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Self-supervised learning (SSL) has significantly bridged the gap between supervised and unsupervised learning in computer vision tasks and shown impressive success in the field of remote sensing (RS). However, these methods have primarily focused on single-modal RS data, which may have limitations in capturing the diversity of information in complex scenes. In this paper, we propose the Asymmetric Attention Fusion (AAF) framework to explore the potential of multi-modal representation learning compared to two simpler fusion methods: early fusion and late fusion. Given that data from active sensors (e.g., digital surface models and light detection and ranging) is often noisier and less informative than optical images, the AAF is designed with an asymmetric attention mechanism within a two-stream encoder, applied at each encoder stage. Additionally, we introduce a Transfer Gate module to select more informative features from the fused representations, enhancing performance in downstream tasks. Our comparative analyses on the ISPRS Potsdam datasets, focusing on scene classification and segmentation tasks, demonstrate significant performance enhancements with AAF compared to baseline methods. The proposed approach achieves an improvement of over 7% in all metrics compared to randomly initialized methods for both tasks. Furthermore, when compared to early fusion and late fusion methods, AAF consistently outperforms in achieving superior improvements. These results underscore the effectiveness of AAF in leveraging the strengths of multi-modal RS data for SSL, opening doors for more sophisticated and nuanced RS analysis. [ABSTRACT FROM AUTHOR]

Mangrove wetlands are hotspots of global biodiversity and blue carbon reserves in coastal wetlands, with unique ecological functions and significant socioeconomic value. Annual fine-scale monitoring of mangroves is crucial for evaluating national conservation programs and implementing sustainable mangrove management strategies. However, annual fine-scale mapping of mangroves over large areas using remote sensing remains a challenge due to spectral similarities with coastal vegetation, tidal periodic fluctuations, and the need for consistent and dependable samples across different years. In previous research, there has been a lack of strategies that simultaneously consider spatial, temporal, and methodological aspects of mangrove extraction. Therefore, based on an approach that considers mangrove habitat, tides, and a semantic segmentation approach, we propose a method for fine-scale mangrove mapping suitable for long time-series data. This is an optimized hybrid model that integrates spatial, temporal, and methodological considerations. The model uses five sensors (GF-1, GF-2, GF-6, ZY-301, ZY-302) to combine deep learning U-Net models with mangrove habitat information and algorithms during low-tide periods. This method produces a mangrove map with a spatial resolution of 2 m. We applied this algorithm to three typical mangrove regions in the Beibu Gulf of Guangxi Province. The results showed the following: (1) The model scored above 0.9 in terms of its F1-score in all three study areas at the time of training, with an average accuracy of 92.54% for mangrove extraction. (2) The average overall accuracy (OA) for the extraction of mangrove distribution in three typical areas in the Beibu Gulf was 93.29%. When comparing the validation of different regions and years, the overall OA accuracy exceeded 89.84% and the Kappa coefficient exceeded 0.74. (3) The model results are reliable for extracting sparse and slow-growing young mangroves and narrow mangrove belts along roadsides. In some areas where tidal flooding occurs, the existing dataset underestimates mangrove extraction to a certain extent. The fine-scale mangrove extraction method provides a foundation for the implementation of fine-scale management of mangrove ecosystems, support for species diversity conservation, blue carbon recovery, and sustainable development goals related to coastal development. [ABSTRACT FROM AUTHOR]

For crop growth monitoring and agricultural management, it is important to use hyperspectral remote sensing techniques to estimate canopy nitrogen content in a timely and accurate manner. The traditional nadir method has limited ability to assess the nitrogen trophic state of cotton shoots, which is not conducive to high-precision nitrogen inversion, whereas the multi-angle remote sensing monitoring method can effectively extract the canopy's physicochemical information. However, multi-angle spectral information is affected by a variety of factors, which frequently causes shifts in the band associated with nitrogen uptake, and lowers the estimation accuracy. The capacity of the spectral index to estimate aerial nitrogen concentration (ANC) in cotton was therefore investigated in this work under various observation zenith angles (VZAs), and the Relief−F method was employed to select the best spectral band with weight for ANC that is insensitive to VZA. Therefore, in this study, the ability of the spectral index to estimate ANC in cotton was explored under different VZAs, and the Relief-F algorithm was used to optimize the optimal spectral band with weight for ANC that is insensitive to VZA. The angle insensitive nitrogen index (AINI) for various VZAs was calculated using the expression (R530 − R704)/(R1412 + R704). The results show that the correlation between the spectral index and the ANC chosen in this study is stronger than the correlation between off-nadir observations, and the correlation coefficients between Photochemical Reflectance Index (PRI), AINI, and ANC are highest when VZA is −20° and −50° (r = 0.866 and 0.893, respectively). Compared with the traditional vegetation index, AINI had the best correlation with ANC under different VZAs (r > 0.84), and the performance of ANC in the backscatter direction was estimated to be better than that in the forward-scatter direction. At the same time, the ANC estimation model of the optimal indices AINI and PRI was combined with the machine learning method to achieve better accuracy, and the prediction accuracy of the random forest (RF) model was R2 = 0.98 and RMSE = 0.590. This study shows that the AINI index can estimate cotton ANC under different VZAs. Simultaneously, the backscattered direction is revealed to be more conducive to cotton ANC estimation. The findings encourage the use of multi-angle observations in crop nutrient estimation, which will also help to improve the use of ground-based and satellite sensors. [ABSTRACT FROM AUTHOR]

