Mangrove ecotourism is gaining immense popularity in the Gulf Cooperation Council (GCC) countries as a neoliberal conservation tool, and it has contributed significantly to the growth of the tourism sector in the region over the past two decades. However, there is no comprehensive review on the full extent of mangrove ecotourism activities and the contribution to mangrove conservation/restoration and economic growth in the region. A systematic literature review approach was used to examine the evolution of mangrove ecotourism in the GCC countries from 2010 to 2023. A total of 55 articles were retrieved from the Google and Google Scholar search engines, and the Scopus and Web of Science databases were incorporated. We synthesized the results and provided perspectives on the following: (1) the geographical and temporal distribution of studies in relation to mangrove extent, (2) key sites, attractions, and values for mangrove ecotourism activities, (3) the positive and negative impacts of mangrove ecotourism, and (4) existing mangrove conservation and restoration initiatives for the growth of mangrove ecotourism in the GCC countries. The findings underscore the significance of mangrove ecotourism in supporting economic development, protecting coastal ecosystems, and sustaining local livelihoods in the GCC countries. However, this study highlights the crucial need for sustainable coastal environmental management through integrated land use planning and zoning to address the negative impacts of anthropogenic pressures on mangrove ecosystems and ecotourism attractions. The use of remote sensing tools is invaluable in the monitoring of mangrove ecosystems and associated ecotourism impacts for informing evidence-based conservation and restoration management approaches. Thus, harnessing mangrove ecotourism opportunities can help the GCC countries with balancing economic growth, coastal environmental sustainability, and community well-being.

Runoff modelling is a crucial element in hydrologic sciences. However, a global runoff database is not currently available at a resolution higher than 0.1°. We use the recently developed Global Curve Number dataset (GCN250) to develop a dynamic runoff application (2015 – present) and that can be accessed via a Google Earth Engine application. We also provide a global mean monthly runoff dataset for April 2015-2021 in GeoTIFF format at a 250-meter resolution. We utilize soil moisture and GPM rainfall to dynamically retrieve the appropriate curve number and generate the corresponding runoff anywhere on Earth. Mean annual global runoff ratio results for 2021 were comparable to the runoff ratio from the Global Land Data Assimilation System (0.079 vs. 0.077, respectively). Mean annual global runoff from GCN and GLDAS were within 11% each other for 2020–2021 (0.18 vs. 0.16 mm/day, respectively). The GCN250 runoff application and the dataset are useful for many water applications such hydrologic design, land management, water resources management, and flood risk assessment.

With the foreseen increase in population and the reliance on water as a key input for agricultural production, greater demand will be placed on freshwater supplies. The objective of this work was to present the newly developed Android smartphone application to calculate crop evapotranspiration in real-time to support field-scale irrigation management. As part of the answer to water shortage, we embraced technology by developing AgSAT, a Google Earth Engine-based application that optimizes water use for food production. AgSAT uses meteorological data to calculate daily water requirements using the ASCE-Penman–Monteith method (ETref) and vegetation indices from satellite imagery to derive the basal crop growth coefficient, Kcb. The performance of AgSAT to estimate ETref was assessed using climatic data from 18 meteorological stations distributed over several climatic zones worldwide. ETref estimation through the app showed acceptable results with values of 1.27, 0.9, 0.79, 0.95, and 0.5 for root mean square error (RMSE), correlation coefficient (r), modeling efficiency (NSE), concordance index (d), and percentage bias (Pbias), respectively. AgSAT guides gross irrigation requirements for crops and rationalizes water quantities used in agricultural production. AgSAT has been released, is currently in use by research scientists, agricultural producers, and irrigation managers, and is freely accessible from the Google Play and IOS Store and also at agsat.app. Our work is geared towards the development of remote sensing-based technologies that transfer significant benefits to farmers and water-saving efforts.

Recent literature shows increasing interest in analyzing causes of what was referred to as “unusual” fires in Iraq and Syria in 2019. Here, we examine the causes of uncontrolled and irregular fires within farmlands in parts of the two countries in 2019–2020 and quantify their extent spatiotemporally using a combination of medium and coarse-resolution satellite imagery, land cover, precipitation, temperature data, and multiple sources of armed violence data. Our analysis reveals the extent of the deliberate arson of farmland in Iraq and Northeast Syria in 2019–2020. In comparison, only a few fires of farmland were observed in 2007–2013 in Iraq, and Al-Hassakeh, Syria, that became a stronghold of the Islamic State (IS) in 2013. In 2014–2017, we find evidence for increased farmland fires in various locations that closely tracked the military withdrawal of IS between 2015/2016 and 2017 when it lost most of its territory. We find that although the burned area in wheat-producing regions of Iraq (Ninewa and Kirkuk) and Syria have recently increased, the increase in the percentage burned area of agricultural lands in Iraq’s Ninewa and Kirkuk was not unprecedented. MODIS overestimates burned areas when in low fire activity while it underestimates it when fire activity is high, compared to a Landsat–Sentinel-2 combination. A significant positive relationship (r = 0.83) exists between the number of IS-related incidents and the percent burned agricultural area during 2019, which raises questions about the future strategy of the IS terror group and its use and targeting of the water-food complex.

Quantifying Evapotranspiration (ET), a major component of the hydrologic cycle, is critical for monitoring agricultural water use at the field scale and enabling better water management. Despite the importance of a global field-scale ET product, there is limited information available on the inter- and intra-annual variability in recent years. Here, we generate a monthly 100-m resolution ET dataset from 1990 to 2021, using a validated single-source energy balance algorithm and more than four million thermal Landsat satellite imagery scenes. We find that global ET has significantly accelerated over the last two decades at an annual rate of 1.33 mm yr^− 1 (0.2%), despite regional disparities. This rate has intensified to 0.47% and 1.97–2.15% in the most recent twelve and seven years of the study, respectively, primarily due to increases in summer ET over North America, African tropics, and Indochina. Our machine learning analysis indicates that air temperature, rainfall, and atmospheric moisture are the primary drivers of these ET trends. This publicly available ET datasets has the potential to advance our understanding of global and local water use dynamics and can inform water policy and management decisions in a warming climate.