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Downscaling remote sensing-based Land Surface Temperature (LST) datasets is of paramount importance for multiple research fields including urban heating, irrigation scheduling, volcanic monitoring, to name a few. Even with the constant development in technology, especially improvements in spatial, temporal and spectral resolutions of satellite sensors, global satellites are limited to a 60-m LST datasets at best. In this study, we use the massive computation power and large dataset directory found in the Google Earth Engine (GEE) platform to design a 1-m fully-automated, open-source and user-friendly LST downscaling system, named High Spatial Resolution-GEE or HSR-GEE. It has the ability to downscale Landsat-8 LST, combined with the National Agriculture Imagery Program (NAIP) images, into 1-m HSR LSTs over the CONUS and at Landsat-8 overpass time. Using only red, green, blue and near infrared bands, HSR-GEE implements multiple machine learning approaches, including, Robust Least Square (RLS), Random Forests (RF), and Support Vector Machine (SVM), along with comparison to two commonly-known and classical methods: the disaggregation procedure for radiometric surface temperature (DisTrad) and thermal sharpening (TsHARP). We validate HSR-GEE outputs against multiple airborne thermal images over the USA. We obtained a MAE of 1.92 °C, 2.53 °C, 1.33 °C, 3.42 °C and 3.4 °C for the RLS, RF, SVM, DisTrad and TsHARP, respectively. With RF showing visually salt and pepper effect and SVM a Land Cover/Use form, the RLS appears to be most suited for 1-m LST downscaling. HSR-GEE is proposed as a high-potential system aiding researchers from different backgrounds to advance their research. HSR-GEE remains the only available 1-m GEE-based downscaling system that is able to derive the needed high resolution LST information in a matter of seconds and in five different approaches (i.e., RLS, RF, SVM, DisTrad and TsHARP). The research community is invited to implement this dynamic system over CONUS and enhance it if deemed necessary.

The evaporation of water is an indispensable process in the earth water cycle. While high rates of evaporation made access to water resources harsher and costlier, monitoring the spatial and tempor...

International audience; The widespread usage of the surface balance energy models coupled with remote sensing techniques proved their effectiveness in terms of accurately assessing evapotranspiration rates, crop water requirements as well as crop water usage/productivities. Still, such models require diverse inputs/elements, including climatic, topo-graphic, soil, and remote sensing datasets. The availability of these inputs is questionable and outdated in many developing countries. Thus, an Improved Surface Energy Balance Algorithm for Land (SEBAL), also known as SEBALI, was proposed and adapted for regions lacking soil-related datasets. Still, the usage of a standard calibration method in the surface energy balance models, yielding constant value throughout the cropping season, requires further revision and improvement. In this context, this study proposed a novel dynamic calibration approach to be included within SEBALI, related to the actual wind speed and relative humidity conditions. It was followed by the calibrations and validations of monthly evapotranspiration SEBALI values. Datasets were retrieved randomly from four countries (i.e. Belgium, Germany, Italy and United States) representing different climatic zones, between 2013 and 2014, and based on Eddy Covariance flux towers' outputs. When calibrated, results showed a Root Mean Square Error (RMSE) of 21.32 mm and an Average of Mean Error (AME) of 15.4 mm between monthly SEBALI outputs and flux towers' datasets, with an R-squared value of 88.4%. When investigating SEBALI outputs on different buffered zones around the flux towers along a changing Normalized Difference Vegetation Index (NDVI), ET values were poorly correlated (i.e. R-squared lower than 60%) in any buffer zone outside the parcels' boundary. RMSE showed values larger than 40 mm/month, even at a buffer zone of 250 m. This was related to the diverse Land Use Land Cover (LULC) classes, generating different evapo-transpiration rates, found at the boundary of the selected parcels. With the proposed dynamic calibration, the enhanced SEBALI could be then implemented in any agricultural region missing soil-related datasets with high accuracy.

