Land use–land cover (LULC) alteration is primarily associated with land degradation, especially in recent decades, and has resulted in various harmful changes in the landscape. The normalized difference vegetation index (NDVI) has the prospective capacity to classify the vegetative characteristics of many ecological areas and has proven itself useful as a remote sensing (RS) tool in recording vegetative phenological aspects. Likewise, the normalized difference built-up index (NDBI) is used for quoting built-up areas. The current research objectives include identification of LULC, NDVI, and NDBI changes in Jhelum District, Punjab, Pakistan, during the last 30 years (1990–2020). This study targeted five major LULC classes: water channels, built-up area, barren land, forest, and cultivated land. Satellite imagery classification tools were used to identify LULC changes in Jhelum District, northern Punjab, Pakistan. The perception data about the environmental variations as conveyed by the 500 participants (mainly farmers) were also recorded and analyzed. The results depict that the majority of farmers (54%) believe in the appearance of more drastic changes such as less rainfall, drought, and decreased water availability for irrigation during 2020 compared to 30 years prior. Overall accuracy assessment of imagery classification was 83.2% and 88.8% for 1990, 88.1% and 85.7% for 2000, 86.5% and 86.7% for 2010, and 85.6% and 87.3% for 2020. The NDVI for Jhelum District was the highest in 1990 at +0.86 and the lowest in 2020 at +0.32; similarly, NDBI values were the highest in 2020 at +0.72 and the lowest in 1990 at −0.36. LULC change showed a clear association with temperature, NDBI, and NDVI in the study area. At the same time, variations in the land area of barren soil, vegetation, and built-up from 1990 to 2020 were quite prominent, possibly resulting in temperature increases, reduction in water for irrigation, and changing rainfall patterns. Farmers were found to be quite responsive to such climatic variations, diverting to framing possible mitigation approaches, but they need government assistance. The findings of this study, especially the causes and impacts of rapid LULC variations in the study area, need immediate attention from related government departments and policy makers.

Cellular Automata models are employed for simulating geographical distributions, while Markov-Chain models are utilized for simulating temporal changes. This study aims to forecast the dynamics of land use and land cover (LULC) change and land surface temperature (LST) using a Cellular Automata (CA) and Markov-Chain model for the period 1992-2022 in Islamabad, Pakistan. This research innovatively predicts future LULC and LST and examines their correlations with various vegetation indices. LULC maps were generated from time series data of Landsat satellite images (Landsat 5, 7, and 8) for the years 1992, 2002, 2012, and 2022, utilizing the Random Forest (RF) algorithm. Five satellite indices were employed: Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BSI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), and Soil Adjusted Vegetation Index (SAVI). Through the CA-Markov-Chain analysis, the results showed significant new findings, such as a rise in the built-up area of Islamabad, which increased from 105.63 km2 (11.66%) in 1992 to 447.39 km2 (49.38%) in 2022. The study predicted that the built-up area would further increase to 531.82 km2 (55.38%) by 2042. The study analyzed the enhancement of LST, which was about 2.40°C from 1992 to 2022, ultimately because of the expansion of uncontrolled urban areas. The correlation of LST with the vegetation indexes NDVI, BSI, NDWI, NDBI, and SAVI was also analyzed through regression analysis. Furthermore, the surface temperature was predicted well by the Urban Index (UI), a non-vegetation index, demonstrating the positive correlation of R2 = 0.87 with respect to retrieved surface temperature. Using the Urban Index (UI) as a predictor of LST, our projections indicate that regions with temperatures ranging from 20–24°C and 25–28°C will decrease in coverage from 4.63% to 3.85% and from 25.33% to 21.08%, respectively, between 2032 and 2042.

The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10-15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, co-kriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling.

Soil moisture (SM) is a vital parameter in all environments because it influences the relationship between the land surface and atmospheric processes. Therefore, identifying products that can accurately measure SM is crucial for improving drought management. This study aimed to investigate the accuracy of SM satellite data produced by the China Meteorological Administration Land Data Assimilation System (CLDAS), focusing on the Huaihe and Heihe River basins, China, since both are susceptible to drought. To verify the accuracy of daily surface SM data measurements were obtained from 34 meteorological stations between January and December, 2016. In addition, the CLDAS measurements data were collected at depths of 10 cm and 40 cm and compared with observed soil moisture (OBS SM) measurements. The results show that the CLDAS SM agreed with OBS at 10 cm was R>0.66 and 40cm was R>0.47 in Huaihe River Basin. R>0.63 at 10cm and R>0.44 at 40 cm was observed in Heihe River Basin. The results indicated that CLDAS product was effective in measuring SM at different soil depths; however, the data were more accurate at a soil depth of 10 cm than at 40 cm. Consistency between 10 cm and 40 cm SM was acceptable in both regions. Therefore, the findings of this study justify the use of the CLDAS to measure SM in China. Further research is required to confirm the regions and circumstances that are most suited for these products.