Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from the CoSpectroCam sensor (CSC, licensed to AgriWatch BV, Enschede, The Netherlands) mounted on an unmanned aerial vehicle (UAV) in Iran. The model included nine bands from Landsat-8/9, 11 bands from Sentinel-2, and 1252 bands from the CSC (covering the wavelength range between 420 and 850 nm). The relative feature importance and band sensitivity to SM variations were analyzed. In addition, four indices, including the perpendicular index (PI), ratio index (RI), difference index (DI), and normalized difference index (NDI) were calculated from the different bands of the datasets, and their sensitivity to SM was evaluated. The results showed that the PI exhibited the highest sensitivity to SM changes in all datasets among the four indices considered. Comparisons of the performance of the datasets in SM estimation emphasized the superior performance of the UAV hyperspectral data (R² = 0.87), while the Sentinel-2 and Landsat-8/9 data showed lower accuracy (R² = 0.49 and 0.66, respectively). The robust performance of the CSC data is likely due to its superior spatial and spectral resolution as well as the application of preprocessing techniques such as noise reduction and smoothing filters. The lower accuracy of the multispectral data from Sentinel-2 and Landsat-8/9 can also be attributed to their relatively coarse spatial resolution compared to the CSC, which leads to pixel non-uniformities and impurities. Therefore, employing the CSC on a UAV proves to be a valuable technology, providing an effective link between satellite observations and ground measurements.

Accurate estimation of soil moisture content (SMC) is crucial for effective water management, enabling improved monitoring of water stress and a deeper understanding of hydrological processes. While satellite remote sensing provides broad coverage, its spatial resolution often limits its ability to capture small-scale variations in SMC, especially in landscapes with diverse land-cover types. Unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors offer a promising solution to overcome this limitation. This study compares the effectiveness of Sentinel-2, Landsat-8/9 multispectral data and UAV hyperspectral data (from 339.6 nm to 1028.8 nm with spectral bands) in estimating SMC in a research farm consisting of bare soil, cropland and grassland. A DJI Matrice 100 UAV equipped with a hyperspectral spectrometer collected data on 14 field campaigns, synchronized with satellite overflights. Five machine-learning algorithms including extreme learning machines (ELMs), Gaussian process regression (GPR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) were used to estimate SMC, focusing on the influence of land cover on the accuracy of SMC estimation. The findings indicated that GPR outperformed the other models when using Landsat-8/9 and hyperspectral photography data, demonstrating a tight correlation with the observed SMC (R² = 0.64 and 0.89, respectively). For Sentinel-2 data, ELM showed the highest correlation, with an R² value of 0.46. In addition, a comparative analysis showed that the UAV hyperspectral data outperformed both satellite sources due to better spatial and spectral resolution. In addition, the Landsat-8/9 data outperformed the Sentinel-2 data in terms of SMC estimation accuracy. For the different land-cover types, all types of remote-sensing data showed the highest accuracy for bare soil compared to cropland and grassland. This research highlights the potential of integrating UAV-based spectroscopy and machine-learning techniques as complementary tools to satellite platforms for precise SMC monitoring. The findings contribute to the further development of remote-sensing methods and improve the understanding of SMC dynamics in heterogeneous landscapes, with significant implications for precision agriculture. By enhancing the SMC estimation accuracy at high spatial resolution, this approach can optimize irrigation practices, improve cropping strategies and contribute to sustainable agricultural practices, ultimately enabling better decision-making for farmers and land managers. However, its broader applicability depends on factors such as scalability and performance under different conditions.

A sub-irrigated planter (SIP) is a container irrigation technique in which water is supplied to the crop from the bottom, stored in a saturated media-filled reservoir beneath an unsaturated soil, and then delivered by capillary action to the root zone. The aim of this study was to optimize the water management and to assess the performance of this technique in terms of water use efficiency, soil moisture, and solute distribution in comparison with surface irrigation in a Mediterranean greenhouse. The experiment consisted of four SIP treatments, with a constant water level in the bottom reservoir in order to evaluate the effect of two different irrigation salinities (1.2 and 2.2 dS m⁻¹) and two depths of substrate profiles (25 and 15 cm). The results showed that SIP is capable of significantly improving both water-use efficiency and plant productivity compared with surface irrigation. Also, a 24% average reduction in water consumption was observed while using SIP. Moreover, SIPs with a higher depth were recommended as the optimum treatments within SIPs. The type of irrigation method affected the salinity distribution in the substrate profile; the highest salinity levels were registered at the top layers in SIPs, whereas the maximum salinity levels for the surface treatments were observed at the bottom layers. SIPs provide a practical solution for the irrigation of plants in areas facing water quality and scarcity problems.

Evapotranspiration is an essential component of the hydraulic cycle and is a crucial topic for water resource management. This study focuses on modeling daily reference crop evapotranspiration using an artificial neural network and a genetic algorithm in three stations: Ahvaz (dry climate), Saghez (semi-humid and dry climate), and Sardasht (humid climate). The study uses daily meteorological data from 2011 to 2020, including temperature, humidity, sunshine hours, and wind speed. The PMO-56 method is chosen as the modeling objective. The modeling process is investigated under various data scarcity conditions to determine the superior model in each scenario. The comparison of results between different models is based on Root Mean Squared Error (RMSE) and correlation coefficient (R). The results show that the best outcomes are achieved using four input parameters and the whale optimization algorithm approach. Also, ANN-WOA31, ANN-WOA21, and ANN-WOA₁₁ models had the highest estimates in the Ahvaz station in different input conditions. ANN-WOA31, ANN-WOA24, and ANN-WOA11 models were the best in the Saghez station, and ANN-WOA33, ANN-WOA21, and ANN-WOA11 models performed best in Sardasht station with different input combinations. Furthermore, it is observed that with only the temperature parameter and using either an artificial neural network, genetic algorithm, or whale optimization algorithm method, good results can be obtained in all three stations. Following this, humidity and solar radiation can significantly influence the results, while wind speed alone does not substantially impact. Additionally, in all cases, the whale optimization algorithm consistently outperforms the other models in producing better results.