The Middle East-North Africa (MENA) region has a prevailing hot and dry climate. The increasing demographic pressure brings an increased demand on water resources. In this study, we analyzed the drought dynamics using precipitation and soil moisture remote sensing data over the MENA region in the last two decades. Two types of remotely sensed micro-wave datasets (i.e. TRMM 3B43 and ESA-CCI SM) were used, to depict meteorological and agricultural droughts. Our results showed that the Standardized Precipitation Index (SPI) [1] from TRMM showed a good correlation with SPI from in-situ rainfall data and predicted successfully historical droughts. The standardized index shows lesser extreme agricultural droughts than meteorological drought for the same period. The region faced the biggest number of extreme meteorological droughts in 2015 and the biggest number of extreme agricultural droughts in 2013. These outputs help local and regional water management authorities as well as policy makers in combating water scarcity in the region. Further studies will look into optical remote sensing.

International audience; The promotion of optimum rotations and agricultural management of winter wheat-based cropping systems is very critical, as wheat is considered an essential component in the Mediterranean diet. Considering the delicate economic situation of farmers in the Mediterranean area, recommending a low risk, sustainable farming system is desirable. In this study, an innovative application of a multi-criteria field-level approach is presented, targeting food security, farmer profitability and environmental sustainability. The CropSyst biophysical simulation model was calibrated and implemented for the study site. It was chosen for its agro-environmental robustness to simulate four rotations (wheat-wheat, wheat-fallow, wheat-potato, and wheat-fava bean). Four types of wheat agricultural management systems (full fertilization and full irrigation, full fertilization and zero irrigation, zero fertilization and full irrigation, and zero fertilization and irrigation) were tested in low and high soil water holding capacity (WHC) types. The effects of soil conditions, management practices and rotation type on wheat grain yields were assessed. Furthermore, the performance of each winter wheat-based cropping system was evaluated in terms of productivity (protein production and profitability) and the efficient use of resources (nitrogen and water), as well as the economic risk of low relative productivity each one engenders. The results show that there is no particular optimal scenario that can simultaneously ensure high productivity, reduce economic risk of low relative productivity, and achieve high wheat- water- and nitrogen-use efficiency. However, the wheat-fava bean rotation cultivated with no wheat fertilization appeared to be a better substitute to the wheat-wheat rotation in terms of protein production (0.93 t/ha versus 0.8 t/ha in low WHC soil and 1.34 t/ha versus 1.17 t/ha in high WHC). This cropping system achieved a higher net profit (2111 US$/ha versus 1222US$/ha in low WHC and 3550 US$/ha versus 2450 US$/ha in high WHC), showing high resource-use efficiency and was less risky for farmers. Moreover, a very high profit could only be attained with the wheat-potato rotation (8640 US$/ha and 12,170 US$/ha in low and high WHC, respectively), yet with low input-efficiency and high economic risk of low relative productivity.

The sea-level rise phenomenon affects several socio-economic and ecological aspects worldwide, particularly in terms of coastal erosion and saltwater intrusion. While the Mediterranean region is showing an increase vulnerability to the sea-level rise, in this study, we implement the Coastal Vulnerability Index (CVI), between 2005 and 2013, on the Southern Lebanese coast. The selection of this study area is related to its commonly known vulnerable sandy beaches. The CVI is derived from six physical and geological variables that are estimated using Geographical Information System and Remote Sensing techniques. Results show that this eastern Mediterranean coast has a moderate (66% of its total lengths) to high coastal vulnerability (30% of its total lengths) to the sea-level rise. As predicted, sandy beaches in our study area represent the most vulnerable regions. On the other hand, an absence of a Pearson correlation between the amount of the sea level rise and the sea surface temperature, is shown. It is probably related to the direct human interference that eclipsed the climate change as the main driving force in that period (i.e. 2005–2013). In this context, we propose an intervention strategy that focuses on offering ’soft’ technics performed at the sandy beaches and in dune systems. Stakeholders are encouraged to implement this approach to combat the coastal vulnerability of these areas. And by applying the proposed method to other limited-resources/datasets countries, regional organizations and institutions could have vital information to reduce the impact of the SLR at a larger-scale.

