Due to the harm forest fires cause to the environment and the economy as they occur more frequently around the world, early fire prediction and detection are necessary. To anticipate and discover forest fires, several technologies and techniques were put forth. To forecast the likelihood of forest fires and evaluate the risk of forest fire-induced damage, artificial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest fires are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest fires. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power.

In the semi-arid regions of northern Jordan, soil surface colors show gradual variation from west to east. The dominant soil color in the northwest is a dark reddish brown. Toward the east, lighter brown colors dominate, and colors change further to light yellow in the east. These changes coincide with a climatic gradient (mean annual precipitation). Earlier studies showed a close and possibly causal correlation of soil colors (redness), soil weathering intensity, and mean annual precipitation. However, these conclusions were based on a limited number of soil samples. Our study, in contrast, shows the regional variability of soil colors in the context of geomorphological conditions and the climatic gradient. Two thematic maps of soils surface colors depending on verified supervised classification of Landsat-8 and Sentinel-2 data were created. Results show a remarkable similarity of Support Vector Machines (SVM) classification of Landsat-8 and Sentinel-2 in the area, and confirm a strong correlation of red soil color distribution, mean annual precipitation, and geomorphological aspects (depressions leading to higher water availability and thus soil weathering intensity). Accordingly, this approach offers suitable tools for a quantitative investigation of soil color distribution under the consideration of climatic gradients and varying geomorphological conditions.

Soil fertility must be viewed as a dynamic concept that involves the constant cycling of nutrients between organic and inorganic forms. In this context, it refers also to supply adequate amounts of water and aeration for plant growth. Soil fertility under arid and semi-arid lands is constrained not only by limited water availability but also by small organic matter contents. Most fertility assessment systems are based on organic matter contents as the main parameter. However, crop experiments from various irrigated arid and semi-arid soils indicate that productivity is less- affected by organic matter contents than assumed. Therefore, we propose a new soil fertility system for dryland soils. It is a rule-based set of algorithms, mainly using additions and subtractions. Soil, climate, and landscape factors are integrated to calculate the numerical value of fertility for a given soil. We expect the system, which is focused on soil properties that keep or increase optimum soil moisture (such as texture), to be applicable in arid and semi-arid lands and to provide more realistic estimates of fertility regarding agricultural purposes. The manuscript will provide an outline of the main aspects of the system, illustrated by various case applications.

Although soil organic matter (SOM) forms a small portion of the soil body. Nevertheless, it is the most important component of the soil ecosystem, as well as of the carbon global cycle. In the semi-arid environment, there has been little research on the spatial distribution of SOM and soil organic carbon (SOC) stock. In this study, stratified random samples of total 30 soils were collected from two different soil depth (topsoil, subsoil) of Al Balikh plain and used for mapping the spatial variability of SOC and to estimating the SOC stock. The result showed that the values were relatively homogenate, with the normal decreasing trend with increasing the depth. The standard deviation (Std. D) for both SOC and SOC stock indicates homogeneous and absence of outliers values, whereas the coefficient of variation (C.V) indicates non-dispersion and clustering of values around the average. SOC was 0.38%, 0.17% in topsoil and subsoil respectively; the corresponding averages of SOC stock were 1.23 kg•m −2 and 1.14 kg•m −2 respectively, these values reflecting typical characteristics of poor SOC semi-arid soil. The correlation between SOC and SOC stock was (R 2 = 0.996, p < 0.001) in topsoil and it was (R 2 = 0.941, p < 0.001) for subsoil. The semivariograms were indicated that both SOC and SOC stock were best fitted to the exponential model. Nugget, range, and sill were equal to 0.002, 0.036, and 0.044, respectively for SOC in topsoil, and 0.014, 0.071, and 0.081, for SOC in the subsoil. For SOC stock, it was 0.0, 0.036, and 0.0508, respectively in topsoil. In the subsoil, the values were 0.1899, 0.086, and 4.159, respectively. SOC and SCO stock in both two layers are shown a strong spatial dependence, for which were 4.3, 17.2 for SOC in topsoil and subsoil respectively, and 0.0, 4.5 for SOC stock in topsoil and subsoil respectively, thus, which can be attributed to intrinsic factors.

Ground data on spectral characteristics of Jordan's soils remain sparse, which makes the interpretation of remote sensing datasets challenging. These are, however, very useful for predicting soil properties and agricultural suitability. Previous studies have shown that soil colours correlate well with degrees of weathering intensity, as indicated by magnetic parameters and dithionite‐extractable iron (Fed) contents along a climosequence in northern Jordan. This study enlarges the database by the results of 160 bulk samples that were collected systematically from the soil surface at 40 locations. In addition to soil colour and contents of Fed, we explore mean soil reflectance spectra (MSRS) measured by analytical spectral devices (ASD) and analyse the morphological conditions of the spectra referring to the effects of iron oxides on spectral behaviours. Results show a high correlation of spectral behaviours and colour variations with Fe oxides, and no correlation with soil organic carbon Corg. The influence of the Fe oxide contents is clearly apparent in the visible range (VIS). The presence of CaCO3 increases the reflectance in all spectral ranges. Six soil groups (Gr. I ‐ Gr. VI) were discerned qualitatively and quantitatively in the study area, which largely mirror the intensity of red colour expressed by redness indices. Highlights In northwestern Jordan there is an evident connection between the spectral properties, chemistry and soil colour. This study established a preliminary spectral library of soils in NW Jordan to facilitate the use of remote sensing in soil studies. The morphological properties and statistical analysis of the spectral data show that spectra of soils in NW Jordan are dominated by iron oxides. Spectral properties can be used to characterize the soil colours and types of Fe oxides in soils of Jordan.