Remote Sensing Applications-Society and Environment最新文献

This research endeavors to advance land surface temperature (LST) prediction accuracy through the development of a sophisticated machine learning model. Leveraging the potential of Sentinel 2 data and atmospheric parameters, we augment Landsat-based LST with MODIS-based LST, enriching the temporal dimensions of our dataset. A distinctive feature of our study is the pioneering use of Sentinel 2 data as inputs for LST prediction, a facet scarcely explored in the existing literature. Our investigation delves into the correlation dynamics between LST and atmospheric parameters. Notably, the study employs a diverse set of machine learning models, including Extra Trees, Random Forests, LightGBM, XGBoost, and Support Vector Regressor. These models collectively exhibit superior performance, with Extra Trees emerging as a standout performer, with a minimal mean absolute error (MAE) of 0.423, a root mean square error (RMSE) of 1.340 °C, and an impressive coefficient of determination ($R^2$) of 0.984. The exploration of Sentinel 2 data as an input source for LST prediction not only refines predictive accuracy but also opens novel research avenues in the realm of LST dynamics. This study contributes to the existing body of knowledge by introducing innovative methodologies and providing a comprehensive understanding of the intricate correlations influencing LST.

Recently, a strong international focus has been placed on invasive species and their ecological, economic, and social impacts. Satellite remote sensing (SRS) for the detection of invasive alien plants (IAPs) is a promising and actively researched application of satellite-derived earth observation data. Despite its all-day, all-weather detection and mapping capability, synthetic aperture radar (SAR) data is underrepresented in these efforts. This review discussed the foundational elements and capabilities of spaceborne SAR for IAP monitoring and investigated the current state of the scientific literature concerning the detection and monitoring of IAPs by spaceborne SAR. Twenty-six published articles were discovered and analysed for trends.

The analysis revealed several key findings regarding the current state of SAR in the detection and monitoring of IAPs. Data fusion techniques, especially those combining SAR with multispectral data, are gaining popularity due to their improved performance compared to single-sensor approaches. However, the full potential of SAR imagery, particularly polarimetric SAR (PolSAR), remains underutilised in multi-sensor studies. SAR analyses demonstrated strong performance in scenarios where the IAP structure exhibited distinct characteristics compared to its surroundings, such as plants isolated on water surfaces or palms displacing mangroves, due to the unique interactions of microwave radiation with the structural characteristics of targets.

Several key principles in the deployment of SAR were identified, including band and polarisation selection, basic techniques such as grey-level thresholding, and more advanced analyses such as polarimetry. Also noted are the capabilities of SAR in enabling indirect methods, such as inundation mapping and soil modelling. Suggestions are made for future directions in consideration of recently launched and forthcoming spaceborne SAR sensors. Significant among these are fully polarimetric systems which will provide freely accessible data, offering huge opportunities for sophisticated PolSAR analyses. This data will need to be fully exploited to advance species-level IAP detection and monitoring. Examples of IAPs which may benefit from SAR approaches are given, with special attention paid to the Australian Weeds of National Significance (WoNS).

Forests play a crucial role in the global climate system by acting as important carbon storage sinks and controlling the flow of carbon between land and the atmosphere. They provide a wide range of ecosystem services, including the supply of resources and biodiversity conservation. Deforestation is a significant issue leading to the release of carbon dioxide and greenhouse gases. The destruction and fragmentation of existing habitats pose significant threats to biodiversity. This study examined land use/land cover (LULC) alterations in the West Singhbhum district between 1987 and 2021, specifically emphasizing the influence of mining operations on the local forest ecosystem. This study used Landsat satellite imagery to examine data from 1987 to 2021, emphasizing five primary classifications: water body, mining area, built-up areas, open/cropland, and forest/vegetation. The maps were reclassified into two categories, namely, “No-Forest" and “Forest. Forest fragmentation maps were created using Landscape Fragmentation Tool (LFT) v2.0. A regression analysis was conducted to ascertain the correlation between mining growth and the reduction in forest cover. The analysis revealed increased mining areas, developed buildings, and cultivated land accompanied by a decline in forested areas and vegetation. There were substantial changes in land use, with mining areas expanding by 31.14 km² and open/cropland increasing by 30.39 km². The conversion of forested areas into agricultural zones and mining regions resulted in a 1.08% reduction in forest coverage.

Accurate root zone soil moisture (RZSM) estimation using remote sensing (RS) in areas with dense vegetation is essential for real-time field monitoring and precise irrigation scheduling. Traditional methods often face challenges due to the dense crop cover and the complexity of soil and climate interactions. These challenges include the coarse spatial resolution of available soil moisture products, the influence of vegetation and surface roughness, and the difficulty of estimating RZSM from surface data. Aiming to overcome these limitations, two RZSM estimation methods were developed by combining synthetic aperture radar (SAR) data from Sentinel-1 (VV and VH polarizations) and optical and thermal RS data from Landsat-8. These data sources were used in conjunction with various machine learning (ML) models such as M5-pruned (M5P), support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest regression (RFR) to improve the accuracy of soil moisture estimation. In addition to RS data, soil physical and hydraulic properties, meteorological variables, and topographical parameters were selected as inputs to the ML models for estimating the RZSM of sugarcane crops in Khuzestan, Iran. This study identified the temperature vegetation dryness index (TVDI) as a critical parameter for estimating RZSM in combination with the Sentinel-1 SAR data under high vegetation conditions. In both methods, the RFR algorithm outperformed, with similar performance, the XGBoost, SVR, and M5P algorithms in estimating soil surface moisture (R² = 0.89, RMSE = 0.04 cm³cm⁻³). However, the accuracy of the RFR algorithm decreased with increasing depth for both the optical-thermal and combined SAR and optical-thermal RS data. This decrease was more pronounced in the combined approach, particularly for the root zone, where the RMSE reached approximately 0.073 cm³cm⁻³. Accordingly, the key findings demonstrated that the optical-thermal RS data outperformed the SAR RS data for retrieving RZSM in high-vegetated areas. However, combining TVDI with SAR data is a substantial improvement that opens a new path in radar-based RZSM estimation methods under high vegetation conditions.