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Satellite remote sensing (RS) data are increasingly being used to estimate total evaporation, often referred to as evapotranspiration (ET), over large regions. Since RS-based ET (RS-ET) estimation inherits uncertainties from several sources, many available studies have assessed these uncertainties using different methods. However, the suitability of methods and reference data subsequently affects the validity of these evaluations. This study summarizes the status of the various methods applied for uncertainty assessment of RS-ET estimates, discusses the advances and caveats of these methods, identifies assessment gaps, and provides recommendations for future studies. We systematically reviewed 676 research papers published from 2011 to 2021 that assessed the uncertainty or accuracy of RS-ET estimates. We categorized and classified them based on (i) the methods used to assess uncertainties, (ii) the context where uncertainties were evaluated, and (iii) the metrics used to report uncertainties. Our quantitative synthesis shows that the uncertainty assessments of RS-ET estimates are not consistent and comparable in terms of methodology, reference data, geographical distribution, and uncertainty presentation. Most studies used validation methods using eddy-covariance (EC)-based ET estimates as a reference. However, in many regions such as Africa and the Middle East, other references are often used due to the lack of EC stations. The accuracy and uncertainty of RS-ET estimates are most often described by root-mean-squared errors (RMSEs). When validating against EC-based estimates, the RMSE of daily RS-ET varies greatly among different locations and levels of temporal support, ranging from 0.01 to 6.65 mm d -1 , with a mean of 1.18 mm d -1. We conclude that future studies need to report the context of validation, the uncertainty of the reference datasets, the mismatch in the temporal and spatial scales of reference datasets to those of the RS-ET estimates, and multiple performance metrics with their variation in different conditions and their statistical significance to provide a comprehensive interpretation to assist potential users. We provide specific recommendations in this regard. Furthermore, extending the application of RS-ET to regions that lack validation will require obtaining additional ground-based data and combining different methods for uncertainty assessment. [ABSTRACT FROM AUTHOR]

The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The primary objective is to establish a standardised method applicable to different arid environments using the Google Earth Engine platform, considering local relief of terrain and seasonal differences in vegetation. This study integrates multispectral data from LANDSAT 5, Sentinel-2, SAR imagery from Sentinel 1, and TanDEM-X (12 m and 30 m) DSMs. Using these datasets, calculations of selected vegetation indices such as the SMTVI and NDVSI, spectral decomposition methods such as TCT and PCA, and topography-based methods such as the MSRM contribute to a comprehensive understanding of landscape irrigation. This paper investigates the influence of modern environmental conditions on the visibility of features like levees and palaeo-channels by testing different methods and parameters. This study aims to identify the most effective approach for each case study and explore the possibility of applying a consistent method across all areas. Optimal results are achieved by combining several methods, adjusting seasonal parameters, and conducting a comparative analysis of visible features. [ABSTRACT FROM AUTHOR]

Earth observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: atmosphere, water, agriculture, land, disaster risk reduction (DRR), cultural heritage, energy, marine safety and security (MSS), and big Earth data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high-resolution and near-real-time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems, and services. [ABSTRACT FROM AUTHOR]

The UK‐based Endangered Archaeology in the Middle East and North Africa (EAMENA) project uses remote sensing techniques to rapidly record and evaluate the status of archaeological and cultural heritage sites in the MENA region. Applying remote sensing methods to the archaeological landscapes of 20 countries, EAMENA is one of the largest documentation projects of its kind. Such a scope raises important ethical questions fundamental to the practice of remote‐sensed archaeology, and this paper contributes to this discussion by reflecting on EAMENA's unique role in this subfield. We present ethical issues and possible solutions related to remote sensing and archaeology, drawing on models developed within the humanitarian aid sector and postcolonial archaeology. In addition, we consider issues of national sovereignty and their relationship to the engagement of local communities. Finally, this paper examines the roles of data openness and open access policies as ethical factors and how EAMENA has addressed these so far. [ABSTRACT FROM AUTHOR]

The historic environment of north-west Saudi Arabia is likely to be severely impacted by the construction of the Neom mega-city complex and the development of the city's hinterland. This region, located east of the Gulf of Aqaba, has only been subject to limited archaeological survey in the past, limiting any assessment of this negative impact. This paper presents data collected through a remote sensing survey of the region undertaken by the Endangered Archaeology in the Middle East and North Africa (EAMENA) project, independent of the Neom project. It is argued that this data reflects a landscape of movement between north and south, with evidence of structures and monuments from at least the Bronze Age through to the pre-Islamic period, but little evidence of permanent settlement or agriculture. This should not be dismissed as a peripheral landscape, but one that contains rich archaeological evidence of concentrated activity, suggesting that some areas have held long-term significance to the people inhabiting and moving through this region. [ABSTRACT FROM AUTHOR]

Urbanisation, comprising development, land reclamation and population growth along coastal margins, continues to place significant pressure on the maritime cultural heritage (MCH), particularly in the Middle East and North Africa (MENA) region. Thus, there is a growing need for ascertaining the extent of the affected MCH resource and its condition. One such assessment is being undertaken by the Maritime Endangered Archaeology (MarEA) project, which is generating a unique informed database of the maritime resource in the MENA region. Through a regional overview combined with focused assessment on two case studies – Marsa Matruh (Egypt) and Bahrain – this paper demonstrates the threat urbanisation poses and the damage it has inflicted on MCH. The analyses and documentation that MarEA produces via remote sensing, desk-based and field-based assessments, constitutes a valuable resource that, at the very least, exists in digital perpetuity. It establishes a record that can be drawn upon to formulate targeted strategies and initiatives inclusive of the maritime cultural heritage resource. [ABSTRACT FROM AUTHOR]

Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales. [ABSTRACT FROM AUTHOR]

