In remote sensing (RS), shadows play an important role, commonly affecting the quality of data recorded by remote sensors. It is, therefore, of the utmost importance to detect and model the shadow effect in RS data as well as the information that is obtained from them, particularly when the data are to be used in further environmental studies. Shadows can generally be categorized into four types based on their sources: cloud shadows, topographic shadows, urban shadows, and a combination of these. The main objective of this study was to review the recent literature on the shadow effect in remote sensing. A systematic literature review was employed to evaluate studies published since 1975. Various studies demonstrated that shadows influence significantly the estimation of various properties by remote sensing. These properties include vegetation, impervious surfaces, water, snow, albedo, soil moisture, evapotranspiration, and land surface temperature. It should be noted that shadows also affect the outputs of remote sensing processes such as spectral indices, urban heat islands, and land use/cover maps. The effect of shadows on the extracted information is a function of the sensor–target–solar geometry, overpass time, and the spatial resolution of the satellite sensor imagery. Meanwhile, modeling the effect of shadow and applying appropriate strategies to reduce its impacts on various environmental and surface biophysical variables is associated with many challenges. However, some studies have made use of shadows and extracted valuable information from them. An overview of the proposed methods for identifying and removing the shadow effect is presented.

Recreational Ecological Parks (REPs) play a pivotal role in advancing sustainable development by promoting ecotourism, conserving biodiversity, and providing inclusive recreational opportunities with minimal environmental impact. Assessing the potential for REP development is crucial to optimize the sustainable use of natural resources and enhance ecological and socio-economic benefits. This study introduces a sustainability-oriented Spatial Multi-Criteria Decision Analysis (SMCDA) framework to evaluate and optimize REP development in Mazandaran Province, Iran. The framework incorporates 33 criteria across five categories: 13 for network connectivity and accessibility; 10 for facilities, services, and tourism potential; 4 for landform and land use; 3 for natural hazards; and 3 for climate conditions. Criteria were standardized using the minimum–maximum method and weighted based on expert input via the Best–Worst Method. A weighted linear combination approach was then applied to generate REP suitability maps. Existing recreational and tourism (R&T) sites were assessed against these maps, and a non-parametric bootstrapping method quantified uncertainties in suitability classifications. The results revealed that approximately 8%, 17%, 26%, 30%, and 19% of the study area fell into very low, low, moderate, high, and very high suitability classes, respectively. Among 151 R&T sites, 33% and 34% were situated in areas classified as very high and high suitability. Prediction rates were most accurate in the very high suitability category, while uncertainty analysis indicated a mean of 13% and a standard deviation of 1.7%, with uncertainties predominantly concentrated in lower suitability classes. The findings underscore the SMCDA framework’s efficacy in guiding sustainable REP development by identifying optimal sites and managing uncertainties. This study contributes to sustainability by integrating ecological, economic, and social dimensions into decision-making processes, thereby fostering resilience and long-term environmental stewardship in tourism planning.

