The rapid growth of urban areas is a major challenge facing cities around the world. This growth can have a significant impact on the local climate, leading to higher temperatures and other changes. In desert climates, the effects of urban expansion can be particularly pronounced. This study investigated the impact of urban expansion on land surface temperature (LST) in Baghdad, Iraq. Notably, this study employs a sophisticated artificial intelligence method known as Random Forest for Land Use Land Cover (LULC) classification, utilizing three Landsat images spanning the temporal spectrum from 1985 to 2021 to meticulously monitor land use transformations and associated LST variations. The results showed that vegetated areas declined by 46.8% during the study period, while built-up areas increased by 124.7%. This decline in vegetation was accompanied by an increase in LST, with bare soil recording the highest temperatures. The study also found that LST has a strong inverse relationship with vegetation and moisture. This means that areas with more vegetation and moisture tend to have lower LSTs. These findings suggest that urban expansion can lead to higher LSTs in desert climates, which can have implications for the health and wellbeing of residents. The study has important implications for urban planners and policymakers in Baghdad and other cities in desert climates. By identifying the main factors that control LST, the study provides insights into strategies for mitigating the effects of urban expansion on temperature.

Land Surface Temperature (LST) and Land Use Land Cover (LULC) are the principal aspects of climate and environment studies. The object of the study is to assess spatial relationship between LST and remote sensing LULC indices at the global and continental scale. Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua daytime LST and eight LULC MODIS indices of 2018 prepared and processed using Earth Engine Code Editor. R squared and significance of the relationship values of randomly selected points computed in R program. The research observed the relationship between examined indices and LST is significant at the 0.001 level. Normalized Difference Water Index (NDWI) and Normalized Difference Snow Index (DSI) are the dominant drivers of LST in the world, Asia and North America. In Australia and Africa, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are the dominant drivers of LST. Albedo and Normalized Difference Soil Index (NDSI) have superior in Central America. In South America and Europe, the dominant driver of LST is NDWI. Relationship between albedo and LST is moderate inverse on a global scale. Observed relationship between LST and examined vegetation indices is positive in Europe and North America while inverse in Australia and Africa. All observed relationship between Normalized Difference Built-up Index (NDBI) and LST are positive. Association observed between NDSI and LST is positive in Australia, Africa and Central America.

One of the most destructive natural disasters is the earthquake which brings enormous risks to humankind. The objective of the current study was to determine the Earthquake’s remote sensing multiparameter (i.e. land surface temperature (LST), air temperature, specific humidity, precipitation and wind speed) spatiotemporal anomaly of many earthquake samples occurred during 2018 around the world. In this research 11 earthquake (M > 6:0) studied (4 samples selected in a land with transparent sky situations, 3 samples in land within cloudy situations and 4 samples in marine earthquakes). The interquartile range (IQR) and mean ± 2σ methods utilized to improve the efficiency of anomalous differences. As a result, based on the IQR method, negative anomaly before the event detected during the daytime in Mexico and during the nighttime in Afghanistan. In addition, a negative outlier of brightness temperature (BT) detected in Alaska before, after and during the event. In contrast, based on IQR and mean ± 2σ positive anomaly detected in precipitation before and after the event in all investigated examples. According to mean ± 2σ, negative anomaly LST, specific humidity, sea surface temperature (SST_100) and wind detected in most examined earthquake samples. In contrast, positive SST_0 anomaly observed in Fiji and Honduras after the earthquake. Our results suggested in marine earthquakes, for earthquake forecasting we can merge a prior negative anomaly in the wind speed and SST_100. Regarding the in land cloudy sky earthquakes, merging anomaly parameters could be the negative prior anomaly in BT, skin temperature, in contrast, a positive anomaly in precipitation. In land transparent sky earthquake, usually negative prior anomalies in air temperature, specific humidity and LST.

Remote sensing data and techniques utilized for various purposes including natural disasters such as earthquake as well as flood. The research aims to consume liberates Landsat 8 images for investigating crashed airplanes such as MH370. Overall approximately 300 Landsat images with less than 10% clouds utilized in addition processed through Google Engine Platform. Due to the materials as well as the color of airplane body different from the area which is a plane crashed there, moreover, it should be the characteristics of the plane shapefile different in terms of albedo, temperature as well as vegetation index value. The research observed Landsat 8 data as well as methods utilized in this research, especially, NDVI, albedo in addition to band 4, capable to distinguish between the plane and its surrounding green area. Therefore, our result confirms during the research period, there was no plane on the location as well as MH370 not crashed in this site.