The Middle East is an area characterized by strong gradients in meteorological conditions and varied land cover. As a result, some of the hottest places in the world as well as very cold areas can be found in this region. The objective of this paper is to quantitatively assess land surface temperature (LST) variations in the countries of the Middle East to estimate the trends and determine whether relationships suggesting the impact of climate change can be observed. Spatiotemporal variability of LST was investigated through the analysis of MODIS Terra LST images (MOD11A2) from 2001 to 2018. The LST differences in both daytime series and seasonal and international means were assessed. The analyses showed that in spring (MAM), about 20,000 km2 of the study area has an LST higher than 50 °C, whereas 3700 km2 has LST lower than 0 °C. In summer (JJA), about 1 million km2 has LST higher than 50 °C. In the fall (SON), about 5 million km2 of the study area has a land surface temperature higher than 30 °C, with about 11 km2 hotter than 50 °C, whereas 320 km2 has LST lower than 0 °C. In winter (DJF), the hottest countries are Yemen, Oman, and Saudi Arabia, with 44.7, 41.8, and 39.8 °C, respectively, whereas the lowest LST values were recorded in Turkey, Iran, and Iraq, with −19.5, −18.1, and −10.5 °C, respectively. An upward trend in the minimum and a downward trend in the maximum value of the winter LST suggest that winters in the Middle East countries such as Iran, Israel, and Jordan have become milder during the considered period. Negative trends in the spring LST in Bahrain and Oman and the summer LST in Bahrain and Qatar suggest that these seasons in those countries became colder during the study period. [ABSTRACT FROM AUTHOR]

Satellite remote sensing (RS) data are increasingly being used to estimate total evaporation or evapotranspiration (ET) over large regions. Since RS-based ET (RS-ET) estimation inherits uncertainties from several sources, many available studies have assessed these uncertainties using different methods and reference data. However, the suitability of methods and reference data subsequently affects the validity of these evaluations. This study summarizes the status of the various methods applied for uncertainty assessment of RS-ET estimates, discusses the advances and caveats of these methods, identifies assessment gaps, and provides recommendations for future studies. We systematically reviewed 601 research papers published from 2011 to 2021 that assessed the uncertainty or accuracy of RS-ET estimates. We categorized and classified them based on (i) the methods used to assess uncertainties, (ii) the context where uncertainties were evaluated, and (iii) the metrics used to report uncertainties. Our quantitative synthesis shows that the uncertainty assessments of RS-ET estimates are not consistent and comparable in terms of methodology, reference data, geographical distribution, and uncertainty presentation. Most studies used validation methods using Eddy Covariance (EC) based ET estimates as reference. However, in many regions such as Africa and the Middle East, other references are often used due to the lack of EC stations. The accuracy and uncertainty of RS-ET estimates are most often described by Root-Mean-Squared Error (RMSE). When validating against EC-based estimates, the RMSE of daily RS-ET varies greatly among different locations and levels of temporal support, ranging from 0.01 to 6.65 mm/day with a mean of 1.12 mm/day. We conclude that future studies need to report the context of validation, the uncertainty of the reference datasets, the mismatch in temporal and spatial scales of reference datasets to that of the RS-ET estimates, and multiple performance metrics with their variation in different conditions and statistical significance to provide a comprehensive interpretation to assist potential users. We provide specific recommendations in this regard. Furthermore, extending the application of RS-ET to regions that lack validation will require obtaining additional ground-based data and combining different methods for uncertainty assessment. [ABSTRACT FROM AUTHOR]

Remote sensing has provided a modern wider perspective to approach the earth with its various environments and impact of humans by prospecting previously unknown frontiers of human life. The traces of mobile groups are archaeologically often more difficult to detect than those of the sedentary ones, but new approaches and methods have changed and enhanced the ways to extract archaeological information of hunter-gatherers and pastoral nomads. Remote sensing, for example, provides alternative views from above and better visibility in a larger scale, especially with high resolution solutions, than on the ground to trace sites. Mobile people have become more visible in archaeology, and therefore their importance in the development of human cultures has received more focus and understanding. This paper will focus on the use of remote sensing in the archaeological study of mobile cultures and their environments in the Near East. Various examples of techniques and site types will be discussed, and the suitability of applications will be considered based on the studies by Finnish and Finnish-Swedish projects in the Near East. We will provide examples of applications and emphasize the importance of empirical approaches in studying archaeological evidence by remote sensing. GPS coordinate points have served as the basis of our field survey and mapping. From the image-based data we shall deal with aerial photographs, CORONA satellite photographs, Landsat, SPOT, QuickBird and GeoEye satellite images. From the range-based data we shall discuss X-SAR Shuttle Mission 2000 and ASTER-DEM data, but LiDAR and geophysical devices will only be briefly considered. [ABSTRACT FROM AUTHOR]

The Middle East and North Africa have witnessed a surfeit of geospatial data collection projects, resulting in big databases with powerful deductive capacities. Despite the valuable insights and expansive evidentiary record offered by those databases, emphasis on anthropogenic threats to cultural heritage, combined with a limited integration of local perspectives, have raised important questions on the ethical and epistemological dimensions of big data. This paper contextualizes maritime cultural heritage (MCH) in those debates through the lens of the Maritime Endangered Archaeology in the Middle East and North Africa project (MarEA). MarEA is developing a unique for the region database for MCH designed to amalgamate a baseline record emphasizing spatial location, state of preservation, and vulnerability. This record will form a stepping stone toward finer-grained research on MCH and its interdisciplinary intersections. It is also developed as an information resource to facilitate local collaborators in prioritizing site monitoring and developing documentation, management, and mitigation strategies. [ABSTRACT FROM AUTHOR]

The desert regions of the Arabian Peninsula and Levant are criss-crossed by innumerable pathways. Across large areas of north-west Arabia, many of these pathways are flanked by stone monuments, the vast majority of which are ancient tombs. Recent radiometric dating indicates that the most abundant of these monuments, elaborate and morphologically diverse 'pendant' structures, were constructed during the mid-to-late third millennium BCE. Thousands of kilometres of these composite path and monument features, 'funerary avenues', can be traced across the landscape, especially around and between major perennial water sources. By evidencing routes of human movement during this period, these features provide an emerging source for reconstructing important aspects of ancient mobility and social and economic connectivity. They also provide significant new evidence for human/environment interactions and subsistence strategies during the later Middle Holocene of north-west Arabia, and suggest the parallel existence of mobile pastoralist lifeways and more permanent, oasis-centred settlement. This paper draws upon the results of recent excavations and intensive remote sensing, aerial and ground surveys in Saudi Arabia to present the first detailed examination of these features and the vast cultural landscape that they constitute. [ABSTRACT FROM AUTHOR]

