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This paper presents a meticulous exploration of advanced machine learning techniques for precise forest type classification using multi-temporal remote sensing data within a woodland environment. The study comprehensively evaluates a diverse range of models, ranging from advanced (ensemble) machine learning (ML) methods to several finely tuned support vector machine (SVM) variants, with a specific focus on Bayesian-optimized SVM with a radial basis function (RBF) kernel. Our findings highlight the robust performance of the Bayesian-optimized SVM, achieving a high accuracy of up to 94.27% and average precision and recall of 94.46% and 94.27%, respectively. Notably, this accuracy aligns with the levels attained by acclaimed ensemble techniques such as random forest and CatBoost while also surpassing those of XGBoost and LightGBM. These results highlight the potential of these methodologies to significantly enhance forest type mapping accuracy compared to traditional (linear) SVM and black-box neural networks. This, in turn, can enable the reliable identification and quantification of key services, including carbon storage and erosion protection, intrinsic to the forest ecosystem. The findings of our comparative study emphasize the profound impact of employing and fine-tuning ML approaches in the realm of remote sensing-based environmental analysis. [ABSTRACT FROM AUTHOR]

Super-resolution (SR) is a well-established technique used to enhance the resolution of low-resolution images. In this paper, we introduce a novel approach for the super-resolution of Sentinel-2 10 m RGB images using higher-resolution Venus 5 m RGB images. The proposed method takes advantage of a modified SRResNet network, integrates perceptual loss based on the VGG network, and incorporates a learning rate decay strategy for improved performance. By leveraging higher-resolution VENµS 5 m RGB images as reference images, this approach aims to generate high-quality super-resolved images of Sentinel-2 10 m RGB images. The modified SRResNet network was designed to capture and learn underlying patterns and details present in Venus images, enabling it to effectively enhance the resolution of Sentinel-2 images. In addition, the inclusion of perceptual loss based on the VGG network helps preserve important visual features and maintain the overall image quality. The learning rate decay strategy ensures the network converges to an optimal solution by gradually reducing the learning rate during the training process. Our research contributes to the field of super-resolution by offering a novel approach specifically tailored for enhancing the resolution of Sentinel-2 10 m RGB images using Venus 5 m RGB images. The proposed methodology has the potential to benefit various applications, such as remote sensing, land cover analysis, and environmental monitoring, where high-resolution imagery is crucial for accurate and detailed analysis. In summary, our approach presents a promising solution for the super-resolution of Sentinel-2 10 m RGB images, providing an effective means to obtain higher-resolution imagery by leveraging the complementary information from Venus 5 m RGB images. We used the SEN2VENµS dataset for this research. The SEN2VENµS dataset comprises cloud-free surface reflectance patches obtained from Sentinel-2 imagery. Notably, these patches are accompanied by corresponding reference surface reflectance patches captured at a remarkable 5 m resolution by the VENµS Micro-Satellite on the same acquisition day. To assess the effectiveness of the proposed approach, we evaluated it using widely used metrics such as the mean squared error (MSE), the peak signal-to-noise ratio (PSNR), and the structural similarity index (SSIM). These metrics provided quantitative measurements of the quality and fidelity of the super-resolved images. Experimental results demonstrate the effectiveness of our proposed approach in achieving improved super-resolution performance compared to existing methods. As an example, our method achieved a PSNR of 35.70 and a SSIM of 0.94 on the training dataset, outperforming the bicubic interpolation method, which yielded a PSNR of 29.53 and a SSIM of 0.92. On the validation dataset, our approach achieved a PSNR of 40.3809 and a SSIM of 0.98, while the bicubic interpolation method achieved a PSNR of 34.26 and a SSIM of 0.94. Finally, on the test dataset, our approach achieved a PSNR of 29.8231 and a SSIM of 0.90, whereas the bicubic interpolation method yielded a PSNR of 26.99 and a SSIM of 0.85. The evaluation based on MSE, PSNR, and SSIM metrics showcases the enhanced visual quality, increased image resolution, and improved similarity to the reference Venus images. [ABSTRACT FROM AUTHOR]