Urban micro-climate plays an important role in human activities and in ensuring public health. For instance, the urban heat island effect is crucial to the thermal comfort of citizens and tourists, similar to the urban cool island effect’s importance on human and infrastructure resilience. Approximately 35% of global big cities are located in drylands. While existing research has focused on the spatial and temporal changes of surface urban cooling island intensity (SUCII) in drylands in the past, there is a gap in predicting the future spatiotemporal changes in SUCII for cities within these dryland regions. This study aims to forecast the spatiotemporal dynamics of daytime SUCII of representative growing cities with a dry and cold climate. Kerman and Zahedan cities, which are undergoing large urbanization and have harsh hot summer climates, were selected as the study area. Landsat 5 and 8 images and products were utilized for six timestamps within the timeframe of 1986–2023. Various methods, including a random forest algorithm, spectral indices, Cellular Automata-Markov (CA-Markov) model, the cross-tabulation model, and spatial overlay and zonal statistics, were employed to assess and model the spatiotemporal changes in SUCII. Initially, historical land cover maps, land surface temperature (LST), surface biophysical characteristics, and SUCII data were prepared, and their spatiotemporal changes were evaluated. Then, projected maps for these variables for the year 2045 were produced. The results indicated that the built-up areas, bare lands, and green spaces of Kerman (Zahedan) city in 1986 were 26.6 km² (17.6 km²), 103 km² (92.5 km²), and 44.4 km² (5.6 km²), respectively, and these values reached 99.3 km² (41.9 km²), 61.2 km² (70.7 km²), and 13.5 km² (3.2 km²) in 2023. The built-up lands area of Kerman (Zahedan) city is expected to increase by approximately 26% (36%) by 2045, while bare land and green space are expected to decrease by about 32% (20%) and 39% (31%), respectively. The greatest rise in average LST of Kerman (Zahedan) city is associated with the conversion of green spaces to barren land, resulting in a notable increase of 5.5 °C (4.3 °C) in 1986–2023. The conversion of barren land to built-up land in Kerman (Zahedan) city has led to a decrease of 4.6 °C (3.8 °C) in LST. The SUCII of Kerman (Zahedan) city for 1986, 1994, 2001, 2008, 2015, and 2023 were −0.3 °C (0.9 °C), −0.8 °C (0.4 °C), −1.4 °C (−0.5 °C), −1.9 °C (−1.5 °C), −2.6 °C (−2.5 °C), and −3.2 °C (−3.4 °C), respectively. The projected SUCII in Kerman (Zahedan) city for 2045 is about −4.3 °C (−4.5 °C), indicating an increasing trend in SUCII in the future. The area of zones without SUCII in Kerman (Zahedan) city decreased by 44.8 Km² (54.8 Km²) from 1986 to 2023, while the areas of low, medium, and high SUCII classes increased by 9.1 Km² (9.9 Km²), 10.9 Km² (11.9 Km²), and 24.8 Km² (33.1 Km²), respectively. The area of non-SUCII and high SUCII classes of Kerman (Zahedan) city in 2045 is expected to decrease by 31.5 Km² (12.0 Km²) and increase by 51.2 Km² (9.5 Km²) compared with 2023. The findings of this research indicate that the physical growth of cities in drylands can lead to the moderation of LST, contrary to mechanisms in humid and wet regions.

The ability to obtain accurate precipitation data from various geographic locations is crucial for many applications. Various global products have been released in recent decades for estimating precipitation spatially and temporally. Nevertheless, it is extremely important to provide reliable and accurate products for estimating precipitation in a variety of environments. This is due to the complexity of topographic, climatic, and other factors. This study proposes a multi-product information combination for improving precipitation data accuracy based on a generalized regression neural network model using global and local optimization strategies. Firstly, the accuracy of ten global precipitation products from four different categories (satellite-based, gauge-corrected satellites, gauge-based, and reanalysis) was assessed using monthly precipitation data collected from 1896 gauge stations in Iran during 2003–2021. Secondly, to enhance the accuracy of the modeled precipitation products, the importance score of effective and auxiliary variables—such as elevation, the Enhanced Vegetation Index (EVI), the Land Surface Temperature (LST), the Soil Water Index (SWI), and interpolated precipitation maps—was assessed. Finally, a generalized regression neural network (GRNN) model with global and local optimization strategies was used to combine precipitation information from several products and auxiliary characteristics to produce precipitation data with high accuracy. Global precipitation products scored higher than interpolated precipitation products and surface characteristics. Furthermore, the importance score of the interpolated precipitation products was considerably higher than that of the surface characteristics. SWI, elevation, EVI, and LST scored 53%, 20%, 15%, and 12%, respectively, in terms of importance. The lowest RMSE values were associated with IMERGFinal, TRMM3B43, PERSIANN-CDR, ERA5, and GSMaP-Gauge. For precipitation estimation, these products had Kling–Gupta efficiency (KGE) values of 0.89, 0.86, 0.77, 0.78, and 0.60, respectively. The proposed GRNN-based precipitation product with a global (local) strategy showed RMSE and KGE values of 9.6 (8.5 mm/mo) and 0.92 (0.94), respectively, indicating higher accuracy. Generally, the accuracy of global precipitation products varies depending on climatic conditions. It was found that the proposed GRNN-derived precipitation product is more efficient under different climatic conditions than global precipitation products. Moreover, the local optimization strategy based on climatic classes outperformed the global optimization strategy.

The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features’ capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.