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Declassified CORONA satellite imagery, collected from 1960–1972 as part of the world's first intelligence satellite program, provides nearly global, high-resolution, stereo imagery that predates many of the land-use changes seen in recent decades, and thus has proven to be an immensely valuable resource for archaeological research. While challenges involved in spatially correcting these unusual panoramic film images has long served as a stumbling block to researchers, an online tool called "Sunspot" now offers a straightforward process for efficient and accurate orthorectification of CORONA, helping to unlock the potential of this historical imagery for global-scale archaeological prospection. With these new opportunities come significant new challenges in how best to search through large imagery datasets like that offered by CORONA. In contrast to currently popular trends in archaeological remote sensing that seek to employ either automated, machine learning-based approaches, or alternatively, crowd-sourced approaches to assist in the identification of ancient sites and features, this paper argues for systematic, intensive, and expert-led "brute force" methods. Results from a project that has sought to map all sites and related features across a large study in the northern Fertile Crescent illustrate how an expert-led analysis may be the best means of generating nuanced, contextual understandings of complex archaeological landscapes. [ABSTRACT FROM AUTHOR]

Over recent decades, dust storms have had a severe impact on the Middle East at the local and regional scales. Remote sensing is a feasible method for exploring dust storm sources, and this study utilized SeaWiFS satellite data and GIS modeling to investigate the issue in the region. Aerosol optical depth was used as an indicator to detect dust events and identify sources. The results revealed an increase in dust sources in some areas of Iraq, Saudi Arabia, and Oman in recent years, while the situation remained stable in eastern neighbors. Four main areas were identified as high-intensity dust producers: the northern part of Iraq near Mosul and Baghdad, the common border of Kuwait, Saudi Arabia, and Iraq, the southeast of Saudi Arabia and the western border of the UAE, and the middle of Oman. To validate the results, the study used 35 AERONET ground stations, including 608 comparative data. The achieved results showed a correlation coefficient of 0.75 and RMSE error of 8.6% between the AERONET stations and the findings of the study. These results provide valuable insights into dust storm sources and trends in the Middle East, which can inform efforts to mitigate their impact on the region. [ABSTRACT FROM AUTHOR]

Mineral dust produced by wind erosion of arid and semiarid surfaces is a major component of atmospheric aerosol that affects climate, weather, ecosystems, and socioeconomic sectors such as human health, transportation, solar energy, and air quality. Understanding these effects and ultimately improving the resilience of affected countries requires a reliable, dense, and diverse set of dust observations, fundamental for the development and the provision of skillful dust-forecast-tailored products. The last decade has seen a notable improvement of dust observational capabilities in terms of considered parameters, geographical coverage, and delivery times, as well as of tailored products of interest to both the scientific community and the various end-users. Given this progress, here we review the current state of observational capabilities, including in situ, ground-based, and satellite remote sensing observations in northern Africa, the Middle East, and Europe for the provision of dust information considering the needs of various users. We also critically discuss observational gaps and related unresolved questions while providing suggestions for overcoming the current limitations. Our review aims to be a milestone for discussing dust observational gaps at a global level to address the needs of users, from research communities to nonscientific stakeholders. [ABSTRACT FROM AUTHOR]

This paper reviews key recent developments in geospatial technologies and their archaeological applications, an emerging area of research that can be termed "spatial archaeometry." I focus on three areas: 1) regional-scale satellite remote sensing of archaeological landscapes, 2) kite, balloon, and drone-based low-altitude aerial imaging, and 3) innovative approaches to archaeological geophysics at complex Near Eastern sites. Far from being simply a support to traditional archaeological investigations, these and other spatial archaeometric technologies offer powerful new means to explore the past, producing rich, robust, and large-scale datasets that could not be replicated through conventional fieldwork. [ABSTRACT FROM AUTHOR]

Improvements in agricultural water productivity with constrained water resources are often regarded a prerequisite to meet food demands of a growing world population. The WaPOR data portal was launched to monitor biomass, evapotranspiration and biomass water productivity in Africa and the Near East using remote sensing technologies. The WaPOR database shows spatial pixel variation in biomass, suggesting scope to improve water productivity at field level. The aim of this paper is to assess with regression analyses for different spatial and temporal scales whether spatial variability in biomass and evapotranspiration as revealed by WaPOR can be attributed to human influenceable factors, variations in local climate, or methodologically inherent inacuracies of the WaPOR data. Variation in biomass and evapotranspiration data was assessed through agronomic linear regression analyses, for two large-scale irrigated sugarcane estates in Ethiopia (Wonji) and Mozambique (Xinavane). In these cases 82–94% of the variation in biomass and evapotranspiration is attributed to crop photosynthetic efficiency (very large influence), local climate (large influence) and irrigation technology (small influence). The remaining unexplained spatial variability is small (6–18%) and falls within an error range of +/− 9%. In conclusion, WaPOR performed very well by neatly reproducing the conservative relationship between biomass and evapotranspiration, which also means there is very limited scope to improve biomass water productivity through WaPOR monitoring. Further research is recommended on the magnitude of WaPOR accuracy and other sources that explain variations in biomass and evapotranspiration. Applicability of the WaPOR database to monitor biomass water productivity was assessed. Spatial variability in biomass and evapotranspiration data largely stemmed from photosynthesis and local climate, factors farmers and water managers can hardly influence. [Display omitted] • WaPOR remote sensing database monitors biomass (B) and evapotranspiration (ET). • Variation in WaPOR data for B and ET was assessed through agronomic analyses. • Nearly all variation was attributed to crop, local climate and irrigation method. • Remaining (unexplained) variation falls within an accuracy range of +/− 9%. • Scope to improve biomass water productivity through WaPOR monitoring is very small. [ABSTRACT FROM AUTHOR]

Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food production. In addition, ASA regions in the Middle East and Africa are often characterised by political instability, which can increase population vulnerability to hunger and ill health. Remote sensing (RS) provides a platform to improve the spatial prediction of crop production and food availability, with the potential to positively impact populations. This paper, firstly, describes some of the important characteristics of agriculture in ASA regions that require monitoring to improve their management. Secondly, it demonstrates how freely available RS data can support decision-making through a cost-effective monitoring system that complements traditional approaches for collecting agricultural data. Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. Finally, existing approaches used in these applications are evaluated, and the challenges associated with their use and possible future improvements are discussed. We demonstrate that agricultural activities can be monitored effectively and both crop area and crop yield can be predicted in advance using RS data. We also discuss the future challenges associated with maintaining food security in ASA regions and explore some recent advances in RS that can be used to monitor cropland and forecast crop production and yield. [ABSTRACT FROM AUTHOR]

