waves southward, passing through Niño 3.4 and India. Furthermore, we found that no other region shared the dominant frequency of the Niño 3.4 region.

California, known for its diverse agriculture, is also a major producer of rice, especially in its northern regions in Sacramento River Valley. Traditional methods, predominantly reliant on optical-based satellite imagery, encounter limitations due to atmospheric interference and sensor resolution. The ability of Synthetic Aperture Radar (SAR) to penetrate atmospheric distortions and exhibit high sensitivity to vegetation structure presents a distinct advantage over optical-based methods. Utilizing Optical and SAR data fusion, this study advances the enhanced pixel-based phenological feature composite (Eppf) method using SVM classification algorithm, which can track phenological changes and patterns, providing valuable insights for agricultural planning and management. We demonstrate that Radar Vegetation Index (RVI) derived from SAR data, offers an improved alternative for identifying and mapping rice fields with enhanced accuracy. Subsequent research will focus on enhancing the suggested approach and investigating its relevance and adaptability to different types of crops.

Water preservation and maximization of its efficient use is key in areas facing water scarcity like California. One of the most important resources available to us are dams, which are useful to address a variety of needs like water supply, flood control, and maintaining environmental flows. However, if not managed properly, dams can be disastrous to humans and wildlife alike, different water species, habitats, and even impact water quality for a region. In this context, we have used newer Synthetic Aperture Radar Interferometry techniques like Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) to estimate the displacement rates at Shasta Dam in California, USA, and compare between the two techniques. Our results indicate that both the analyses show similar displacement trends, however, they differ in the magnitude of the displacements. The varying displacement values can be attributed to fundamental differences in the way both these techniques analyze data.

Climate impacts are particularly noticeable for the nations that share the Nile basin with an increase in hotter temperatures and fluctuating precipitation which expands natural catastrophes. Provincial work is required to precisely predict floods and dry seasons, thus preparing, and adapting to climatic events to build climate resilience among these Nile basin nations. In this context, an index indicating vulnerability to drought is derived for the Nile basin using Geographically Weighted Principal Component Analysis (GWPCA) and K-means clustering. Several climate indicators images related to atmosphere, land, and ocean are collected to build clusters categorized as high, mild, and low drought risk. Additionally, STL decomposition is conducted for the Palmer Drought Severity Index (PDSI) using time series data from 2010–2020 for the Nile basin to identify exceptional drought events for the past decade. Furthermore, correlations among PDSI and other climate indicators are analyzed using time series.

In the northwestern Arabian Gulf, the offshore islands of Jana and Karan are the major nesting sites of sea turtles. Continued litter pollution may clog the beaches of the islands and hamper nesting activities and hatchling emergence success. We examined the distribution of marine litter on Jana and Karan islands using a combination of in-situ beach litter survey and remote sensing satellite technologies. Results showed that the western portions of the islands receive the most significant amount of marine litter. The dominant debris type were plastic bottles by count and processed wood by weight. Jana island had relatively higher number of plastic bottles than Karan island. The vegetation line accumulated more significant amount of marine debris compared to the beach zone and the within vegetation zone. Debris was also observed deep into the islands. Plastic Index (PI) from Sentinel-2 was better in detecting a 19m-by-19m test plastic sheet than Floating Debris Index. The potential of PI was tested at Jana and Karan islands but require further studies to detect scattered macroplastics.

Land cover changes in the Mediterranean region have caused phenomenal shifts in weather patterns in areas of Europe and North Africa over the past years. Complicated short-term and gradual land cover changes in this region are caused by natural and human factors. Remote sensing and GIS techniques are widely used to detect the changes in land use land cover (LULC). Additionally, statistical analysis, data mining, and machine learning models are implemented in literature studies. In this study, a Siamese Neural Network, an extension of Convolution Neural Networks (CNN), is used to detect the landcover type changes that occurred in the Mediterranean region from 2009 to 2018 using MCD12Q1.006 data. Similarity scores are generated in Siamese Neural Network using 2009 and 2018 landcover classified images. Based on the similarity scores change of landcover classes are identified. Finally, base models such as Nearest Neighbor model and a Confusion matrix which is generated from image histograms are used to compare the performance of the Siamese Neural Network.

The summer phytoplankton bloom events have been recently investigated using remote sensing observations over several geographical areas of the Red Sea and changed our impression of its oligotrophic characteristic. However, only limited blooms events were recorded due to active dust storms limiting the observations. This work focuses on predicting the potential bloom events in the central region of the Red Sea, indicated by the chlorophyll- a values, through the machine learning models built from the simulated and observed oceanographical parameters. Four subregions showing active eddy activities are selected to generate modeling datasets in the Case-1 waters (water depth > 300 meters) for each region. Automated model selection and tuning are performed among different candidate supervised models including linear regression, trees models, ensemble models and deep neural networks (101 in total). The ensemble models (random decision forest and bootstrap decision forest) outperform others in showing effective performance in estimating chlorophyll-a values with ( ) of the training and ( ) of the testing processes, respectively. This work shows the potential applications to use a machine learning model to reconstruct missing ocean color observations, as well as revealing the oceanographical mechanism to induce phytoplankton growth in the Red Sea.

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R2> 0.95) in the testing and (R2> 0.93) in the evaluation process.

Primary productivity (PP) has been recently investigated using remote sensing-based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models-the vertically generalized production model (VGPM), the Eppley-VGPM, and the carbon-based production model (CbPM)-produced over the Red Sea domain for the 1998-2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization, and moving averages correlative analysis to uncover the models' responses to various climate factors. Here, we use the models' eight-day composite and monthly averages compared with satellite-based variables, including chlorophyll-a (Chla), mixed layer depth (MLD), and sea-surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO), and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI, and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models' connection to oceanographic and atmospheric parameters, as well as the trends in the southern Red Sea, thus calling for further validation efforts.