Global and regional climate models tend to underestimate temperatures over urban areas. This limitation is exacerbated in complex-topography regions, where coarse spatial resolutions of climate models fail to detect large apparent change in cities' microclimates. In this study, a night air temperature downscaling approach was proposed in cities with heterogeneous- and complex-topography. It was developed based on six ground-based national weather station datasets along the 1-km remote-sensing-based MODIS land surface temperature (LST) data, between 2000 and 2020. An ordinary least square (OLS) statistical test was applied, including the 1-km MODIS normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), evapotranspiration (ET) and the digital elevation model (DEM) datasets as independent variables. Based on the generated regression relationship and a five-year pixel-based trend analysis, the 25-km NASA NEX-GDDP datasets were downscaled into 1-km spatial resolution night air temperatures between 2030 and 2072 and under two scenarios (i.e. RCP 4.5 and 8.5), averaged from 21 CMIP5 models. Validations were carried out showing good results with an RMSE of 0.49 °C, an AME of 0.42 °C and an R-squared value of 89.5%. Further assessment was made over the largest cities in Lebanon, namely Beirut, Tripoli, Zahle and Baalbeck. Main results showed that, on average, urban night air temperatures under the intermediate RCP 4.5 scenario are expected to increase by 0.12 °C or 0.61% in 2030, 0.36 °C or 1.82% in 2050 and 0.6 °C or 3.07% in 2070. Under the worst-case climate change RCP 8.5 scenario, the average increase would be an overwhelming 7.41 °C or 38.4% by 2070. City and policy makers are invited to implement the proposed straightforward and validated downscaling approach and to use the findings for a more responsive urban planning and design to the particular environemntal and geographic conditions; this isespecially required in cities and agglomorations dominated by complex- and heterogeneous-topographic characteristics.

Several Surface Energy Balance (SEB) models are currently used to unleash the boundless potential of Geographic Information System (GIS) and Remote Sensing (RS) techniques. Their main output, namely the actual evapotranspiration (ETa), is required for the assessment of water budget at basin, regional and national levels. Still, the lack of the required expertise coupled with sometimes missing input data limits their usage by researchers, policy makers, water managers and farmers. In this study, a novel comprehensive monthly adjusted crop coefficients (aKc) list was produced, in which these coefficients are usually used to retrieve the much-needed ETa using the climate-sensitive reference crop evapotranspiration (ET0). More particularly, the ETa is calculated for more than sixty different crop types present in the Mediterranean-climate California, United States. It is based on the two consecutive years of 2018 and 2019, which were wet and dry, respectively. The Google Earth Engine (GEE) version of the Surface Energy Balance Algorithm for Land-Improved (SEBALI), surnamed SEBALIGEE, with an Absolute Mean Error (AME) of 9.56 mm/month in the study area was used along the annual crop map from the United States Department of Agriculture (USDA). The main results showed that the average monthly aKc values ranged between 0.45 and 1.53. More specifically, October and November presented the lowest aKc values averaged among all crop types with aKc values around 0.76. On the other hand, March, April and June had the largest average aKc values at nearly 1.17. The monthly aKc values of the 60 crops studied in the plain of California, would potentially assist authorities and hydrologists to better calculate the actual monthly plant water consumption, in a simpler way over the upcoming years.

Dwellers experience a constant threat of wildfires when constructing their residences in woodland settings in or near forests. In these regions, also recognized as Wildland-Urban Interface (WUI), consequences of wildfires can be fatal to humans, animals and vegetation. The establishment of wildfire risk indexes is useful when produced on a community scale; these indexes proved their effectiveness worldwide to identify vulnerabilities to ignition and fire spread, to understand the underlying science and to simulate exposure. In this paper, four main inputs (i.e. wildfire likelihood, defensible space, building envelope, and community infrastructure) are used to produce the WUI Building Risk Index (WUIBRI), representing the wildfire likelihood at the property-level, in a typical WUI area, namely, Beit-Meri – Lebanon. Results show that one fourth of the total buildings have an undesirable WUIBRI value – greater than eight, demanding fast response and effective mitigation techniques. This index illustrates a weak positive spatial autocorrelation in the study area, which measures dependency among sub-regions, and no relationship with the buildings’ prices. A Cost-Benefit Analysis (CBA) is conducted and projected over the next five years from the date of the study. Under the conservative scenario, output reveals that implementing simple measures (i.e. pruning and trimming vegetation around the home, creating two-ways roads with two exits, establishing fire sprinklers system) reduces WUIBRI values; accordingly, decreasing the wildfire threat would require only 20% on the first year and 6.15% on the long run (for the next five years) of the total costs when only one wildfire occurs per year. It is recommended that homeowners, municipalities, and decision-makers, join forces to create a fire-adapted community. The approach outlined in this study is achievable in other Mediterranean regions to reduce wildfire suppression costs.