The Middle East is one of the world regions that has frequently suffered from armed conflicts that resulted in mass burials. However, the detection of clandestine graves in such an arid environment by deploying remote sensing payload on unmanned aerial vehicles (UAVs) has received little attention. The present study used a UAV equipped with a thermal sensor aimed at narrowing down the search area of possible gravesites in the arid climate of Kuwait. The enclosed research area, which includes both control and experimental mass graves, was imaged for 18 months. The variation in topsoil temperature and soil moisture between the graves and their surroundings was evaluated. The results of the analysis demonstrated the effectiveness of thermal imaging techniques in detecting heat produced from buried sheep carcasses and detecting the change in grave soil moisture for our research environment for 7 and 10 months, respectively. The buried animals significantly influenced the topsoil temperature (p = 0.044), while the height from which the images were captured had an insignificant effect on the measured temperature within the range tested (p = 0.985). Furthermore, there was a negative correlation (−0.359) between grave temperature and the calculated soil moisture. The results from these cost‐ and time‐effective search methods presented in this study confirm their potential for the detection of burial sites in an arid environment. [ABSTRACT FROM AUTHOR]

Climate change threatens coastal archaeology through storm flooding (extreme sea-level: ESL), long-term sea-level rise (SLR) and coastal erosion. Many regions, like the Middle East and North Africa (MENA), lack key baseline evidence. We present initial results from a climate change threat assessment of MENA's coastal heritage using the Maritime Endangered Archaeology inventory: a geospatial database of MENA maritime archaeological sites incorporating a disturbance/ threat assessment. It informs two analyses of past disturbance and future threat: (1) using the integral threat/disturbance assessment, and (2) geospatial extraction of information from external coastal change models. Analysis suggests <5% of documented coastal sites are definitely affected by coastal erosion but up to 34% could also have experienced past flooding, erosion, or storm action. Climate change-related threats will increase over the 21st Century and accelerate post-2050 if carbon emissions remain high. SLR and ESL could impact 14–25% of sites by 2050 and 18–34% by 2100. Over 30% to 40% of sites could be impacted by erosion by 2050 and 2100 respectively. Whilst documentation is ongoing and there remain modeling uncertainties, this approach provides a means to redress the absence of baseline data on climate change threats to coastal cultural heritage in MENA. [ABSTRACT FROM AUTHOR]

Increasing population size and economic dependence on the coastal zone, coupled with the growing need for residential, agricultural, industrial, commercial and green space infrastructure, are key drivers of land reclamation. Until now, there has been no comprehensive assessment of the global distribution of land use on reclaimed space at the coast. Here, we analyze Landsat satellite imagery from 2000 to 2020 to quantify the spatial extent, scale, and land use of urban coastal reclamation for 135 cities with populations in excess of 1 million. Findings indicate that 78% (106/135) of these major coastal cities have resorted to reclamation as a source of new ground, contributing a total 253,000 ha of additional land to the Earth's surface in the 21st century, equivalent to an area the size of Luxembourg. Reclamation is especially prominent in East Asia, the Middle East, and Southeast Asia, followed by Western Europe and West Africa. The most common land uses on reclaimed spaces are port extension (>70 cities), followed by residential/commercial (30 cities) and industrial (19 cities). While increased global trade and the rapid urbanization have driven these uses, we argue that a city's prestigious place‐making effort to gain global reputation is emerging as another major driver underlying recent reclamation projects to create tourist and green spaces Meanwhile, the study suggests that 70% of recent reclamation has occurred in areas identified as potentially exposed to extreme sea level rise (SLR) by 2100 and this presents a significant challenge to sustainable development at the coast. Plain Language Summary: Coastal regions face enormous pressure from growing human footprints, especially given the current rates of sea‐level rise. It is now recognized that seawards extension of the land through land reclamation and infrastructure development, at least partly in response to increased vulnerability induced by extreme storm surge events, is also a feature of recent coastal dynamics, especially in the world's coastal megacities Despite its growing global importance and reach worldwide, coastal reclamation is regarded as a local issue. Subsequently, the scale, intensity, and justification are not globally known, rather they are documented through localized case studies. By leveraging advanced satellite and cloud computing technology, this study aims to map the global state of 21st coastal land reclamation and to highlight the risk of such an anthropogenic footprint at the coast in the era of SLR. Key Points: 253,000 ha of additional land to the Earth's coastal surface in the 21st century, equivalent to an area the size of LuxembourgCoastal Reclamation is especially prominent in East Asia, the Middle East, and Southeast Asia, followed by Western Europe and West AfricaWe suggest that 70% of recent reclamation has occurred in areas identified as high risk to extreme SLR by 2100 [ABSTRACT FROM AUTHOR]

The variability of currently available remote sensing datasets raises the question of which specific processing methods should be used for feature detection and feature extraction in both large and small-scale overhead images. In some cases, particular analyses allow us to carry out feature detection much more easily and effectively. The high-definition approach enables enhanced analysis of remote sensing data using all the spectral and graphical potential of multi-temporal ordered components. The deserted urban site of Kona Makhmūr, Iraqi Kurdistan, is taken as a case study to demonstrate this fine-grained approach. The analysis described in this paper is based on the complementary use of a variety of overlayed imagery, augmented by data from terrestrial surveys. The resulting map substantially enhances our knowledge of the built environment and the economic and environmental conditions of this early Islamic-period site. Spectral analysis of raster images allowed us to detect the real shapes of features, and with the addition of digital elevation model (DEM) (created via unmanned aerial system (UAV)) we were also able to interpret the state of preservation of the overground relics and the diachronic dynamics of their degradation. [ABSTRACT FROM AUTHOR]