eXplainable Artificial Intelligence (XAI) has emerged as an essential requirement when dealing with mission-critical applications, ensuring transparency and interpretability of the employed black box AI models. The significance of XAI spans various domains, from healthcare to finance, where understanding the decision-making process of deep learning algorithms is essential. Most AI-based computer vision models are often black boxes; hence, providing the explainability of deep neural networks in image processing is crucial for their wide adoption and deployment in medical image analysis, autonomous driving, and remote sensing applications. Existing XAI methods aim to provide insights about the methodology used by the black-box model in making decisions by highlighting the most relevant regions within the input image that contribute to the model's prediction. Recently, several XAI methods for image classification tasks have been introduced. In contrast, image segmentation has received comparatively less attention in the context of explainability, although it is a fundamental task in computer vision applications, especially in remote sensing. Only some research proposes gradient-based XAI algorithms for image segmentation. This paper adapts the recent gradient-free Sobol XAI method for semantic segmentation. To measure the performance of the Sobol method for segmentation, we propose a quantitative XAI evaluation method based on a learnable noise model. The main objective of this model is to induce noise on the explanation maps, where a higher induced noise signifies low accuracy and vice versa. A benchmark analysis is conducted to evaluate and compare the performances of three XAI methods, Seg-Grad-CAM, Seg-Grad-CAM++ and Seg-Sobol, using the proposed noise-based evaluation technique. This constitutes the first attempt to run and evaluate XAI methods using high-resolution satellite images. Our code is publicly available at GitHub. [ABSTRACT FROM AUTHOR]

This paper presents a technique for mapping the vegetation cover of mountainous areas based on seasonal satellite data from Landsat-OLI 8, using information about vegetation growth conditions. This mapping is based on the creation of a layer of relatively homogeneous areas in terms of relief and climate. Training samples for the classification of images were formed within these areas. Satellite images were classified using the maximum likelihood method. The created map reflects the spatial distribution of 9 classes of forest vegetation and 10 classes of non-forest vegetation. [ABSTRACT FROM AUTHOR]

Forest fires are common occurrences in Kazakhstan, particularly from June until September, and damage the country's forest resources extensively. The mapping of burned areas is crucial for fire management, to implement the proper mitigation strategies and restoration actions following the fire season. The mapping of burned areas enables a thorough evaluation of the damage caused by fires to forests. The unique characteristics of forest plants and soil are dramatically altered by the fire's destruction, leading to a dramatic shift in reflectance. The destruction caused by fires can be mitigated, and vegetation can be replanted, with the use of maps depicting the affected areas. The accurate and timely mapping of burned areas is critical for fire prevention methods such as planning, mitigation, and vegetation regeneration. The country Kazakhstan launched two satellites, KazEOSat 1 and KazEOSat 2, as part of the Earth Remote Sensing Satellite System (ERSSS) for the management of natural resources and monitoring. The KazEOSat 1 is a high-resolution observation satellite, launched in a Sun-synchronous orbit at an altitude of about 630 km, consisting of four spectral bands (4 m) and a very high panchromatic (1 m) band. In this study, KazEOsat 1 satellite datasets were used to map the burned areas in various parts of Kazakhstan. Three different spectral indices, viz., the Global Environmental Monitoring Index (GEMI), Ashburn Vegetation Index (AVI), and Burn Area Index (BAI), are used and the findings are compared to the best burnt area discrimination index using the KazEOsat 1 satellite datasets. The results show that the BAI shows a higher accuracy than the other indices at mapping the burnt area using the KazEOsat 1 satellite datasets. [ABSTRACT FROM AUTHOR]