Droughts are a common occurrence in various climates and are primarily caused by a prolonged decrease in rainfall. Several factors contribute to droughts, including temperature, wind speed, relative humidity, rainfall timing, amount, and intensity during the growing season. The objective of this study is to establish a new index, named soil moisture and evapotranspiration revealed drought index (SERDI), for displaying dry and wet conditions based on combining both soil moisture and evapotranspiration (Penman-Monteith) to improve drought early warning and its severity globally. For validation of the SERDI with other indices such as LST, VHI, NDVI, and NDWI, different ways used such as R-square, RMSE, MAPE, and P-value to estimate the accuracy, variability of data, the forecast conditions, and how the significance of data. The results showed that the low RMSE and high r² were found between SERDI with LST and VHI. In contrast, the low R- square and high RMSE were between SERDI with NDVI and NDWI in most of the semi-arid areas. Furthermore, most of the semi-arid areas from Iran, Iraq, Syria, Jordan, and Israel experienced moderate and severe dry conditions, except some parts of these regions had normal conditions in Iran and Syria. The SERDI analysis revealed a strong correlation between (LST) and a moderate correlation with (VHI) across all study areas. However, the relationship between other indices, like (NDWI) and (NDVI), varied depending on the regions. To conclude, SERDI can be used globally for detecting drought based on soil moisture and evapotranspiration.

Wheat and barley are among the primary food resources of the world population; therefore, their growth and observation are essential in farms to enhance food security worldwide. On top of that, careful observation of the product is essential to find solutions for the issues faced during their production and to reduce the impacts of weather changes. With the advancement of Remote Sensing technology, the observation and estimation process has increased. In this study, numbers of spectral vegetation indices was used along with canopy biophysical properties ( LAI ) and biochemical properties (chlorophyll), there calculated from (Landsat 8 and Sentinel-2) satellite data. The wheat and barley samples were collected before were be ready for harvest, and a relation with the vegetarian indices was established using the Multi-Linear Regression module, in which the equations used in predicting the harvest were developed and used to create a graph for expected harvest. The result indicated that there is a strong relationship between the vegetation indices of Sentinel-2 and Landsat images and the actual grain yield with R² of 0.77 and 0.71, respectively. The results show that the strongest correlation is observed between the LAI data obtained from Sentinel data and cereal yield data, with an R² 0.68, and the highest correlation for the indices of Landsat images is observed in the NDWI with R² 0.59 and the lowest degree of error was in the root mean square error (RMSE) for the Sentinel-2 and Landsat 8 with 0.57 and 1.54. In addition, this study also showed that the least relationship for grain yield prediction was observed between the NDRI for Sentinel-2 (R² 0.1) and SAVI for Landsat image (R² 0.47).

Observing cultivated crops and other forms of land use is an important environmental and economic concern for agricultural land management and crop classification. Crop categorization offers significant crop management data, ensuring food security, and developing agricultural policies. Remote sensing data, especially publicly available Sentinel 1 and 2 data, has effectively been used in crop mapping and classification in cloudy places because of their high spatial and temporal resolution. This study aimed to improve crop type classification by combining Sentinel-1 (Synthetic Aperture Rader (SAR)) data and the Sentinel-2 Multispectral Instrument (MSI) data. In the study, Random Forest (RF) and Classification and Regression Trees (CART) classier were used to classify grain crops (Barley and Wheat). The classification results based on the combination of Sentinel-2 and Sentinel-1 data indicated an overall accuracy (OA) of 93 % and a kappa coefficient (K) of 0.896 for RF and (89.15%, 0.84) for the CART classifier. It is suggested to employ a mix of radar and optical data to attain the highest level of classification accuracy since doing so improves the likelihood that the details will be observed in comparison to the single-sensor classification technique and yields more accurate results.

Drought is considered one of the severest natural disasters and it is difficult to predict it. This review article aimed to display the state of the art of methods used to predict and monitor types of droughts. We examine more than 30 indices and models to identify the strengths and weaknesses of methods and identify gaps remaining in this field. Examples of examined indies are Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI). The research found improvement in drought modeling, however, more focus and improvement are required to monitor and predict drought types. It also found that some methods outperform others such as PDSI, SPI, SPEI, EVI, NDVI, NDWI, VCI and TCI.