ABSTRACT Sensible heat flux is a key component of the surface energy flux that directly influences the urban energy balance. Changes in Land Use and Land Cover (LULC) affect heat transfer between the surface and the atmosphere, thereby influencing the distribution of available energy into latent, sensible, and ground heat. This study aimed to analyse the impact of LULC changes on sensible heat flux in the Itupararanga Environmental Protection Area, a remnant of the Brazilian Atlantic Forest biome, from 1986 to 2021. The significant increase in LULC categories such as temporary crops and urbanized areas, along with the reduction in pasturelands, substantially impacted sensible heat in the study area. The expansion of temporary crop areas contributed to a decrease in sensible heat during the study period, as these areas tend to retain more moisture due to the irrigation demands of the crops. Conversely, the growth of urbanized areas exhibited an unusual pattern, with sensible heat showing a decline despite these areas having the highest sensible heat averages overall. Notably, the year 1994 recorded the lowest sensible heat values, attributed to atypical weather conditions.

ABSTRACT Accurate wall-to-wall mapping of tree species composition (TSC) is essential for effective forest management. However, distinguishing species-level information from satellite imagery remains a challenge due to the coarse spatial resolution of open-access satellite imagery. In this study, we present the first systematic evaluation of spatial resolution enhancement and multi-seasonal data fusion for deep learning (DL)-based TSC mapping using Sentinel-2 imagery. Specifically, we assessed: (1) the impact of different spatial resolutions and enhancement methods, comparing native 20 m Sentinel-2 imagery against bilinear resampled imagery at 10 m and 5 m, super-resolution (SR)-enhanced imagery at 10 m and their combined use; (2) the contributions of multi-seasonal imagery and auxiliary environmental data (climate, topography); and (3) the effectiveness of a novel multi-source multi-seasonal fusion (MSMSF) method for integrating seasonal and environmental datasets. Our results demonstrated substantial improvements (7% higher R a d j 2 ) when increasing spatial resolution from 20 m to 10 m and achieved the best result (RMSE = 0.120, R a d j 2 = 0.731) by combining bilinear resampled 5 m and SR-enhanced 10 m datasets. Additionally, our proposed MSMSF module and multi-seasonal data outperformed the best single-season model by >5% in terms of R a d j 2 . These findings establish a new benchmark for DL-based TSC mapping and highlight the novelty of combining resolution enhancement with a detail-preserving fusion strategy to enable scalable, high-precision forest inventories using freely available satellite data.

ABSTRACT In recent years, the use of Unmanned Aerial Vehicles (UAVs) for remote sensing and aerial photogrammetry has surged, owing to their affordability and ability to capture high-resolution data in hard-to-reach areas. However, the effectiveness of these platforms can be limited by environmental factors such as turbulence and wind gusts, which may destabilize the aircraft and compromise the proper exposure of images. To address this issue, this work presents a terrain survey planner that considers the environmental conditions derived from a meteorological forecast. The system employs Computational Fluid Dynamics (CFD) simulations to generate high-resolution wind predictions for a given study area. This data is integrated into a state-of-the-art UAV simulator to estimate aircraft behaviour at various locations. Additionally, it incorporates an empirically calibrated camera model to predict sensor performance based on solar radiation estimates. With this information, a multi-objective optimization is performed, computing the optimal path and camera settings to mitigate the impact of wind on photogrammetry. Results highlight the significant impact of wind and poor lighting on motion blur, emphasizing the need to carefully plan not only the inspection path but also the time and date for correct image exposure.

ABSTRACT In relatively dry areas, due to lower soil moisture and less vegetation coverage, land surface microwave radiation comes from a certain depth of soil, while infrared skin temperature is only sensitive to the thin layer of the land surface. The inconsistencies in the detection depth of the microwave and infrared can lead to significant differences in retrieved emissivity between day and night. To improve the instantaneous microwave emissivity retrieval over barren areas, the land surface effective temperature calculation method was proposed based on AMSR2, which constructed the relationship between effective temperature and microwave brightness temperatures (TBs) in hours, approximating that monthly mean skin temperature equals monthly mean effective temperature and ignoring the change in emissivity over a month. The results showed that the effective temperature from 10.65 to 89 GHz had significantly smaller diurnal amplitude than skin temperature, and the lower the frequency, the smaller the amplitude. The effective temperature was then applied to the instantaneous emissivity inversion, and it was shown that this method remarkably reduced the emissivity difference between day and night, with the mean difference at the magnitude of 10^-3.