Irrigation monitoring is of great importance in agricultural water management to guarantee better water use efficiency, especially under changing climatic conditions and water scarcity. This study presents a detailed assessment of the potential of the Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data to detect irrigation events at the plot scale. The potential of the S1 data to detect the irrigation events was carried out using the Irrigation Event Detection Model (IEDM) over semi-arid and temperate oceanic climates in five study sites in south Europe and the Middle East. The IEDM is a decision tree model initially developed to detect irrigation events using the change detection algorithm applied to the S1 time series data. For each study site and at each agricultural plot, all available S1 images during the period of irrigation were used to construct an S1 time series and apply the IEDM. Different types of major summer irrigated crops were analyzed in this study, including Maize, Soybean, Sorghum and Potato, mainly with the sprinkler irrigation technique. The irrigation detection accuracy was evaluated using S1 images and the IEDM against the climatic condition of the studied area, the vegetation development (by means of the normalized difference vegetation index, NDVI) and the revisit time of the S1 sensor. The main results showed generally good overall accuracy for irrigation detection using the S1 data, reaching 67% for all studied sites together. This accuracy varied according to the climatic conditions of the studied area, with the highest accuracy for semi-arid areas and lowest for temperate areas. The analysis of the irrigation detection as a function of the crop type showed that the accuracy of irrigation detection decreases as the vegetation becomes well developed. The main findings demonstrated that the density of the available S1 images in the S1 time series over a given area affects the irrigation detection accuracy, especially for temperate areas. In temperate areas the irrigation detection accuracy decreased from 70% when 15 to 20 S1 images were available per month to reach less than 56% when less than 10 S1 images per month were available over the study sites. [ABSTRACT FROM AUTHOR]

Examining the ecology quality of the Pan-Third Pole region has implications for global environmental change and sustainable development. However, spatio-temporal evolution of ecological quality in this region is still lacking. In this study, 65 countries of the Pan-Third Pole region were selected. A comprehensive evaluation index system of ecological quality was constructed using a dataset containing remote sensing, ecological environment and socio-economic data to spatially quantify the ecological quality, as well as its change from 2000 to 2020. The results displayed that the average ecological quality of the Pan-Third Pole region was at a moderate level of 0.53. Spatially, the excellent ecological quality regions were mainly concentrated in East Asian countries, while the severe quality regions were located in the Middle East. From 2000–2020, areas with improved ecological changes accounted for 38.48% of the total area, and 10.66% of the total area experienced a decline; specifically, European countries had a large proportion of improved ecological quality areas, while East Asian countries had a significantly larger proportion of declining ecological quality areas. We also found that ecosystem changes and human activities had an influence on ecological quality in the Pan-Third Pole region. This study provides an important empirical study on ecosystem services in the region. [ABSTRACT FROM AUTHOR]

Building a Green Silk Road by integrating the Sustainable Development Goals (SDGs) is one of the Belt and Road Initiative (BRI) visions, but the BRI faces enormous challenge that is the conflict between economic development and ecological sustainability. Understanding the current scale and trend of the impact of human activities on the ecosystem is the preliminary work to ensure that human activities do not exceed the ecological carrying capacity under the BRI. This study evaluated the ecosystem pressure in countries along the Belt and Road (B&R) from 2000–2017 based on the supply-consumption balance relationship of ecological resources. Net primary productivity (NPP) is taken as the measure of ecological resources, and the supply level and consumption intensity of ecological resources is estimated based on remote sensing data and statistical data, respectively. Results show that thirteen countries with over-consumed ecological resources concentrated in the West Asia/Middle East. Although the intensity of the ecological resource consumption correlated with ecological resource endowments, the ecosystem pressure was determined by social development dependence on the ecological resources at the same ecological resource endowments level. Nearly 80% of countries along the B&R suffered from significantly increased (P < 0.05) ecosystem pressure during 2000–2017, since most of the countries along the B&R were developing countries, and their economic development was highly dependent on ecological resources. Some West Asia/Middle East countries successfully mitigated the ecosystem pressure by importing feed for livestock. Likewise, the Southeast Asian islands benefitted from the import of agricultural products. The results highlight that the BRI should reduce the dependence of social development demands on local ecological resources by international trade for ensuring the increasing ecosystem pressure trend within the ecological carrying capacity. [ABSTRACT FROM AUTHOR]

Several studies have reported the increased values of surface -latent heat flux (SLHF) and rainfall events prior to the earthquakes as the seismic precursors. In order to investigate the variation of two mentioned atmospheric variables, we analyzed 39 major earthquakes in the Middle East based on the time series of NASA remote sensing data. On this basis, we observed that accumulated rainfall and SLHF of about 29 earthquakes were higher than 10 mm and 50 W/m, respectively (~75 %), over 3-23 days prior to the main shock of major earthquakes. We believed that the records of atmospheric variables are the consequence of a seismic-triggered chain including of air ionization, surface -latent heat exhalation, water vapor condensation and subordinate rainfall as the atmospheric responses to lithospheric motions. This seismic triggering in the Middle East has averagely caused to accumulated rainfall of ~35 mm and maximum SLHF of ~115 W/m over the 3-23 days prior to 39 major earthquakes. To investigate of spatial correlation between earthquakes and atmospheric variations, we clustered 39 major earthquakes in eight seismological regions. Then, we estimated the moderate and strong correlation ( R) between preceding times of earthquakes with magnitude of major earthquakes and their hypocenter depth equal to 0.48 and 0.68, respectively. [ABSTRACT FROM AUTHOR]

Global change impacts including climate change, increased CO2 and nitrogen deposition can be determined through a more precise characterisation of Land Surface Phenology (LSP) parameters. In addition, accurate estimation of LSP dates is being increasingly used in applications such as mapping vegetation types, yield forecasting, and irrigation management. However, there has not been any attempt to characterise Middle East vegetation phenology at the fine spatial resolution appropriate for such applications. Remote-sensing based approaches have proved to be a useful tool in such regions since access is restricted in some areas due to security issues and their inter-annual vegetation phenology parameters vary considerably because of high uncertainty in rainfall. This study aims to establish for the first time a comprehensive characterisation of the vegetation phenological characteristics of the major vegetation types in the Middle East at a fine spatial resolution of 30 m using Landsat Normalized Difference Vegetation Index (NDVI) time series data over a temporal range of 20 years (2000–2020). Overall, a progressive pattern in phenophases was observed from low to high latitude. The earliest start of the season was concentrated in the central and east of the region associated mainly with grassland and cultivated land, while the significantly delayed end of the season was mainly distributed in northern Turkey and Iran corresponding to the forest, resulting in the prolonged length of the season in the study area. There was a significant positive correlation between LSP parameters and latitude, which indicates a delay in the start of the season of 4.83 days (R2 = 0.86, p < 0.001) and a delay in the end of the season of 6.54 days (R2 = 0.83, p < 0.001) per degree of latitude increase. In addition, we have discussed the advantages of fine resolution LSP parameters over the available coarse datasets and showed how such outputs can improve many applications in the region. This study shows the potential of Landsat data to quantify the LSP of major land cover types in heterogeneous landscapes of the Middle East which enhances our understanding of the spatial-temporal dynamics of vegetation dynamics in arid and semi-arid settings in the world. [ABSTRACT FROM AUTHOR]

