Remote Sensing of Environment最新文献

With the upcoming spaceborne synthetic aperture radar (SAR) missions (BIOMASS, LuTan-1, NISAR, and TanDEM-L), it will become possible to extract vegetation height at a global scale by utilizing spaceborne low-frequency (L- and P-band) polarimetric synthetic aperture radar interferometry (PolInSAR) data. However, in the context of single-baseline parameter retrieval by the random volume over ground (RVoG) model, three main error sources that affect the inversion accuracy should be carefully considered, i.e., the ground scattering contribution, the spatial baseline configuration, and the temporal decorrelation (the main part of non-volume decorrelation). To make the estimation more reliable, several kinds of multibaseline PolInSAR inversion methods have been proposed over the past few years and have achieved improved inversion performances. Dual-baseline inversion effectively avoids the ambiguity of the ground contribution, whereas the performance is highly dependent on the appropriate combination of two spatial baselines as well as the mitigation of the non-volume decorrelation. In this study, we conducted in-depth research into the effect of these influencing factors on dual-baseline inversion, aiming to provide effective guidance on dual-baseline combination selection among multibaseline data. Accordingly, a novel multibaseline inversion scheme (MBLFPI) suitable for low-frequency PolInSAR data is proposed in this paper. The significant advantage of the new method is that the three aforementioned error sources can be taken into account simultaneously, without relying on external data. The proposed scheme was validated using L- and P-band SAR data acquired by the DLR's E-SAR/F-SAR and ONERA's SETHI systems, as well as corresponding light detection and ranging (LiDAR) data collected during the BioSAR-2008, AfriSAR-2016, and TropiSAR-2009 campaigns. A series of experiments was performed to evaluate the applicability and generalizability of the proposed method. The results showed that this innovative scheme produced forest height maps with a root-mean-square error (RMSE) of 2.37 m (R² = 0.88) and 3.13–4.43 m (R² = 0.35–0.94) in the L-band and P-band scenarios (boreal and tropical forest), respectively, indicating a significant improvement over the three conventional multibaseline methods and pure dual-baseline inversion. The comprehensive analysis provided in this paper should assist with and provide strong support for SAR system and mission design, and the proposed scheme could be considered a promising way for future spaceborne missions to invert vegetation parameters at a global scale.

The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km²), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.

Intertidal areas, which emerge during low tide, form a vital link between terrestrial and marine environments. Seagrasses, a well-studied intertidal habitat, provide a multitude of different ecosystem goods and services. However, owing to their relatively high exposure to anthropogenic impacts, seagrasss meadows and other intertidal habitats have seen extensive declines. Remote sensing methods that can capture the spatial and temporal variation of marine habitats are essential to best assess the trajectories of seagrass ecosystems. An advanced machine learning method has been developed to map intertidal vegetation from satellite-derived surface reflectance at a 12-band multispectral resolution and distinguish between similarly pigmented intertidal macrophytes, such as seagrass and green algae. The Intertidal Classification of Europe: Categorising Reflectance of Emerged Areas of Marine vegetation with Sentinel-2 (ICE CREAMS v1.0), a neural network model trained on over 300,000 Sentinel-2 pixels to identify different intertidal habitats, was applied to the open-access long term archive of systematically collected Sentinel-2 imagery to provide 7 years (2017–2023) worth of intertidal seagrass dynamics in 6 sites across Western Europe (471 Sentinel-2 Images). A combination of independently collected visually inspected Uncrewed Aerial Vehicle imagery and in situ quadrat images were used to validate ICE CREAMS. Having achieved a high seagrass classification accuracy (0.82 over 12,000 pixels) and consistent conversion into cover (19% RMSD), the ICE CREAMS model outputs provided evidence of site specific variation in trajectories of seagrass extent, when appropriate consideration of intra-annual variation has been considered. Inter-annual dynamics of sites showed some instances of consistent change, some indicated stability, while others indicated instability over time, characterised by increases and decreases across the time-series in seagrass coverage. This methological pipeline has helped to create up-to-date monitoring data that, with the planned continuation of the Sentinel missions, will allow almost real-time monitoring of these habitats into the future. This process, and the data it provides, could aid management practitioners from regional to international levels, with the ability to monitor intertidal seagrass meadows at both high spatial and temporal resolution, over continental scales. The implementation of Earth Observation for high-resolution monitoring of intertidal seagrasses could therefore allow for gap-filling seagrass datasets, and sustain specific and rapid management measures.

