Kamal Nabiollahi

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.

Land suitability assessment is an important process in modern agricultural management, involving the evaluation of various factors such as soil properties, climate, relief, hydrology, crop varieties and socio-economic considerations. Various methods have been used to assess land suitability, such as the parametric method developed by Sys et al. (1991) and the FAO (1976) approach to land evaluation. Determining the relative weighting of the factors that influence land suitability is a particularly challenging step in the evaluation process. An alternative to these procedures is the use of multi-criteria decision making (MCDM) techniques, which enable land managers and policy makers to make informed decisions about land use and development. Spatial MCDM techniques include complex spatial data and methods such as the Technique of Preference Ordering by Similarity to the Ideal Solution (TOPSIS), which are widely used in the agricultural sector. TOPSIS determines the optimal alternative according to the principle of minimizing the proximity to the ideal solution and maximizing the distance to the negative ideal solution. The aim of this study was to assess the suitability of land for wheat cultivation in western Iran, a country facing the challenge of becoming self-sufficient in wheat. Seventy soil profiles were selected and described on the basis of a geomorphologic map and the content of various soil properties and wheat yield were determined. MCDM (TOPSIS) and FAO models were applied and evaluated according to wheat yield. The Shannon entropy method (SHE) was used to extract the criteria weights. Land suitability assessment was mapped using a Random Forest machine learning model and auxiliary variables. According to the results of the Shannon entropy method, slope, cation exchange capacity (CEC) and calcium carbonate equivalent (CCE) are the most important criteria for wheat cultivation. Furthermore, the results are also confirmed by the spatial autocorrelation between the key criteria and wheat yield. These results also show that the soil suitability values calculated with the TOPSIS model have a higher correlation with wheat yield than the values calculated with the FAO model (0.73 and 0.67, respectively). The spatial distribution of the suitability values for wheat cultivation showed that 30 to 33% of the areas were very suitable, 13-16% moderately suitable and 51% and 57% unsuitable. For the areas with high and medium suitability, the TOPSIS and FAO results were largely in agreement, in contrast to the areas with low suitability. This study provided a comprehensive approach to land suitability for wheat cultivation using advanced MCDM techniques and machine learning, which can be beneficial for sustainable land management and food security in Iran and similar regions.

Land suitability assessment, as an important process in modern agriculture, involves the evaluation of numerous aspects such as soil properties, climate, relief, hydrology and socio-economic aspects. The aim of this study was to evaluate the suitability of soils for wheat cultivation in the Gavshan region, Iran, as the country is facing the task of becoming self-sufficient in wheat. Various methods were used to evaluate the land, such as multi-criteria decision-making (MCDM), which is proving to be important for land use planning. MCDM and machine learning (ML) are useful for decision-making processes because they use complicated spatial data and methods that are widely available. Using a geomorphological map, seventy soil profiles were selected and described, and ten soil properties and wheat yields were determined. Three MCDM approaches, including the technique of preference ordering by similarity to the ideal solution (TOPSIS), gray relational analysis (GRA), and simple additive weighting (SAW), were used and evaluated. The criteria weights were extracted using Shannon’s entropy method. Random forest (RF) model and auxiliary variables (remote sensing data, terrain data, and geomorphological maps) were used to represent the land suitability values. Spatial autocorrelation analysis as a statistical method was applied to analyze the spatial variability of the spatial data. Slope, CEC (cation exchange capacity), and OC (organic carbon) were the most important factors for wheat cultivation. The spatial autocorrelation between the key criteria (slope, CEC, and OC) and wheat yield confirmed these results. These results also showed a significant correlation between the land suitability values of TOPSIS, GRA, and SAW and wheat yield (0.74, 0.72, and 0.57, respectively). The spatial distribution of land suitability values showed that the areas classified as good according to TOPSIS and GRA were larger than those classified as moderate and weak according to the SAW approach. These results were also confirmed by the autocorrelation of the MCDM techniques with wheat yield. In addition, the RF model showed its effectiveness in processing complex spatial data and improved the accuracy of land suitability assessment. In this study, by integrating advanced MCDM techniques and ML, an applicable land evaluation approach for wheat cultivation was proposed, which can improve the accuracy of land suitability and be useful for considering sustainability principles in land management.

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.

Spatial information on land and soil resources are critical towards addressing land degradation for ensuring sustainable soil and crop management. To address these needs, digital soil mapping techniques have emerged as an efficient and low-cost solution. Although digital soil mapping has typically leveraged geospatial environmental variables (e.g., remote sensing), the application and integration of spectroscopic data with those environmental variables remain limited. Hence, this study combines visible and near-infrared (Vis-NIR) spectroscopy, remote sensing, and topographic data and applies random forests, hybridized with particle swarm optimization algorithm (RF + PSO), to predict the spatial variability of soil clay content, electrical conductivity (EC), and calcium carbonate equivalent (CCE) for 370 km² of agricultural land in western Iran. Using a conditioned Latin hypercube approach, 220 soil samples at the 0–20 cm depth increment were acquired throughout the study area. Three sets of environmental covariates were tested: Scenario A (Vis-NIR spectroscopy data), Scenario B (environmental data), and Scenario C (Vis-NIR spectroscopy + environmental data). According to the 10-fold cross-validation procedure with 100 replications, the RF + PSO model showed an acceptable level accuracy for all scenarios, although the accuracy of the RF + PSO model using the Scenario C data was higher than all other scenarios: the Lin's Concordance Correlation Coefficient values were 0.77, 0.83, and 0.74 for the clay contents, EC, and CCE, respectively. The results demonstrated that the combination of Vis-NIR spectroscopic data and commonly available environmental covariates provided the best input data for the hybridized model and enhanced its performance.