ABSTRACT Currently, hyperspectral image classification (HSIC) faces two major challenges: the attenuation of knowledge transfer efficiency caused by cross-domain distribution differences, and the insufficient generalization ability of representation learning under a single visual modality. Although scholars have attempted to address these challenges, there are still shortcomings in achieving deep cross-domain alignment and multimodal collaboration. Therefore, this paper proposes a dual stage adversarial domain adaptation (DSADA) HSIC framework which incorporates multi-modal learning. Specifically, this article proposes a dual stage adversarial learning framework that significantly alleviates the distribution shift between the source domain and the target domain and enhances the model’s cross-domain adaptability. In addition, the introduction of label text modality optimizes the similarity between image and text prototypes through cross-modal alignment mechanism to fully explore the complementary information between modalities and enhance feature discriminative power. This paper conducts systematic experimental verification on three hyperspectral image datasets, and the results show that DSADA can effectively alleviate cross-domain migration challenges in few shot scenarios and significantly improve the accuracy of HSIC.

ABSTRACT With the advancement of real-time object detection technology, maintaining high detection accuracy for small objects across multiple scales remains challenging. Conventional convolutional neural networks (CNNs) struggle to effectively capture multi-scale features, often failing to meet detection requirements. This study proposes RMRN-DETR, an optimized remote sensing image detection network based on multi-dimensional real-time detection and domain adaptation. First, we introduce a Multi-dimensional Real-time detection module (MR) to achieve efficient end-to-end accuracy improvement. Second, a Multi-dimensional Domain Adaptation module is proposed to address feature fusion across different scales, effectively capturing both low-level and high-level semantic information in a multi-scale hierarchy. Finally, a novel loss boundary regression module is introduced to enhance bounding box regression accuracy, precisely reflecting the discrepancy between predicted and ground-truth boxes. Experimental results demonstrate a 1.8% accuracy improvement over the baseline on the ROSD dataset and a 2.9% gain on the DIOR dataset. The proposed method significantly enhances the detection accuracy and efficiency of small objects in remote sensing images, demonstrating strong adaptability to complex multi-scale scenarios.

ABSTRACT Accurate estimation of crop leaf area index (LAI) dynamics at the field scale is crucial in precision agriculture. Crop LAI can directly affect agroecosystem functioning and water use efficiency through evapotranspiration and photosynthesis. An optical radiative transfer model PROSAIL, integrated with satellite remote sensing data, can simulate crop LAI dynamics. Mid-resolution Sentinel-2 and the emergence of novel high-resolution PlanetScope satellite data provide an opportunity to simulate LAI at 10 m and 3 m resolutions, respectively. In this study, we conducted a comprehensive analysis of LAI simulation from both satellite data to explore their potential in capturing within-field LAI variability. We employed a hybrid inversion approach, integrating the PROSAIL model with support vector regression (a machine learning approach), to simulate LAI across different phenological phases of the winter triticale crop in the years 2020 and 2021 at the agriculture field in Brandenburg, Germany, with high spatial variability in crop growth. For validation, ground observations of LAI across the field were obtained, covering different soil classes with varying yield potentials. This allowed us to examine the impact of soil class on LAI development, and we additionally performed a heterogeneity analysis using Rao’s Q index to capture within-field variability in simulated LAI. The results showed good accuracy of the simulated LAI from both satellite data, as indicated by Kling-Gupta-Efficiency (KGE) greater than 0.3 threshold and RMSE ranging from 0.48 to 1.51. Although both satellite data showed similar performance in terms of LAI magnitude, PlanetScope showed higher within-field variability and was more sensitive to the yield potential of different soil classes. Rao’s Q heterogeneity index map further indicated that PlanetScope captured more detailed and localized variations in LAI compared to Sentinel-2. These findings underscored the respective strengths of mid- and high-resolution satellite data in supporting precision agriculture and highlighted the value of PlanetScope for detailed field-scale applications.