Existing, state-of-the-art vegetation models disagree by a factor of four on the seasonal amplitude of the global, terrestrial carbon cycle. This seasonal amplitude is likely increasing over time due to climate change, and disagreements among vegetation models therefore complicate efforts to quantify how climate change is impacting the carbon cycle. We evaluate the seasonal cycle of terrestrial CO₂ fluxes from an ensemble of vegetation models using CO₂ observations from the Orbiting Carbon Observatory-2 (OCO-2), in situ CO₂ observations, and inverse models. We find that vegetation models with a larger seasonal amplitude are also more sensitive to climate change, in that they exhibit a larger increase in amplitude during the past century. Furthermore, ten of the 17 models analyzed have a seasonal amplitude smaller than an ensemble of inverse CO₂ flux estimates based on OCO-2 observations; these discrepancies are largest across the Eastern US, boreal Asia, the Congo, and the Amazon. Vegetation models with larger seasonal amplitudes, when run through an atmospheric transport model (i.e. GEOS-Chem), typically exhibit a better fit compared to atmospheric CO₂ observations. We also find that vegetation models produce similar seasonal amplitudes of net CO₂ fluxes using very different combinations of gross primary production and respiration, making these model disagreements challenging to resolve.

In this era of global warming, the regular and accurate mapping of vegetation conditions is essential for monitoring ecosystems, climate sustainability and biodiversity. In this context, this work proposes a physics-guided data-driven methodology to invert radiative transfer models (RTM) for the retrieval of vegetation biophysical variables. A hybrid paradigm is proposed by incorporating the physical model to be inverted into the design of a neural network architecture, which is trained by exploiting unlabeled satellite images. In this study, we show how the proposed strategy allows the simultaneous probabilistic inversion of all input PROSAIL model parameters by exploiting Sentinel-2 images. The interest of the proposed self-supervised learning strategy is corroborated by showing the limitations of existing simulation-trained machine learning algorithms. Results are assessed on leaf area index (LAI) and canopy chlorophyll content (CCC) in-situ measurements collected on four different field campaigns over three European tests sites. Prediction accuracies are compared with performances reached by the well-established Biophysical Processor (BP) of the Sentinel Application Platform (SNAP). Obtained overall accuracies corroborate that the proposed methodology achieves performances equivalent to or better than the state-of-the-art methods.

