# Remote Sensing Applications in Agricultural, Earth and Environmental Sciences
###### Abstract  
This paper presents a novel approach to the detection of the detection  
assessed the effectiveness of different classification techniques and offered a comparative analysis of their accuracy in distinguishing land cover types. The findings emphasised the importance of integrating large-scale datasets with high-resolution satellite imagery to enhance environmental monitoring and land use planning. Similarly, advancements in climate data accuracy have been achieved by calibrating and validating MODIS-derived ground-level air temperature models. By incorporating extensive ground-based measurements, researchers refined these models and improved the reliability of climate trend analyses and forecasting. Developments in image processing have further strengthened remote sensing applications [12]. A recent study introduced a fusion-based thresholding and non-linear diffusion approach to mitigate speckle noise in SAR imagery, enhancing data clarity and interpretability. These advancements are crucial for applications requiring high-precision remote sensing data, which may also include flood monitoring, disaster response, and infrastructure assessment. In precision agriculture, remote sensing technologies have demonstrated their efficacy in optimising crop management [4; 14]. One study examined the impact of nematode infestation on soybean production using aerial multispectral imagery and machine learning techniques. The findings highlighted the potential of artificial intelligence in identifying crop stress and facilitating targeted interventions to improve yield and mitigate economic losses. Geological studies have also benefited from remote sensing advancements [15; 16]. Machine learning algorithms were successfully applied to differentiate lithological formations in the Khyber Range and showcased the utility of remote sensing in geological mapping and mineral exploration. Furthermore, a combined approach integrating remote sensing data with ground-based observations enabled a spatio-temporal analysis of heatwaves, which provided insights into extreme temperature trends and their implications for climate adaptation strategies. Vegetation monitoring has emerged as an important area where remote sensing is instrumental [17; 18]. Automated computation of vine objects and ecosystem structures through UAS-based data acquisition and 3D point cloud analysis has demonstrated the effectiveness of object-based image analysis in ecological studies. Additionally, satellite-driven automation for detecting moving events based on predicted compressed sward heights has enhanced grassland management, which contributes to sustainable pasture maintenance. Climate studies have significantly advanced from remote sensing research [19]. A long-term analysis of mid-tropospheric temperature trends over Tibet and the Eastern Himalayas (1978-2022) utilised satellite-derived data to track atmospheric temperature changes. In a separate study, machine learning algorithms improved forest detection accuracy in southeastern Serbia, which enhanced land use monitoring and forest conservation. Moreover, multisource data fusion techniques facilitated historical eco-environmental quality mapping in China and highlighted the potential of remote sensing in large-scale ecological assessments. Agricultural applications remain a focal point of remote sensing research [9; 20]. Investigations into the predictive capabilities of single spectral bands, vegetation indices, and their combinations have provided valuable insights into nitrogen-level estimation in protected mountainous grasslands. Furthermore, unmanned aerial vehicle hyperspectral remote sensing has been utilised to detect ratholes in desert steppe regions. In contrast, hyperspectral imaging has been employed to analyse the influence of water availability on nitrogen discrimination in sugar beet and celery crops, which informs precision nutrient management strategies.