North Sinai is of significant historical interest primarily because of its role since late prehistoric times as a land bridge between Egypt and the Levant. Access to this region is challenging due to its harsh geography and security concerns. Remote sensing constitutes a convenient method for archaeological prospection and monitoring over such regions with its low cost (relative to ground based sensing techniques), global coverage, and high temporal and spatial sampling. This paper describes part of a study to revisit a number of sites investigated during the North Sinai Survey (1972–1982) with very high resolution optical and Synthetic Aperture Radar satellite imagery. These were acquired throughout the summer of 2017 in the framework of a European Space Agency research project. The Synthetic Aperture Radar data includes Spotlight and Staring Spotlight modes of the TerraSAR-X mission, while the optical imagery was acquired by the Pleiades mission. The TerraSAR-X data were processed to derive filtered amplitude and consecutive coherence time series. The results of the TerraSAR-X data processing, and the pan-sharpened Pleiades data were compared with the results of the North Sinai Survey to detect possible additional buried structures in the radar data, or newly excavated sites in the optical data. While the analysis is still ongoing, results are reported here of the Qasrawet archaeological site, which was partially investigated by the North Sinai Survey expedition, but assumed to cover a much larger area. Herein, a number of newly excavated structures are apparent in the remote sensing data. The similarity of features in both the TerraSAR-X and Pleiades data suggest that all structures are surface residues, and therefore, that the subsurface mapping capabilities of the TerraSAR-X data in this area are limited. The utility of both data types for archaeological site monitoring are discussed. [ABSTRACT FROM AUTHOR]

Abstract: Radial systems of spoke-like pathways, often termed “hollow ways,” are frequently found surrounding mounded tell sites in northern Mesopotamia and have been explained as the product of a particular set of land use practices involving dry-farming agriculture and intensive ovicaprid pastoralism. Yet while similar subsistence strategies were very common across the Near East throughout much of the Holocene, classic hollow ways have only been previously documented in a small region and articulate almost exclusively with sites of the third millennium BC. This paper presents newly discovered hollow ways in western Syria and southwestern Iran, made possible through analysis of an online database of declassified, Cold War-era CORONA satellite imagery. The association of these previously undocumented ancient roads with archaeological sites dating to the Iron Age, Roman/late Roman and early medieval periods, suggests that the land use practices which produced radial route systems may have been quite widespread. Taking into account the wide geographic and temporal distribution of hollow ways, analysis explores various aspects of the agro-pastoral systems that disparate communities may have shared. Results confirm some aspects existing models of hollow way formation, while offering some refinements in terms of the roles that settlement organization, agricultural land use and pastoral strategies play. [Copyright &y& Elsevier]

Abstract: This paper describes different change detection techniques, including image differencing, image rationing, image regression and change vector analysis (CVA) to assess their effectiveness for detecting land use/cover change in a Mediterranean environment. Three Landsat TM scenes recorded on 7 July 1985, 27 July 1993 and 21 July 2005 were used to minimize change detection error introduced by seasonal differences. Images were geometrically, atmospherically and radiometrically corrected. The four change detection techniques were applied and an object-based supervised classification was used as a crossclassification to determine ‘from–to’ change which enabled assessment of the four techniques. The change vector analysis resulted in the largest overall accuracy of 75.25 and 75.55% for the 1985–1993 and 1993–2005 image pairs, respectively. The ratio yielded the least accurate results with an overall accuracy of 59.10 and 61.05% for the 1985–1993 and 1993–2005 image pairs, respectively. Different change detection algorithms have their own merits and advantages. However, the change vector analysis change detection technique was the most accurate model for handling the variability present in Mediterranean land use/cover. [Copyright &y& Elsevier]

Most urban areas in the Middle East and North Africa (MENA) region have experienced unprecedented growth rates over the past few decades to absorb population increase and hefty migration from surrounding rural and/or politically unstable areas. Such expedited urbanization presents substantial stresses to these urban areas' ecological and financial resources, as well as to the overall well-being of their residents. This paper studies the urban growth pattern in five major cities in the MENA region including Dubai (United Arab Emirates (UAE)), Cairo (Egypt), Doha (Qatar), Casablanca (Morocco), and Riyadh (Kingdom of Saudi Arabia (KSA)). The study adopts a machine learning (ML)-based modeling framework, which integrates remote sensing and computer vision technologies to generate high-fidelity urban growth prediction with limited data requirements. The framework treats successive satellite images for the urban area under study as a video for which a future frame is constructed to present the predicted growth in a specific target year. The methodology is shown to produce growth prediction results that are consistent with previous studies conducted for these five cities. The obtained results are used to derive several recommendations to assist in developing sustainable growth policies for these cities. • Studying the urban growth pattern for five major cities in the MENA region • Machine learning model that integrates remote sensing and computer vision technologies • Model validation using the results of previous studies conducted for the region • Providing urban growth prediction capabilities for urban planners and policy makers • Deriving recommendations to develop sustainable growth policies for MENA cities and other cities around the world [ABSTRACT FROM AUTHOR]

