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Semantic segmentation of Polarimetric SAR (PolSAR) images is an important research topic in remote sensing. Many deep neural network-based semantic segmentation methods have been applied to PolSAR image segmentation tasks. However, a lack of effective means to deal with the similarity of object features and speckle noise in PolSAR images exists. Thisstudy aims to improve the discriminative capability of neural networks for various intensities of backscattering coefficients while reducing the effects of noise in PolSAR semantic segmentation tasks. Firstly, we propose pre-processing methods for PolSAR image data, which consist of the fusion of multi-source data and false color mapping. Then, we propose a Multi-axis Sequence Attention Segmentation Network (MASA-SegNet) for semantic segmentation of PolSAR data, which is an encoder–decoder framework. Specifically, within the encoder, a feature extractor is designed and implemented by stacking Multi-axis Sequence Attention blocks to efficiently extract PolSAR features at multiple scales while mitigating inter-class similarities and intra-class differences from speckle noise. Moreover, the process of serialized residual connection design enables the propagation of spatial information throughout the network, thereby improving the overall spatial awareness of MASA-SegNet. Within the decoder, it is used to accomplish the semantic segmentation task. The superiority of this algorithm for semantic segmentation will be explored through feature visualization. The experiments show that our proposed spatial sequence attention mechanism can effectively extract features and reduce noise interference and is thus able to obtain the best results on two large-scale public datasets (the AIR-POlSAR-Seg and FUSAR-Map datasets). [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data gaps. A range of general models (GMs) for SM analysis topics (e.g., gap-filling, forecasting, and downscaling) have been introduced to address these shortcomings. This work presents a novel strategy (i.e., optimized wavelet-coupled fitting method (OWCM)) to enhance the fitting accuracy of GMs by introducing a wavelet transform (WT) technique. Four separate GMs are selected, i.e., elastic network regression, area-to-area regression kriging, random forest regression, and neural network regression. The fitting procedures are then tested within a downscaling analysis implemented between aggregated Global Land Surface Satellite products (i.e., LAI, FVC, albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST, and Random Forest Soil Moisture (RFSM) datasets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are analyzed using direct comparisons with in situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. The results demonstrate that OWCM-derived SM products are generally closer to the in situ SM observations, and better capture in situ SM dynamics during the unfrozen season, compared to the corresponding GM-derived SM product, which shows fewer time changes and more stable trends. Moreover, OWCM-derived SM products represent a significant improvement over corresponding GM-derived SM products in terms of their ability to spatially and temporally match RFSM. Although spatial heterogeneity still substantially impacts the fitting accuracies of both GM and OWCM SM products, the improvements of OWCMs over GMs are significant. This improvement can likely be attributed to the fitting procedure of OWCMs implemented in the WT space, which better captures high- and low-frequency image features than in the regular space. [ABSTRACT FROM AUTHOR]

Rapid urbanization has brought about many problems in urban environments, including the deterioration of the thermal environment, which greatly affects the sustainable development of cities and the health of urban residents. Therefore, a comprehensive and thorough study of the thermal environment is necessary. In this study, quantitative and qualitative analyses of the interannual and seasonal variation of the thermal environment in the urban area of Chongqing were conducted using a combination of equal sector analysis, mathematical statistics, and principal component analysis. In addition, the mechanism by which multiple integrated human and natural factors affect the urban thermal environment in different seasons was explored. The thermal environment of the land surface has a clear spatial correlation with built-up areas. From 2010 to 2020, the Liangjiang New Area and the western high-tech zone were the main areas of thermal environment area expansion. From 2010 to 2020, a decrease in the area of low-temperature zones and an increase in the area of high-temperature zones were the main trends. In particular, during the summer of 2020, the low-temperature zones almost disappeared, and the area of high-temperature zones was nearly twice as large as in the other seasons. The combined influence of multiple factors on surface temperature has certain seasonal characteristics. The slope, elevation, normalized water body index, fractional vegetation cover, normalized imperviousness index, and nighttime light intensity were the main factors affecting the surface temperature, whereas landscape pattern indicators, as well as the population and points of interest (POI) density, had a low influence. The strength of the combined influence of these multiple factors of the different seasons exhibited the following order: winter > spring > summer > autumn. [ABSTRACT FROM AUTHOR]

Urban impervious surfaces area (ISA) and green space (GS), two primary components of urban environment, are pivotal in detecting urban environmental quality and addressing global environmental change issues. However, the current global mapping of ISA and GS is not effective enough to accurately delineate in urban areas due to the mosaicked and complex structure. To address the issue, the hierarchical architecture principle and subpixel metric method were applied to map 30 m global urban ISA and GS fractions for the years 2015 and circa 2020. We use random forest algorithms for retrieval of the Normalized Settlement Density Index and Normalized Green Space Index from Landsat images using Google Earth Engine. The correlation coefficients of global urban ISA and GS fractions were all higher than 0.9 for 2015 and circa 2020. Our results show global urban ISA and GS areas in circa 2020 were 31.19 × 104 km2 and 17.16 × 104 km2, respectively. The novel ISA and GS fractions product can show potential applications in assessing the effects of urbanization on climate, ecology, and urban sustainability. [ABSTRACT FROM AUTHOR]

In recent years, global forest fires have occurred more frequently, seriously destroying the structural functions of forest ecosystem. Mapping the burn severity after forest fires is of great significance for quantifying fire's effects on landscapes and establishing restoration measures. Generally, intensive field surveys across burned areas are required for the effective application of traditional methods. Unfortunately, this requirement could not be satisfied in most cases, since the field work demands a lot of personnel and funding. For mapping severity levels across burned areas without field survey data, a semi-supervised transfer component analysis-based support vector regression model (SSTCA-SVR) was proposed in this study to transfer knowledge trained from other burned areas with field survey data. Its performance was further evaluated in various eco-type regions of southwestern United States. Results show that SSTCA-SVR which was trained on source domain areas could effectively be transferred to a target domain area. Meanwhile, the SSTCA-SVR could maintain as much spectral information as possible to map burn severity. Its mapped results are more accurate (RMSE values were between 0.4833 and 0.6659) and finer, compared to those mapped by ∆NDVI-, ∆LST-, ∆NBR- (RMSE values ranged from 0.7362 to 1.1187) and SVR-based models (RMSE values varied from 1.7658 to 2.0055). This study has introduced a potentially efficient mechanism to map burn severity, which will speed up the response of post-fire management. [ABSTRACT FROM AUTHOR]