This study focuses on the quantification and forecasting of the biological pump potential in the Philippine seas, specifically inside the Exclusive Economic Zone (EEZ). Variabilities and disturbances that might potentially influence ocean productivity such as increased sea surface temperature (SST), and the high frequency of typhoons in the Philippines were investigated. CHL and SST spatio-temporal maps were used to provide visualization for the trends and phenomena before, during, and after typhoon occurrence for the years 2019–2021. Integrating the NASA Ocean Color data of CHL and SST with typhoon tracks, the biological pump potential annual estimate was generated. [ABSTRACT FROM AUTHOR]

The Fez region in Morocco has experienced changes in agricultural land use as a result of climate change. These changes include erratic rainfall, rising temperatures, and evapotranspiration. The objective of this research is to investigate the impact of these changes on agricultural land use between 2018 and 2022 using remote sensing data (sentinel-2 and MODIS), climate data, drought index (Vegetation Condition Index (VCI)) and two machine learning algorithms (Random Forest (RF) and Gradient Tree Boost (GTB). The RF and GTB algorithms were trained and tested, and their performance was analyzed, revealing that the GTB algorithm is more efficient than the RF, with a Kaffa coefficient of 91% and overall accuracy of 93%. The analysis of climate change on land use and land cover (LULC) variations revealed a significant (54%) reduction in rainfall. Furthermore, agricultural land use and water were reduced by 41% and 17%, respectively. Conversely, barren land and built-up areas increased by 58% and 4%, respectively, and the annual mean VCI decreased from 39.72 in 2018 to 19.9 in 2022. The study concluded that climate change had a significant impact on the region's agricultural land cover, and decreases in rainfall directly affect agricultural land use. [ABSTRACT FROM AUTHOR]

Parameter Efficient Fine-Tuning (PEFT) techniques have recently experienced significant growth and have been extensively employed to adapt large vision and language models to various domains, enabling satisfactory model performance with minimal computational needs. Despite these advances, more research has yet to delve into potential PEFT applications in real-life scenarios, particularly in the critical domains of remote sensing and crop monitoring. In the realm of crop monitoring, a key challenge persists in addressing the intricacies of cross-regional and cross-year crop type recognition. The diversity of climates across different regions and the need for comprehensive large-scale datasets have posed significant obstacles to accurately identifying crop types across varying geographic locations and changing growing seasons. This study seeks to bridge this gap by comprehensively exploring the feasibility of cross-area and cross-year out-of-distribution generalization using the state-of-the-art (SOTA) wheat crop monitoring model. The aim of this work is to explore efficient fine-tuning approaches for crop monitoring. Specifically, we focus on adapting the SOTA TSViT model, recently proposed in CVPR 2023, to address winter-wheat field segmentation, a critical task for crop monitoring and food security, especially following the Ukrainian conflict, given the economic importance of wheat as a staple and cash crop in various regions. This adaptation process involves integrating different PEFT techniques, including BigFit, LoRA, Adaptformer, and prompt tuning, each designed to streamline the fine-tuning process and ensure efficient parameter utilization. Using PEFT techniques, we achieved notable results comparable to those achieved using full fine-tuning methods while training only a mere 0.7% parameters of the entire TSViT architecture. More importantly, we achieved the claimed performance using a limited subset of remotely labeled data. The in-house labeled data-set, referred to as the Lebanese Wheat dataset, comprises high-quality annotated polygons for wheat and non-wheat classes for the study area in Beqaa, Lebanon, with a total surface of 170 km², over five consecutive years from 2016 to 2020. Using a time series of multispectral Sentinel-2 images, our model achieved a 84% F1-score when evaluated on the test set, shedding light on the ability of PEFT to drive accurate and efficient crop monitoring, designed mainly for developing countries characterized by limited data availability. We intend to publicly release the Lebanese winter-wheat data set, code repository, and model weights. [ABSTRACT FROM AUTHOR]