The increase in worldwide population is putting much pressure on the existent urban management plans. In this context, strategies and policies should be updated to conserve natural resources, but more importantly, to improve inhabitants' well-being. Studies focusing on pervious areas have many potentials, particularly regarding the assessment of potential green infrastructures within the vicinities of cities. This study highlights the pervious area change between 2001 and 2016 within every county in the CONtiguous United States (CONUS) based on the National Land Cover Database (NLCD). The study found that 43 different counties can be considered dense. A statistical analysis is followed, highlighting the air temperature, wind speed, precipitation, solar radiation, and Normalized Difference Vegetation Index (NDVI) trends in five characteristic counties. These datasets were retrieved from diverse remote sensing and satellite platforms between 1980 and 2019. The main results also found that air temperature was significantly (at α = 0.95) increasing for the same period. The other climatic variables depict fluctuating and usually insignificant trends. Such information would benefit decision- and policy-makers to focus their initiatives towards the most vulnerable counties while projecting different scenarios based on their current and historical conditions. Furthermore, this approach can be portable to other countries.

Wildfire is one of many natural hazards affecting the Mediterranean basin; its consequences could be fatal for individuals and beyond repair for the environment. While factors worldwide included in a fire ignition are unstandardized, in this paper, we built a model from literature-cited factors – fourteen elements were included – to highlight the probability of wildfires' occurrence in the Lebanese forest. It was named Three-Type Model (TTM), where forests were classified into three types: pine, oak and mixed. Validations have been conducted by using thirty percent of datasets versus the other seventy percent; then, by comparing its accuracy to another model that study the forest as one unit only. Accuracy assessment of the model reached above 83%, and it could be portable to other Mediterranean-climate forests. In addition, we produced a wildfire risk map by combining fire ignition-related factors with vulnerability-related variables. Results show that 15.9% of the Lebanese regions and 43.46% of the total amount of wildfires are human-induced wildfires. The majority of human-induced wildfires exists in a medium to high wildfire-ignition probabilities classes and in oak forests, representing approximately 93 and 83% of these wildfires, respectively. We concluded as well that only 1.6% of the Lebanese forest is at high risk of wildfire ignition. The implementation of our methodology in different Mediterranean countries is easy and straightforward, mainly because of the reduction of the ignition parameters as well as the usage of remote sensing datasets. It shall help decision-makers and official authorities in preventing, pre-suppressing and battling this phenomenon.

Lakes and reservoirs are vital resources for food security and economic prosperity. High evaporation rates can result in a greater water loss leading often to water shortage. Data scarcity prevents the estimation of such phenomenon in many lakes throughout the world. Recently, Google-Earth Engine (GEE) has been ho sting the widely acknowledged Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) system that enables the spatial and temporal estimations of the evaporation rates. Evaporation rates retrieved through METRIC-GEE system were compared to other lake variables between 2014 and 2018. A spatio-temporal assessment of the evaporation rates within the studied lake as well as a comparison to another regional lake (i.e. Kinneret) has been conducted. Results show that thermal stratification was already established in Lake Qaraoun in May. This strong persistent stratification (up to 13 °C) continued in June and July, then it started to weaken at the beginning of August. Evaporation correlated slightly better with thermal stratification than with surface temperature, with values of 0.71 and 0.67, respectively. The lowest monthly evaporation rates were found in December (30 mm) and the highest rates in August (above 200 mm). The highest spatial variations were found between May and August. Retrieved monthly evaporation rates were found comparable to those of the nearby studied Lake Kinneret and pan evaporation records from a close station. Such findings highlight on the relation between evaporation and understudied variables and the use of METRIC-GEE system to estimate evaporation rates in data scare regions.