ABSTRACT Methane is a potent greenhouse gas that causes nearly one-third of global warming, but its spatial and temporal dynamics are inadequately understood. This study addresses this gap by providing an integrated methane monitoring strategy for Western Canada for 2019–2024. We implement a quality-screened concentration-mapping strategy using multi-temporal Sentinel-5P methane concentration (XCH₄) and GIS-based Jenks classification to obtain reproducible hotspot and persistence maps. We add a unit-agnostic satellite – inventory concordance screen including Spearman’s ρ and bootstrapped Pearson’s r for prioritization that goes beyond the scope of the traditional air quality monitoring. Our results identify a persistent XCH₄ increase (1801–1878 ppb), with concentrations at their maximum during the autumn and winter months consistent with local activities like industrial and agricultural operations and heat demand. Hotspots recurring in the south of the four western provinces, that is, British Columbia, Alberta, Saskatchewan, and Manitoba, pose potential hazards to residents, while northeastern Manitoba hotspots threaten vulnerable ecosystems. To enhance interpretability and reproducibility, we include non-parametric variability envelopes that transparently convey temporal sampling uncertainty and improve comparability across provinces as descriptive summaries for decision support. Therefore, we recommend the incorporation of Sentinel-5P data into province-level methane monitoring and reporting frameworks to complement the emission inventories published by the Environment and Climate Change Canada. This will bridge policy gaps by complementing inventory-based models with concentration-based hotspot prioritization, thereby directing mitigation to high-risk locations. This information is crucial to achieve a global methane emission reduction of 75% by 2030 and Sustainable Development Goals 3, 13, and 15.

ABSTRACT Recently, generalist vision-language models (VLMs) have proven to be highly versatile across various tasks, making them indispensable in computer vision and natural language processing. Their broad pre-training enables robust performance across many domains. When it comes to tailoring these models for specific downstream tasks such as remote sensing (RS) image captioning, the traditional approach has been fine-tuning. Fine-tuning, however, is often computationally expensive and may compromise the model inherent generalization capabilities by over-specializing on limited domain-specific datasets. This paper investigates Retrieval-Augmented Generation (RAG) as an alternative strategy for adapting generalist VLMs to RS captioning without requiring fine-tuning. We introduce RAGCap, a retrieval-augmented framework that leverages similarity-based retrieval to select relevant image-caption pairs from the training dataset. These examples are then combined with the target image within a carefully designed prompt structure, guiding the generalist VLM to generate stylistically coherent RS captions to the training dataset. While our implementation utilizes SigLIP for retrieval and Qwen2VL as the base VLM, the proposed framework is universal and applicable to other models. Extensive evaluations on four RS benchmark datasets reveal that RAGCap achieves competitive performance compared to traditional fine-tuning approaches. Our findings suggest RAG methods like RAGCap offer a scalable, practical alternative to fine-tuning for domain adaptation in RS image captioning. Code will be available at: https://github.com/BigData-KSU/RAGCap.

ABSTRACT Hyperspectral unmixing (HU) aims to obtain subpixel-level material composition information, which is crucial for the precise advancement of hyperspectral image processing techniques. In recent years, deep learning (DL) has been widely applied to HU due to its strong ability to capture complex and nonlinear feature relationships in the data. However, relying solely on hyperspectral images for unmixing often fails to effectively distinguish objects in complex scenes, particularly when different endmembers exhibit similar spectral characteristics. To address this limitation, we propose a Multimodal Fusion Network (MMFNet) that incorporates the elevation information inherent in light detection and ranging (LiDAR) data. MMFNet is capable of simultaneously extracting spectral features from hyperspectral images and spatial features from LiDAR data. Furthermore, most existing DL-based HU methods operate only in the original spectral domain, which makes them susceptible to spectral variability, noise, and limited discriminative capacity. To overcome these challenges, we integrate Learnable Wavelet Transform (LWT) into MMFNet to adaptively decompose hyperspectral signals into multiple frequency subdomains, thereby mitigating spectral variability and noise while preserving spatial consistency. In addition, a Multi-Scale Convolution Fusion Module (MSCFM) is designed to capture semantic information at different receptive fields and enhance the fine-grained fusion of spectral and spatial features. Through these designs, MMFNet produces more robust and discriminative feature representations, enabling better separation of spectrally similar endmembers. Extensive experiments demonstrate that the proposed MMFNet outperforms several state-of-the-art unmixing methods.