This study investigates the impact of landfills on surrounding vegetation in Naples, Italy, utilizing Sentinel-2 and Landsat 8 imagery processed with Sen2like. Seventeen landfill sites were studied with indices NDVI and GCI using four years of data, revealing significant vegetation growth reduction near two landfills compared to farther areas. Continuous monitoring is crucial, and satellite imagery offers an effective assessment tool. The study establishes a correlation between landfills and vegetation, underlining the need for ongoing monitoring and exploring additional factors influencing vegetation health within landfill environments. The findings contribute valuable insights to promote sustainable landfill management and protect surrounding ecosystems. [ABSTRACT FROM AUTHOR]

Water is an essential resource for the survival of living beings. Remote-sensing data provides the best possible way to detect water bodies and monitor change over time. With a surplus amount of remote sensing data, machine learning approaches have become an effective and efficient way to detect and monitor surface water bodies. This research focused on utilizing remote sensing and machine learning approaches to monitor changes in the surface water of Lake Okeechobee, Florida, USA. This investigation used two sources of remotely sensed data, Landsat 7, and Landsat 8, for 2002 and 2022, respectively. Two machine learning algorithms, support vector machine (SVM) and random forest (RF), were adopted, considering their power and robustness, among other factors, for supervised classification. Both algorithms provided an accuracy of over 92% and a kappa statistic exceeding 0.8. Further, we used image differencing techniques to track changes across two decades. The SVM suggested an increase of 85 km2, and RF indicated an expansion of 52 km2 in the surface water area. This study explicitly demonstrates how dynamic natural resources are, especially water sources. Thus, it can provide a foundation for research that further explores environmental assessments and sustainable water resource planning in Lake Okeechobee. [ABSTRACT FROM AUTHOR]

This article presents the preliminary results of a study on satellite-derived bathymetry. The purpose of this research is to explore the use of remote sensing and optical imagery for mapping the depth of coastal waters. This study uses empirical models to estimate the water depth based on the optical properties of the water column. To carry this out, it employs atmospheric correction algorithms to remove the influence of atmospheric scattering and absorption on the optical signals. The authors then apply the empirical models to the corrected imagery to obtain the bathymetric maps. The study shows promising results (RMSE ranging between 0.49 and 0.96m using the Lyzenga methodology), with the estimated depths generally consistent with the available ground-truth data. However, the accuracy of the estimated depths varies depending on the water conditions, such as the presence of waves and bottom type. The authors conclude that satellite-derived bathymetry has great potential for coastal applications, such as environmental monitoring and coastal management. [ABSTRACT FROM AUTHOR]

In the framework of the ESA-NASA Joint Aeolus Tropical Atlantic Campaign (JATAC), the ASKOS experiment was implemented during the summer and autumn of 2021 and 2022. ASKOS comprised roughly 9 weeks of measurements in the Saharan dust outflow towards the North Atlantic, with operations conducted from the Cabo Verde Islands. Through its unprecedented dataset of synergistic measurements in the region, ASKOS will allow for the calibration and validation of the aerosol/cloud product from Aeolus and the preparation of the terrain for EarthCARE cal/val activities. Moreover, ASKOS marks a turning point in our ability to study Saharan dust properties and the processes affecting its atmospheric transport, as well as the link to other components of the Earth's system, such as the effect of dust particles on cloud formation over the Eastern Atlantic and the effect of large and giant particles on radiation. This is possible through the synergy of diverse observations acquired during the experiment, which include intense 24/7 ground-based aerosol, cloud, wind, and radiation remote sensing measurements, and UAV-based aerosol in situ measurements within the Saharan air layer, up to 5.3 km altitude, offering particle size-distributions up to 40 µm as well as sample collection for mineralogical analysis. We provide an outline of the novel measurements along with the main scientific objectives of ASKOS. The campaign data will be publicly available by September of 2023 through the EVDC portal (ESA Validation Data Center). [ABSTRACT FROM AUTHOR]