Urmia Lake is the largest lake in the Middle East which has greatly decreased in recent years. It has had a great impact on the environmental properties of areas around the lake. This study aimed to identify the relationship among dust storms in the eastern part of Urmia Lake and the surrounding vegetation due to the annual and long-term variability of this water zone during the statistical period 1999–2019. Normalized difference water index (NDWI), normalized difference vegetation index (NDVI), Aerosol Optical Depth (AOD), and Dust Storm Index (DSI) were used due to trend and correlation analysis models and T-student analysis. The study results found that, first, the decreasing trend of Urmia Lake, directly and significantly, has led to an increase in dust concentration (AOD) in the lake's eastern part. On the other hand, changes in the water level of Lake Urmia have led to a general decreasing trend in vegetation in the region which led to an increase in the frequency of DSI in the region. The model developed showed that changes in the water zone of Urmia Lake as a very powerful driver leads to an increase in the intensity and frequency of dust storms in the eastern part of the lake and intensifies the dust conditions in the region. The synergistic resultant of these two drivers was maximized in years when both lake area and vegetation were at a minimum at the same time (2011–2016). The estimated scenario of this model is that if the decreasing trend of Urmia Lake intensifies in the study area, more frequent and severe dust storms will occur in the region. [ABSTRACT FROM AUTHOR]

An objective method was developed to detect local cells of the Siberian high (SH) over the Middle East, and mean sea level pressure (SLP) data from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset were used to specify the centers of local SH cells and associated ridges. The results indicated that approximately 25.8% of winter SH cells influence the Middle East, and this influence diminishes from December to February. Synoptically, the area between the Black and Caspian Seas represents a remote area for sensing the influences of the SH on the Middle East. Although the SH is considered a shallow system, the variability of the effectiveness of the SH throughout the Middle East was estimated from the integration of atmospheric systems at the whole‐atmosphere scale (from the surface to 200 hPa). Further, this study demonstrated that the westward extension of the SH over the Middle East is associated with weak cyclogenesis in the area between the Black and Caspian Seas; that is, the atmospheric systems are favourable for strengthening the upper‐layer ridge in this area. However, the eastward retreat of the SH extension from the Middle East is associated with strong cyclogenesis in the area between the Black and Caspian Seas; that is, the atmospheric systems are unfavourable for strengthening the upper‐layer ridge in this area. This study revealed the importance of the SH in the generation of the pressure gradient over the Arabian Peninsula and the associated development of dust storms and the Red Sea Trough. [ABSTRACT FROM AUTHOR]

over the last decade, Amman's local authorities have faced challenges in meeting the needs of local populations due to unexpected rapid urbanization growth and the impact this has had on welfare and outdated urban infrastructure. Therefore, it is essential to model spatial dynamic growth to identify the land-use class of urbanized regions expansions to prepare for future urban demands. The present study addresses this need by exploring the application of the remotely- sensed data and cellular automation (CA) based SLEUTH model and its effectiveness in modelling of urban growth in highly Vibrant Metropolitan of Amman. the SLETUH model for Amman was successfully calibrated using the Optimum SLEUTH Metric (OSM) method based on archived remote sensing data from 1985 to 2015. The urban growth characteristics were determined using the urban rules derived from SLEUTH via calibration. SLEUTH's probabilistic image of future urbanized lands of historical trend-based urban growth scenario was employed as dynamic factors for urbanization suitability mapping and landscape pattern analysis of the year 2040. The SLEUTH model predicts that Amman's urban space will expand by 57km2 by 2040 (compared to 2015), resulting in a total area of 473.845 km2. Importantly, this research demonstrates that the issues experienced in Amman's urban locations today will continue into the next two decades unless steps are taken to improve urban growth strategies and increase both environmental and urban action. Findings of the present study cooperate the government's plan to demonstrate Jordan's commitment to modernisation through the creation a new administrative city for national-level civil servants and government ministries. [ABSTRACT FROM AUTHOR]

Over the last 8000 years the Fertile Crescent of the Near East has seen the emergence of urban agglomerations, small scale polities and large territorial empires, all of which had profound effects on settlement patterns. Computational approaches, including the use of remote sensing data, allow us to analyse these changes at unprecedented geographical and temporal scales. Here we employ these techniques to examine and compare long term trends in urbanisation, population and climate records. Maximum city size is used as a proxy for the intensity of urbanisation, whilst population trends are modelled from settlement densities in nine archaeological surveys conducted over the last 30 years across the region. These two measures are then compared with atmospheric moisture levels derived from multiple proxy analyses from two locations close to the study area, Soreq Cave in Israel and Lake Van in south-eastern Turkey, as well as wider literature. The earliest urban sites emerged during a period of relatively high atmospheric moisture levels and conform to a series of size thresholds. However, after the Early Bronze Age maximum urban size and population levels increase rapidly whilst atmospheric moisture declines. We argue that although the initial phase of urbanization may have been linked to climate conditions, we can see a definitive decoupling of climate and settlement patterns after 2000 BC. We relate this phenomenon to changes in socio-economic organisation and integration in large territorial empires. The complex relationships sustaining urban growth during this later period resulted in an increase in system fragility and ultimately impacted on the sustainability of cities in the long term. [ABSTRACT FROM AUTHOR]

Despite the extensive records on geomorphological studies in Iran, meteorite impact craters have so far not been considered in to account. Based on both remote sensing technique and field work we have recognized the circular structure of Zirouki crater in the Samsour desert, southeast of Iran, which if confirmed as an impact structure, would be the third impact structure candidate in the Middle East after the Wabar craters in Saudia Arabia and Jebel Waqf as Suwwan in Jordan. Geomorphological investigation of the possible impact structure of Zirouki crater was done based on multi criteria methodology including of geological, topographical, geophysical and petrographical studies. Among different studies, topographical investigation indicated that the crater shape morphology was quite obvious with a central uplift projection; as well geophysical pattern provided very strong evidence for possible impact structure, indicating the presence of circular negative gravity anomaly at the whole of the crater. [ABSTRACT FROM AUTHOR]

Britain and the Middle East were the two areas in which the technique of aerial archaeology was pioneered in the 1920s. Overwhelmingly the latter took place in Syria where a French Jesuit priest, Antoine Poidebard, worked for a generation. Further south in Transjordan - as it then was - three men presided over a brief (1927-1929) but fruitful period: an archaeologist with a passion for the newly developing technique (O.G.S. Crawford) and two pilots of the Royal Air Force with an amateur interest in what they saw beneath their wings (Flt. Lt. P.E. Maitland and Gp/Capt L.W.B. Rees). An aerial photograph published at the time seemed to identify a 'village' as prehistoric. Over eighty years later and just after it was destroyed, a fresh examination and the rediscovery of some crucial dating evidence have combined to identify it as, probably, an early Islamic village on the desert fringe. [ABSTRACT FROM AUTHOR]