Global wheat production reached 754.8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal&mdash, End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017. The proposed methodology is a tree-like approach relying on the Normalized Difference Vegetation Index (NDVI) values of four-month period that coincides with several phenological stages of wheat (i.e., tillering, stem extension, heading, flowering and ripening). The usage of the freely available Sentinel-2 imageries, with a high spatial (10 m) and temporal (5 days) resolutions, was necessary, particularly due to the small sized and overlapped plots encountered in the study area. Concerning the wheat areas, results show that there was a decrease from 11,063 &plusmn, 1309 ha in 2016 to 7605 &plusmn, 1184 in 2017. When SEWMA was applied using 2016 ground truth data, the overall accuracy reached 87.0% on 2017 data, whereas, when implemented using 2017 ground truth data, the overall accuracy was 82.6% on 2016 data. The novelty resides in executing early classification output (up to six weeks before harvest) as well as distinguishing wheat from other winter cereal crops with similar NDVI yearly profiles (i.e., barley and triticale). SEWMA offers a simple, yet effective and budget-saving approach providing early-season classification information, very crucial to decision support systems and the Lebanese government concerning, but not limited to, food production, trade, management and agricultural financial support.

Natural phenomena, such as wildfires, usually require the coincidence of several related factors in both time and space. In wildfire studies, literature-based factors were collected and listed in Mhawej et al. (2015). The question remains: which combination of factors leads to wildfires? In this context, a novel combination of wildfire likelihood factors was proposed in three different Lebanese forest covers (i.e., pine, oak, and mixed) and related literature-based factors to historical wildfire occurrences. The threshold values of each factor were deduced from the relationship between the element and number of fire occurrences. Each combination of factors was given a unique number. These mixtures corresponded to two, three, four or five factor groupings. The result was the association of each likelihood probability (i.e., low, medium, high, and very high) with different combinations of factors. Ultimately, using these combinations, the wildfire likelihood in Lebanese forests was efficiently and instantaneously generated. This approach could be portable to other Mediterranean regions and applied to several natural hazards.

The Middle East and North Africa (MENA) can be considered as the most water-scarce regions of the world and thus affecting the status of vegetation in this zone. Normalized Difference Vegetation Index (NDVI) datasets are used worldwide for large-area mapping and monitoring. Time series analysis techniques are used to define, evaluate, and monitor vegetation dynamics and variability using up-to-date remote sensing datasets. This study assesses vegetation degradation in the Arab countries using the SPOT Vegetation derived NDVI time series data of remotely sensed imageries for the time period between 1999 and 2012. Five classes were identified: Hot spot, negative change, no change, positive change and bright spot areas. Results indicate a severe decrease in the vegetation cover in almost 553 000 km2 of the total Arab region surface. On the other hand, only less than 1% of the region witnessed positive ‎changes in the vegetation cover. The positive and bright spot areas are mainly located in Algeria and Egypt, as well as in the southern part of Somalia and Iraq. Negative change and hot spot areas were found to be wide spreading all over the Arab countries, irrespective of the location, climate and topography. In addition, it is found that the vegetation changes in these regions are mainly related to human activities and decisions; climate change plays only a secondary role in the MENA region. La région du Moyen-Orient et de l’Afrique du Nord (MENA) peut être considérée comme la région la plus pauvre en eau du monde, affectant ainsi l’état de la végétation dans cette zone. Des données du Normalized Difference Vegetation Index (NDVI), qui s’étalent temporellement, sont utilisées dans le monde entier pour la cartographie et la surveillance de grande surface de végétation. Ils sont souvent à base de la télédétection. Cette étude évalue le changement de la végétation dans les pays arabes en utilisant le SPOT Végétation qui calcule le NDVI entre 1999 et 2012. Cinq catégories ont été identifiées: Hot spot ou changement négatif important, changement négatif, pas de changement, changement positif et Bright spot ou changement positif important. Les résultats indiquent une diminution importante du couvert végétal dans environ 553 000 km2 de la surface totale de la région arabe. D'autre part, seulement moins de 1% de la région a connu des changements positifs. Les zones ayant des changements de végétation positives sont principalement situées en Algérie et en Egypte, ainsi que dans la partie sud de la Somalie et l'Irak. Les changements négatifs ont été visibles presque partout dans MENA, indépendamment des pays, du climat et de la topographie. On constate que les changements de la végétation dans cette zone sont principalement liés aux activités humaines; le changement climatique ne joue qu'un rôle secondaire dans la région MENA.