In this work, we utilize space-based radar products from CloudSat mission and provide statistics on the properties of the clouds observed above the PANGEA (PANhellenic GEophysical observatory of Antikythera) observatory, located in the Eastern Mediterranean. We found that the variable atmospheric conditions that prevailed above the region in 2007-2017 resulted in complex cloud structures. From the clouds observed, 39.8% were low-level clouds formed at the top of the marine boundary layer (≤2 km), 34.2% were mid-level clouds (between 2-7 km), and 25.9% were high-level or deep convective clouds (between 7-15 km). Thin clouds (<1 km depth) are observed in 33% of the cases, while thick clouds (>6 km) in 15% of the cases. The results of this study can be used from regional and climate models to evaluate their cloud predictions and investigate the performance of different cloud microphysics schemes. [ABSTRACT FROM AUTHOR]

In this study, we evaluate NASA CloudSat products using ground-based measurements performed in the framework of the Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS). Combined ground-based lidar-radar observations performed in the framework of the PRE-TECT experiment during April 2017 at the Greek Atmospheric Observatory in Finokalia, Greece (35.338° N, 25.670° E, 250 m asl) are used to evaluate the CloudSat performance in detecting hydrometeors. A case study with a persistent thin high layer, mostly consisting of aspherical particles, is presented here. CloudSat CPR (Cloud Profiling Radar) observations detect the hydrometeor layer with a top higher than that detected by the collocated ground-based cloud Doppler radar system (9.0 vs. 8.4 km) and a base higher than that detected by the lidar system (7.1 vs. 6.5 km). The outcome of this work is a step towards the use of CloudSat products for performing a decade-long cloud statistical analysis over the poorly studied East Mediterranean region. [ABSTRACT FROM AUTHOR]

The Atlantic Meridional Overturning Circulation (AMOC) is a major oceanic circulation system in the Atlantic Ocean, which transports heat to the poles and cold saline waters to the tropics. The AMOC mechanism is responsible for many climate and weather systems; however, it is naturally unstable, and it changes in time depending on sea-ice melt, wind patterns, solar radiation variations, etc. At the same time, there are recent indications that the AMOC is in a weaker state than previously thought with impacts more severe than expected. Knowing that the AMOC can impact remote systems, and given the risk the Mediterranean faces with climate change progressing, we focus on the relationship between the AMOC and the Mediterranean climate, based on AMOC indicators and monthly ERA5 fields (sea surface temperature, air temperature and total precipitation) from 1959 to 2021. We find that the two systems exhibit a relationship although it is not homogenous. More specifically, the AMOC is highly correlated with sea and air temperatures of the central and eastern Mediterranean with a time lag of up to 2 years, while strong links are found with the entire Mediterranean's annual total precipitation up to 3 years ahead. The results suggest the possibility of predicting the occurrence of significantly extreme periods in the Mediterranean several months in advance. [ABSTRACT FROM AUTHOR]

Understanding the extent and location of snow cover is crucial for studying climate, hydrology, and ecosystems. Remote sensing, such as EUMETSAT's H-SAF project, enables snow-cover monitoring using SEVIRI satellite data. This study analyzes the spatial variability of snow cover in the southern Balkans over the past five years, focusing on the H10 product. Results reveal distinct seasonal dynamics, with limited snow cover during autumn and extensive cover during winter. January and February exhibit the highest snowfall probability and better snow cover persistence compared to autumn. Winter exhibits greater extent, duration, and quality of snow cover (>80% in December and January, ~70% in February). These findings contribute to regional climate understanding and water resource management. [ABSTRACT FROM AUTHOR]

Mineral dust particles are key ingredients of the atmosphere. They interact in atmospheric physics and chemistry and have important implications for human health. Therefore, it is important to examine the properties of these aerosols, including their ambient concentrations, size distributions, shape and mineral composition. In this work, we use satellite remote sensing from Sentinel 2A and EMIT missions to derive the mineralogical composition of surface areas, and we describe the development of a new module to represent the atmospheric life cycle of individual dust minerals in WRF-Chem. In the first step, the GMINER30 mineralogical database is implemented in WRF-Chem to describe the emission, transport, dry and wet deposition of different mineral types. [ABSTRACT FROM AUTHOR]

