Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVI_MccL and EVI2_MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.

Precise knowledge of crop water consumption is essential to better manage agricultural water use, particularly in regions where most countries struggle with increasing water and food insecurity. Approaches such as cloud computing and remote sensing (RS) have facilitated access, process, and visualization of big geospatial data to map and monitor crop water requirements. To find the most reliable Vegetation Index (VI)-based evapotranspiration (ETa) for croplands in drylands, we modeled and mapped ETa using empirical RS methods across the Zayandehrud river basin in Iran for two decades (2000–2019) on the Google Earth Engine platform using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index 2 (EVI2). Developed ET-VI products in this study comprise three NDVI-based ETa (ET-NDVI*, ET-NDVI*_scaled, and ET-NDVI_Kc) and an EVI2-based ETa (ET-EVI2). We (a) applied, for the first time, the ET-NDVI* method to croplands as a crop-independent index and then compared its performance with the ET-EVI2 and crop ET, and (b) assessed the ease and feasibility of the transferability of these methods to other regions. Comparing four ET-VI products showed that annual ET-EVI2 and ET-NDVI*_scaled estimations were close. ET-NDVI_Kc consistently overestimated ETa. Our findings indicate that ET-EVI2 and ET-NDVI_Kc were easy to parametrize and adopt to other regions, while ET-NDVI* and ET-NDVI*_scaled are site-dependent and sensitive to image acquisition time. ET-EVI2 performed robustly in arid and semi-arid regions making it a better tool. Future research should further develop and confirm these findings by characterizing the accuracy of VI-based ETa over croplands in drylands by comparing them with available ETa products and examining their performance using crop-specific comparisons.

Urban greenery and cooling initiatives have become top priorities for municipalities worldwide as they contribute to improved environmental quality and urban resilience. This study leverages advancements in remote sensing (RS) and cloud-based processing to assess and monitor changes in public urban green spaces (PUGS) in Sanandaj, Iran. Using high-resolution Sentinel-2 imagery (10 m) processed in Google Earth Engine (GEE), we calculated and mapped the normalized difference vegetation index (NDVI) across 20 major PUGSs over a five-year period, from 2019 to 2023. A total of 507 Sentinel-2 images were analyzed, offering a comprehensive view of seasonal and annual greenness trends. Our findings reveal that May is the peak month for greenery, while February consistently shows the lowest NDVI values, indicating seasonal greenness variability. Specifically, the mean NDVI of PUGSs decreased significantly between 2019 and 2022, with values recorded at 0.735, 0.737, 0.622, 0.417, and 0.570 in the greenest month of each respective year, highlighting a noticeable decline in vegetation health and extent. This reduction can be attributed to water scarcity and suboptimal management practices, as evidenced by dried or underperforming green spaces in recent years. Our results underscore the potential of integrating NDVI-based assessments within urban development frameworks to more accurately define and sustain PUGSs in Sanandaj. This methodology provides a replicable approach for cities aiming to optimize urban greenery management through RS technology.

Climate change and drought have imposed tremendous pressure on water resources in most parts of the world. Therefore, addressing drought impacts on water resources and socioeconomic conditions has gained attention to develop mitigation and adaptation strategies. Many innovative and cutting-edge modeling systems set up by academic institutions are exclusively available to academics and are not accessible or used by practitioners or policymakers. These constraints are in particular relevant in data poor regions where decision making and drought management are hampered by a lack of drought information. Therefore, within OUTLAST project (Development of an Operational, mUlTi-sectoral globaL drought hAzard forecasting SysTem), we aim to develop and implement the first worldwide, multi-sectoral, and operational drought forecasting system for quantifying drought hazard in water supply, riverine and non-agricultural land ecosystems, rainfed and irrigated agriculture. The forecasts provided by OUTLAST can also support better drought management and therefore contribute to the achievement of several Sustainable Development Goals (SDGs) of the United Nations, particularly SDG1(no poverty), SDG2 (zero hunger), SDG6 (clean water and sanitation), SDG13 (climate action) and SDG15 (life on land). In cooperation with pilot users in the project regions of Lake Victoria (Burundi, Kenya, Rwanda, Tanzania, and Uganda) and West and Central Asia (e.g. Afghanistan, Armenia, Azerbaijan, Iran, Iraq, Lebanon, Oman, Syria, Tajikistan, Turkey, Uzbekistan), the value of global-scale forecasts released for the subsequent six months for data-poor and transboundary basins will be tested. Through co-design, relevant drought indicators will be defined and the web portal and pilot applications of these global forecasts for drought management and water governance will be developed. We also systematically investigate the predictive ability of global-scale drought forecasts based on bias-corrected seasonal ensemble weather forecasts, as well as the factors influencing the predictive skill in terms of (i) the type of drought (soil moisture and hydrological droughts) and the affected sector, (ii) the length of the forecast period, (iii) seasonal and regional differences in predictive quality. Two global-scale models (WaterGAP and GCWM) will be further developed to provide operational and monthly drought forecasts globally at 0.5-degree spatial resolution and for a six-month forecast period. The applicability of the data and models for drought forecasting will be rigorously examined for different locations, sectors, and periods using historical reanalysis data and historical ensemble forecasts. This will entail using historical meteorological "forecasts" to generate drought forecasts for historical drought events reported for these regions and validating them regionally. Finally, an automated operational modeling system will be created to download and process the necessary meteorological input data, calculate the drought indicators, visualize them appropriately, and transfer them to the Global Hydrological Status and Outlook System (HydroSOS) at World Meteorological Organization (WMO). Through a flexible implementation (cloud-based), we will enable a potential transfer of the forecast system to different users. The use of the HydroSOS-portal of WMO to visualize project results and as an outlet of the drought forecast products will make optimal use of synergies and ensure high visibility and impact of the research performed in OUTLAST.

Urban greenery and cooling initiatives have become top priorities for municipalities worldwide, as they contribute to improved environmental quality and urban resilience. This study leverages advancements in remote sensing (RS) and cloud-based processing to assess and monitor changes in public urban green spaces (PUGS) in Sanandaj, Iran. Using high-resolution Sentinel-2 imagery (10 m) processed in Google Earth Engine (GEE), we calculated and mapped the normalized difference vegetation index (NDVI) across 20 major PUGSs over a five-year period from 2019 to 2023. A total of 507 Sentinel-2 images were analyzed, offering a comprehensive view of seasonal and annual greenness trends. Our findings reveal that May is the peak month for greenery, while February consistently shows the lowest NDVI values, indicating seasonal greenness variability. Specifically, the mean NDVI of PUGSs decreased significantly between 2019 and 2022, with values recorded at 0.735, 0.737, 0.622, 0.417, and 0.570 in the greenest month of each respective year, highlighting a noticeable decline in vegetation health and extent. This reduction can be attributed to water scarcity and suboptimal management practices, as evidenced by dried or underperforming green spaces in recent years. Our results underscore the potential of integrating NDVI-based assessments within urban development frameworks to more accurately define and sustain PUGSs in Sanandaj. This methodology provides a replicable approach for cities aiming to optimize urban greenery management through RS technology.