Land use change and soil organic carbon stock (SOCS) depletion over time is one of the predominant worldwide environmental problems related to global warming and the need to secure food production for an increasing world population. In our research, satellite images from 1988 and 2018 were analyzed for a 177.48 km² region in Kurdistan Province, Iran. Across the study area. 186 disturbed and undisturbed soil samples were collected at two depths (0–20 cm and 20–50 cm). Bulk density (BD), soil organic carbon (SOC), rock fragments (RockF) and SOCS were measured. Random forest was used to model the spatial variability of SOCS. Land use was mapped with supervised classification and maximum likelihood approaches. The Kappa index and overall accuracy of the supervised classification and maximum likelihood land use maps varied between 83% and 88% and 78% and 85%, respectively. The area of forest and high-quality rangeland covered 5286 ha in 1988 and decreased by almost 30% by 2018. Most of the decrease was due to the establishment of cropland and orchards, and due to overgrazing of high-quality rangeland. As expected, the results of the analysis of variance showed that mean values of SOCS for the high-quality rangeland and forest were significantly higher compared to other land use classes. Thus, transformation of land with natural vegetation like forest and high-quality rangeland led to a loss of 15,494 Mg C in the topsoil, 15,475 Mg C in the subsoil and 15,489 Mg C⁻¹ in total. We concluded that the predominant causes of natural vegetation degradation in the study area were mostly due to the increasing need for food, anthropogenic activities such as cultivation and over grazing, lack of government landuse legislation and the results of this study are useful for land use monitoring, decision making, natural vegetation planning and other areas of research and development in Kurdistan province.

Soil quality assessment based on crop yields and identification of key indicators of it can be used for better management of agricultural production. In the current research, the weighted additive soil quality index (SQIw), factor analysis (FA), and multiple linear regression (MLR) are used to assess the soil quality of rainfed winter wheat fields with two soil orders on 53.20 km² of agricultural land in western Iran. A total of 18 soil quality indicators were determined for 100 soil samples (0–20 cm depth) from two soil orders (Inceptisols and Entisols). The soil properties measured were: pH, soil texture, organic carbon (OC), cation exchange capacity (CEC), electrical conductivity (EC), soil microbial respiration (SMR), carbonate calcium equivalent (CCE), soil porosity (SP), bulk density (BD), exchangeable sodium percentage (ESP), mean weight diameter (MWD), available potassium (AK), total nitrogen (TN), available phosphorus (AP), available Fe (AFe), available Zn (AZn), available Mn (AMn), and available Cu (ACu). Wheat grain yield for all of the 100 sampling sites was also gathered. The SQIw was calculated using two weighting methods (FA and MLR) and maps were created using a digital soil mapping framework. The soil indicators determined for the minimum data set (MDS) were AK, clay, CEC, AP, SMR, and sand. The correlation between the MLR weighting technique (SQIw-M) and the rainfed wheat yield (r = 0.62) was slightly larger than that the correlation of yield with the FA weighted technique (SQIw-F) (r = 0.58). Results showed that the means of both SQIw-M and SQIw-F and rainfed wheat yield for Inceptisols were higher than for Entisols, although these differences were not statistically significant. Both SQIw-M and SQIw-F showed that areas with Entisols had lower proportions of good soil quality grades (Grades I and II), and higher proportions of poor soil quality grades (Grades IV and V) compared to Inceptisols. Based on these results, soil type must be considered for soil quality assessment in future studies to maintain and enhance soil quality and sustainable production. The overall soil quality of the study region was of poor and moderate grades. To improve soil quality, it is therefore recommended that effective practices such as the implementation of scientifically integrated nutrient management involving the combined use of organic and inorganic fertilizers in rainfed wheat fields should be promoted.

Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) based on the Food and Agriculture Organization (FAO) “land suitability assessment framework” for 65 km² of agricultural land in Kurdistan province, Iran. Soil samples were collected from genetic layers of 100 soil profiles and the physical-chemical properties of the soil samples were analyzed. Topography and climate data were also recorded. After calculating the land suitability classes for the two crops, they were mapped using machine learning (ML) and traditional approaches. The maps predicted by the two approaches revealed notable differences. For example, in the case of rain-fed wheat, results showed the higher accuracy of ML-based land suitability maps compared to the maps obtained by traditional approach. Furthermore, the findings indicated that the areas with classes of N2 (≈18%↑) and S3 (≈28%↑) were higher and area with the class N1 (≈24%↓) was less predicted in the traditional approach compared to the ML-based approach. The major limitations of the study area were rainfall at the flowering stage, severe slopes, shallow soil depth, high pH, and large gravel content. Therefore, to increase production and create a sustainable agricultural system, land improvement operations are suggested.

Identifying changes in soil organic carbon stocks (SOCS) is essential for determining appropriate ways to deal with land degradation, for understanding soil and crop management and for gathering useful information for a range of environmental studies. The aim of this study was to predict SOCS and compare under current and potential future land uses. Soil organic carbon (SOC) and bulk density were measured at 137 locations across the Marivan, Kurdistan Province, Iran, and soil SOCS was computed. Auxiliary data including, terrain attributes and Landsat 8 ETM⁺ data were acquired. Random forest (RF) models were used to relate the SOCS to the auxiliary data. Results suggested that the mean SOCS in the topsoil and subsurface in croplands were lower than in forestland and wetland, although not significantly so. In the area, approximately 18.48% of forestland and 17.39% of wetland has been brought into cultivation. The authors estimate that this has led to a loss of SOCS from forestland topsoil of 22,860 Mg C, and from subsurface of 15,685 Mg C. The SOCS loss from wetland topsoil and subsurface were not as great, at 4193 and 2680 Mg C, respectively, but this was due to the area not being as large.