The limited availability of wind profile measurements has hindered atmospheric models and climate systems' understanding. The European Space Agency's Aeolus mission's space-based Doppler wind lidar technology could solve this issue by measuring wind profiles in Near-Real-Time, providing valuable data for Numerical Weather Prediction (NWP) models. A case study using the COSMO NWP model demonstrates the potential of Aeolus data in improving NWP models by examining the impact of Medicane IANOS in September 2020 over the Thessaly plain. The study aims to improve our ability to predict severe weather events and advance our understanding of Earth's atmosphere. [ABSTRACT FROM AUTHOR]

This study deals with the effect of clouds and aerosols on solar photovoltaic energy in the urban environments and conditions of Athens, Cairo, Granada and Vienna, so that there is diversity in terms of cloud presence, aerosol types and irradiation levels. To this direction, satellite-based remote sensing data were used for a decade (2010-2019) from Eumetsat in conjunction with Copernicus and radiative-transfer-modelled data. Furthermore, an idealized solar energy planning scenario, making the most of photovoltaics installed on the roofs of city buildings, was investigated in order to cover the electricity needs of the pilot cities and to promote local energy security and transition. [ABSTRACT FROM AUTHOR]

This study analyzes the structure of the Planetary Boundary Layer (PBL) at Mindelo, Cabo Verde, where the ASKOS Campaign took place from 2021 to 2022. Datasets from ground-based remote sensing instruments and radiosondes are used to derive the PBL height, by applying the Wavelet Covariance Transform (WCT), Threshold (TM), and Gradient Method (GM). Two case studies are described in detail, one with a significant dust load (23 September 2022) and one with relatively less dust load (12 September 2022). In the first case, the PBL top is found lower, and the methods used for the retrievals are characterized by larger uncertainties. In the second case, a higher and more convective PBL is observed. Additionally, results are compared with ECMWF outputs, establishing good agreement. [ABSTRACT FROM AUTHOR]

The Hellenic National Meteorological Service (HNMS) issues marine weather bulletins for the Mediterranean and the Black Seas as part of the Global Maritime Distress and Safety System, with wind forecasts being of high importance. This study evaluates the accuracy of HNMS marine forecasts during Wind Warning (WW) events when weather warnings were also issued for Greece. The analysis focuses on events that occurred over the Ionian and the Aegean Seas from September 2019 to February 2023. Remote sensing data are used to objectively verify the forecasted wind speed and sea state. An evaluation of the accuracy of numerical weather prediction products against satellite data during the weather system 'Barbara' (February 2023), that was also a WW event, is included. [ABSTRACT FROM AUTHOR]

Flash floods are considered one of the most devastating and frequent extreme climatological natural hazards in the world. El Minya is one of the most vulnerable areas in Egypt for flash flood problems. It was affected by several hazardous historical flash flood events. These events could lead to both catastrophic losses of life and severe damage to the infrastructures of the study area. The study area is located in the middle of Egypt, about 240 km south of Cairo. It is situated along the Limestone Plateau facing El Minia governorate. The main objective of this study is to assess the risk of flash flood hazard on the human activities in the study area. Integration of remote sensing; geographic information systems, analytical hierarchy process, and the multi-criteria decision analysis (MCDA) techniques were applied in this study. Eight different significant effective factors collected from multisource geospatial data, including lithology, hydrology, topography, soil type, land cover, and rainfall data, were selected to evaluate the flood risk map. Remote sensing imagery was used for land use/cover mapping to detect the vulnerable human activities. ArcGIS-based weighted overlay modeling was used to combine the criteria to calculate the final decision map. [ABSTRACT FROM AUTHOR]