In the past decade there has been a shift of focus from individual archaeological sites to an approach that incorporates the dynamic interplay of land, climate, society, economy, ritual and technical innovation. A growing understanding of past climates and environments, coupled with the use of satellite technology and other means of remote sensing, has opened new avenues of interpretation. Classic problems, such as the origins and spread of agrarian societies, have benefited from an array of new scientific methods, and there is increasing attention to social and ritual aspects of society. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is well demonstrated to be a powerful tool for investigating ancient land use in Southwest Asia. However, few regional studies have systematically integrated satellite-based observations with more intensive remote sensing technologies, such as drone-deployed multispectral sensors and ground-based geophysics, to explore off-site areas. Here, we integrate remote sensing data from a variety of sources and scales including historic aerial photographs, modern satellite imagery, drone-deployed sensors, and ground-based geophysics to explore pre-modern land use along the Upper Diyala/Sirwan River in the Kurdistan Region of Iraq. Our analysis reveals an incredible diversity of land use features, including canals, qanats, trackways, and field systems, most of which likely date to the first millennium CE, and demonstrate the potential of more intensive remote sensing methods to resolve land use features. Our results align with broader trends across ancient Southwest Asia that document the most intensive land use in the first millennium BCE through the first millennium CE. Land use features dating to the earlier Bronze Age (fourth through second millennium BCE) remain elusive and will likely require other investigative approaches. [ABSTRACT FROM AUTHOR]

The present study focuses on the validation and comparison of eight different physical models for soil salinity mapping in an arid landscape using two independent Landsat-Operational Land Imager (OLI) datasets: simulated and image data. The examined and compared models were previously developed for different semi-arid and arid geographic regions around the world, i.e., Latino-America, the Middle East, North and East Africa and Asia. These models integrate different spectral bands and unlike mathematical functions in their conceptualization. To achieve the objectives of the study, four main steps were completed. For simulated data, a field survey was organized, and 100 soil samples were collected with various degrees of salinity levels. The bidirectional reflectance factor was measured above each soil sample in a goniometric laboratory using an analytical spectral device (ASD) FieldSpec-4 Hi-Res spectroradiometer. These measurements were resampled and convolved in the solar-reflective bands of the Operational Land Imager (OLI) sensor using a radiative transfer code and the relative spectral response profiles characterizing the filters of the OLI sensor. Then, they were converted in terms of the considered models. Moreover, the OLI image acquired simultaneously with the field survey was radiometrically preprocessed, and the models were implemented to derive soil salinity maps. The laboratory analyses were performed to derive electrical conductivity (EC-Lab) from each soil sample for validation and comparison purposes. These steps were undertaken between predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab) in the same way for simulated and image data using regression analysis (p ˂ 0.05), coefficient of determination (R2), and root mean square error (RMSE). Moreover, the derived maps were visually interpreted and validated by comparison with observations from the field visit, ancillary data (soil, geology, geomorphology and water table maps) and soil laboratory analyses. Regardless of data sources (simulated or image) or the validation mode, the results obtained show that the predictive models based on visible- and near-infrared (VNIR) bands and vegetation indices are inadequate for soil salinity prediction in an arid landscape due to serious signals confusion between the salt crust and soil optical properties in these spectral bands. The statistical tests revealed insignificant fits (R2 ≤ 0.41) with very high prediction errors (RMSE ≥ 0.65), while the model based on the second-order polynomial function and integrating the shortwave infrared (SWIR) bands provided the results of best fit, with the field observations (EC-Lab), yielding an R2 of 0.97 and a low overall RMSE of 0.13. These findings were corroborated by visual interpretation of derived maps and their validation by comparison with the ground truthing. [ABSTRACT FROM AUTHOR]

The snow-fed river basins of the Near East region are facing an urgent threat in the form of declining water resources. In this study, we analyzed several remote sensing products (optical, passive microwave, and gravimetric) and outputs of a meteorological reanalysis data set to understand the relationship between the terrestrial water storage anomalies and the mountain snowpack. The results from different satellite retrievals show a clear signal of a depletion of both water storage and the seasonal snowpack in four basins in the region. We find a strong reduction in terrestrial water storage over the Gravity Recovery and Climate Experiment (GRACE) observational period, particularly over the higher elevations. Snow-cover duration estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) products point towards negative and significant trends up to one month per decade in the current era. These numbers are a clear indicator of the partial disappearance of the seasonal snow-cover in the region which has been projected to occur by the end of the century. The spatial patterns of changes in the snow-cover duration are positively correlated with both GRACE terrestrial water storage decline and peak snow water equivalent (SWE) depletion from the ERA5 reanalysis. Possible drivers of the snowpack depletion are a significant reduction in the snowfall ratio and an earlier snowmelt. A continued depletion of the montane snowpack in the Near East paints a bleak picture for future water availability in this water-stressed region. [ABSTRACT FROM AUTHOR]

Date palms are a valuable crop in areas with limited water availability such as the Middle East and sub-Saharan Africa, due to their hardiness in tough conditions. Increasing soil salinity and the spread of pests including the red palm weevil (RPW) are two examples of growing threats to date palm plantations. Separate studies have shown that thermal, multispectral, and hyperspectral remote sensing imagery can provide insight into the health of date palm plantations, but the added value of combining these datasets has not been investigated. The current study used available thermal, hyperspectral, Light Detection and Ranging (LiDAR) and visual Red-Green-Blue (RGB) images to investigate the possibilities of assessing date palm health at two "levels"; block level and individual tree level. Test blocks were defined into assumed healthy and unhealthy classes, and thermal and height data were extracted and compared. Due to distortions in the hyperspectral imagery, this data was only used for individual tree analysis; methods for identifying individual tree points using Normalized Difference Vegetation Index (NDVI) maps proved accurate. A total of 100 random test trees in one block were selected, and comparisons between hyperspectral, thermal and height data were made. For the vegetation index red-edge position (REP), the R-squared value in correlation with temperature was 0.313 and with height was 0.253. The vegetation index—the Vogelmann Red Edge Index (VOGI)—also has a relatively strong correlation value with both temperature (R2 = 0.227) and height (R2 = 0.213). Despite limited field data, the results of this study suggest that remote sensing data has added value in analyzing date palm plantations and could provide insight for precision agriculture techniques. [ABSTRACT FROM AUTHOR]