There is evidence that dust aerosols played a non-trivial role in the formation of Sandy. A Saharan dust event transported significant amounts of mineral dust into the troposphere along the path of the tropic wave Invest 99L, which formed into Hurricane Sandy. The Terra/Aqua-MODIS observations clearly define the spatial distribution of the coarse/fine aerosols, while the CALIPSO observations of Sandy development provide a clear view, vertical structure and aerosols type of the dust-laden layer. CALIPSO aerosol types map revealed the varying dust abundance before, during and after Sandy development. Six zones between the West African coast and the central Caribbean were examined during the period that the mineral dust moved across the mid-Atlantic. Increased AOD, followed by increased precipitation and a decrease in cloud top temperature was observed.

Aerosol index measurements from the Total Ozone Monitoring Spectroradiometer (TOMS) revealed the aerosol concentrations scattering properties to the UV radiations during the season well known to the locals and named as the Black cloud season. Aerosol Optical Depth (AOD) obtained from Moderate resolution Imaging Spectroradiometer (MODIS) showed the high aerosol concentrations during October of each year. Vertical temperature profiles obtained from the Atmospheric Infrared Sounder (AIRS) level-3 daily global girded data product during the months of September and October over Cairo are analyzed. AIRS, is one of the most advanced space-based atmospheric sounding systems. These profiles reveal the occurrence of an inversion layer in the lower troposphere in the vicinity of less than 1 km above the ground surface over Cairo. Meanwhile, over the same time period, a regular negative temperature gradient is observed over Alexandria, located 250 km away from Cairo.

In this letter, we propose hierarchical principal component analysis (HPCA) techniques for fusing spatial and spectral data, and compare them to direct principal component analysis (DPCA) over Multiangle Imaging SpectroRadiometer (MISR) data. It is shown that the proposed methods are significantly faster than DPCA. In case of DPCA, we merge the 20 different images resulting from the four spectral bands over the nadir and the four forward angles. In the hierarchical case, we first merge the information from the four spectral camera bands; then, we integrate the spatial information from the five cameras in the second step (or vice versa) by applying principal component analysis (PCA) twice. The classification results show that fused data using HPCA compare favorably to DPCA or to classification using the original data. This is because applying PCA to one particular data domain (e.g., spectral data followed by spatial data or vice versa) tends to better remove redundancies and enhance features within that domain. In addition, classification through hierarchical data fusion results in computational savings over the other methods.

The implications of climatic effects due to aerosols with a large variability like mineral dust serve as indicators of dust events and are examined. Airborne mineral dust can influence the climate by altering the radiative properties of the atmosphere. For instance, aerosols in the form of dust particles reflect the incoming solar radiation to space, thereby reducing the amount of radiation available to the ground. This is known as 'direct' radiative forcing of aerosols. Aerosols also serve as cloud condensation nuclei (CCN) and change the cloud albedo and microphysical properties of clouds, known as 'indirect' radiative forcing of aerosols. Direct and indirect radiative forcing by mineral dust are observed over a desert case study in China as well as a highly vegetated case study over Nile Delta, Egypt, using boundary layer dispersion (BLD), albedo, sensible heat flux (SHF), latent heat flux (LHF) and out going long wave radiation (OLR) parameters. During the presence of the dust event, shortwave fluxes largely decrease accompanied by an abrupt increase in the down-welling long wave fluxes resulting in surface forcing. This leads to absorption of the shortwave and long wave radiations resulting in a positive forcing in the top of the atmosphere. In this research we are focusing on the radiative impacts of the dust over some meteorological parameters.

Principal Component Analysis (PCA) has been widely used as a data reduction technique to overcome the curse of dimensionality. In this research we show a different use for PCA technique as a tool for data fusion. PCA as a data fusion technique is performed over the Multiangle Imaging Spectroradiometer (MISR) data, studying dust storms to better serve their identification. The multi-angle viewing capability of MISR is used to enhance our understanding of the Earth's environment that includes climate particularly of atmosphere and of land surfaces. In this research the multi angle MISR images clearly show a dust storm over the Liaoning region of China as well as parts of northern and western Korea on April 8, 2002. PCA is used to combine the obtained information from the different angle views and frequency bands of MISR datasets. Performing K-means clustering on the original and the assimilated products apply a quantitative measure that is introduced. Upon classifying the first 4 principal components (PCs) having 95% of the information content similar results were obtained as compared to the classification using original datasets.

Dust storms present environmental risks and affect the climate. They have worsened in the Mediterranean and East Asia regions over the last decade due to massive deforestation and increased droughts. Storms can travel over large parts of the Earth, in Asia, Africa, even affecting North America and Europe. Moreover dust storms are related to precipitation, soil moisture, land use/land cover practices, and other human activities. This work is a continuation of previous research in which we analyzed several remote sensing instruments capabilities in monitoring dust storms. We introduce the usage of the Multi-angle Imaging SpectroRadiometer (MISR) and TRMM Microwave Imager (TMI) as an optical and microwave combination in enhancing dust storm detection.

This work analyzes several remote sensing instrument capabilities in monitoring dust storms. Multisensor data analysis is carried out to study the behavior of dust particles at different wavelengths. A technique based on a combination of optical and microwave sensing of dust storms, using the Moderate Resolution Imaging Spectrometer (MODIS) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) respectively, is found to be particularly useful.