In this study, operational decision support systems (DSSs) for irrigation water management that utilize data from weather stations (W/S) or weather data services are presented. The challenges and the ways in which various systems address them are summarized based on a review of the relevant scientific literature and information provided on the websites of the systems under consideration. The selected systems that are presented are categorized into those that utilize W/S data (IRMA_SYS, CIMIS, BlueLeaf, CoAgMet) as well as those that employ remote sensing data (Manna irrigation, Irrisat, Sencrop). Remote sensing DSSs are included in this study because their functionality is closely related to that of W/S-based systems, as it is explained in the study. Additionally, Foreca and OpenET are also examined as they provide data to DSSs for irrigation management. The discussion about the challenges encountered in the use of DSSs based on W/S data aims to stimulate further research and development in this field by the scientific community and system developers. [ABSTRACT FROM AUTHOR]

Remote sensing images are an essential tool for mapping the amount of flood inundation after flood events. For early flood estimation, flood mapping is a crucial component. This study used an integration of geospatial techniques to evaluate the flood extent in District Dera Ghazi Khan, Pakistan. The modified normalized difference water index (MNDWI) was utilized to estimate the flood extent using Landsat data. For a thorough flood investigation, pre-flood, during, and post-flood images were obtained. The analysis enabled us to delineate flood extent as well as flood duration. The result showed that the flood continued for nearly 5 weeks in the study area. This proposed geospatial technique provides a framework for the identification of inundated areas, which allows emergency responses to be focused on newly flooded areas. Hence, the current study offers a novel perspective on flood mapping and significantly contributes to flood monitoring. [ABSTRACT FROM AUTHOR]

Cyanobacteria can have a dramatic effect on the quality of water used for human activities, as reported by the World Health Organization. Remote sensing is an appropriate tool for continuous monitoring of the entire water body, given the current state of the lake. In the present study, surface water quality was evaluated using multi-temporal sentinel-2 images based on cyanobacteria's concentration of chlorophyll a (Chl-a) and the water's dissolved oxygen content. Chl-a was used as an indicator of cyanobacterial blooms, and dissolved oxygen was used as an indicator of water quality. Dissolved oxygen was generated using Sentinel 2 dataset. For the present study, two wetlands, Wadhwana and Timbi, in Vadodara City, Gujarat, India, were assessed from 2018 to 2022. Analysis showed that dissolved oxygen is an important environmental factor that influences cyanobacteria abundance. It was seen that the increased concentration of chlorophyll a was associated with a reduction in dissolved oxygen and hence deteriorated the water quality. [ABSTRACT FROM AUTHOR]

To meet the demand for increasing global food production while using limited water resources, crop water stress must be improved in agriculture. Remote-sensing-based plant stress indicators have the benefits of high spatial resolutions, a cheap cost, and short turnaround times. This study discusses the current advancements in agricultural water stress monitoring and irrigation scheduling, some of the challenges that have been met, and the upcoming research needs. Remote sensing systems are prepared to handle the intricate and technical evaluations of agricultural productivity, security, and crop water stress quickly and effectively. We explore the use of remote-sensing systems in the evaluation of crop water stress by looking at the existing research, technologies, and data. This study examines the connection between relative water content (RWC), equivalent water thickness (EWT), and agricultural water stress. Using remote sensing, evapotranspiration, and sun-induced chlorophyll content are examined in connection to crop drought. Spectral indices, remote sensing satellites, and multi-spectral sensing systems, as well as systems that measure land surface temperature, are examined. This critical study focuses on cutting-edge techniques for assessing crop water stress. [ABSTRACT FROM AUTHOR]

The role of effective irrigation management for optimal food production is well recognized and can be partially solved through the improvement of water use efficiency (WUE). To control the quantity of the water applied to actual crops, net irrigation water requirements (NIWR) are needed. The computation of NIWR is based on the estimation of crop water requirements (CWR) and soil water balance, where crop evapotranspiration (ETc) is the main component. Earth observation (EO) using remote sensing (RS) has already become an important tool for the quantification and the detection of spatial and temporal distributions and variability in several environmental variables at different scales. Remotely sensed models are currently considered to be suitable for crop water use estimation in the field and at regional scales. [ABSTRACT FROM AUTHOR]