By studying the spectral information of cotton leaf nitrogen content, sensitive feature bands and spectral indices for leaf nitrogen content were screened, and different methods were used to model the screened feature bands and indices to find a method with higher accuracy and stability of the inversion model, which provides a theoretical basis and technical support for remote sensing estimation of cotton nitrogen content in Xinjiang. The experiment was conducted in 2019–2020 at the Second Company of Shihezi University Teaching Experimental Farm in Xinjiang, China, with six fertilization treatments (0, 120, 240, 360, 480 kg/hm pure N), sampled at five key fertility stages of cotton (squaring stage, full budding stage, flowering, boll stage, and boll opening stage), and the obtained data were used in two modeling approaches (eigenbands and spectral indices) to establish a cotton nitrogen estimation model and estimate the cotton leaf N content. The results showed that the nonlinear model using SVR was validated with an R2 of 0.71 and an RMSE of 3.91. The linear models of MLR and PLS were developed for the feature bands screened by SPA and RF, respectively, and the best modeling result was achieved by SPA-PLS with a validated R2 of 0.722 and an RMSE of 3.83. The existing spectral indices were optimized by screening the central wavelength and the simple linear regression model was constructed. The inversion effect of the SVR model with the characteristic spectral modeling was better than the index results. However, compared with the direct use of the characteristic wavelengths and the SVR way of modeling, the accuracy of leaf N content estimation by the model built by optimizing the spectral indices was reduced but the stability was greatly improved, and it can be used as a hyperspectral model for leaf N content at full fertility. The hyperspectral estimation of leaf N content in cotton can be used as a hyperspectral estimation method for the whole fertility period. [ABSTRACT FROM AUTHOR]

Groundwater icings, typical features of permafrost hydrology, are indicative of hydrothermal interactions between surface and ground waters, and permafrost. Their main morphological parameters, i.e., icing area and volume, are generally estimated with low accuracy. Only scarce field observational data on icing volume and seasonal development exist to date. Our study evaluates and compares performance of several widely used techniques of icing morphometric estimation, based on field data, collected on a giant Icing #2 in the Samokit River basin, southern Yakutia. Groundwater icing area was estimated by: (a) staking, (b) unmanned aerial vehicle (UAV) surveys, and (c) satellite imagery analysis. Icing #2 area in late February was between 1.38·106 m2 and 1.68·106 m2, icing volume, between 1.73·106 m3 and 4.20·106 m3, depending on the technique used. Staking is the least accurate, but also the only direct technique, which is hence used as a baseline tool in our study. Staking-based assessment of icing morphometry is the most conservative, while UAV-based estimates of icing area are higher by 14% to 17%, and of icing volume, by 74% to 142%, compared to staking. The latter appears, in our case, to be the least accurate method, although a direct one. It requires a sufficient number of staking points and transects, which should be set up to represent all icing zones, i.e., channel branches and alluvial islands. Photogrammetry based on UAV surveys has numerous advantages, i.e., higher precision of a per pixel icing volume calculation, based on an ice-free valley bottom digital surface model (DSM), and potential reusability of a resulting DSM. However, positioning precision suffers from the overlay of multiple flyovers required because of battery replacements, and, in our case, an insufficient number of ground control points. Satellite imagery along with B.L. Sokolov's empirical approach were used to estimate the annual maximum icing area and volume, and the empirical estimates tend to converge to satellite-based values. Finally, all thing being equal, UAV-based photogrammetry shows higher precision in estimating the icing morphometrical parameters. [ABSTRACT FROM AUTHOR]