Satellite-derived vegetation indices (VIs) have been extensively used in monitoring vegetation dynamics at local, regional, and global scales. While numerous studies have explored various factors influencing VIs, a remarkable knowledge gap persists concerning their applicability in mountain areas with complex topographic variations. Here we bridge this gap by conducting a comprehensive evaluation of the topographic effects on ten widely used VIs. We used three evaluation strategies, including: (i) an analytic radiative transfer model; (ii) a 3D ray-tracing radiative transfer model; and (iii) Moderate Resolution Imaging Spectroradiometer (MODIS) products. The two radiative transfer models provided theoretical evaluation results under specific terrain conditions, aiding in the first exploration of the interactions of both shadow and spatial scale effects on VIs. The MODIS-based evaluation quantified the discrepancies in VIs between MODIS-Terra and MODIS-Aqua over flat and rugged terrains, providing new insights into real satellite data across different temporal scales (i.e., from daily to multiple years). Our evaluation results were consistent across these three strategies, revealing three key findings. (i) The normalized difference vegetation index (NDVI) generally outperformed the other VIs, yet all VIs did not perform well in shadow areas (e.g., with a mean relative error (MRE) of 14.7% for NDVI in non-shadow areas and 26.1% in shadow areas). (ii) The topographic impacts exist at multiple spatiotemporal scales. For example, the MREs of NDVI reached 28.5% and 11.1% at 30 m and 3 km resolutions, respectively. The quarterly and annual VIs deviations between MODIS-Terra and MODIS-Aqua also increased with slope. (iii) We found the topography-induced interannual variations in multiple VIs both in simulated data and MODIS data. VIs trend deviations between MODIS-Terra and MODIS-Aqua over the Tibetan Plateau from 2003 to 2020 increased as the slope steepened (i.e., NDVI and enhanced vegetation index (EVI) trend deviations generally doubled). Overall, the sun-target-sensor geometry changes induced by topography, causing shadows in mountains along with obstructions in sensor observations, compromised the reliability of VIs in these terrains. Our study underscores the considerable impacts of topography, particularly shadow effects, on multiple VIs at various spatiotemporal scales, highlighting the imperative of cautious application of VIs-based trend calculation in mountains.

In the context of global urbanization, unplanned urban expansion renders cities particularly susceptible to the impacts of climate change, natural disasters, and extreme heat and humidity. Monitoring the land surface temperature (LST) in urban areas is crucial for assessing the urban thermal environment. Fine-scale (<100 m) LST products are essential for comprehensively understanding urban environments because of their detailed thermal distribution patterns. The Sustainable Development Science Satellite 1 (SDGSAT-1), launched on November 5, 2021, possesses the capability to capture fine-scale urban LST imagery at a 30-m resolution, both day and night. Based on the characteristics of the SDGSAT-1 thermal infrared data, we implemented two methods (the multiband-based (MBB) method and the two-band-based (TBB) method) to generate 30-m urban LSTs. The derived LSTs are evaluated against the MODIS LST product and in situ measurements. Furthermore, various simulation datasets are constructed based on the spectral characteristics of SDGSAT-1/TIS and utilized to derive LSTs using the MBB and TBB methods to further clarify the feasibility of the two methods. The results indicate that the MBB method exhibits superior performance in urban areas, with an RMSE that is 0.74 K lower than that of the TBB method. In contrast, the TBB method is suitable in areas with lower emissivity fluctuations, such as dense vegetated areas, with an RMSE that is 0.96 K lower than that of the MBB method. These two methods are planned for incorporation into the SDGSAT-1 LST production framework, thereby contributing to the advancement of accurate LST retrieval and achieving sustainable development goals in the future.

Large areas of Europe have been repeatedly affected by severe droughts. Stressed trees suffered from direct drought impacts such as water stress or heat and were also more susceptible to other biotic and abiotic stress agents and calamities. Monitoring such vulnerable forests area-wide is crucial to assess the highly dynamic climate change induced impacts not captured by traditional ground-based monitoring approaches. However, most remote sensing studies dealing with forest condition are either not species-specific, not accounting for morphological and climatic conditions across different regions, not considering natural variations in phenology or not including multiple disturbance agents. Here, we extract species-specific reflectance time series separately for seven natural regions covering Germany for 2016 to 2022. The seasonal evolution of these time series serves as reference for the detection of forest condition anomalies. We calculated a similarity metric – further called forest condition anomaly index ($FCA$) – between each single reflectance observation and the respective measurements within the reference time series, also considering the natural temporal deviations caused by phenology. Temporal aggregation of the $FCA$ allows the generation of spatially comprehensive forest condition anomaly maps. We demonstrate that the $FCA$ shows patterns related to fires, storms and insect infestations and found an overall agreement with state-of-the-art forest disturbance products using a threshold of $FCA=-0.15$ for forest loss. Consequently, the $FCA$ can be used to detect forest disturbances or linked with vegetation models to assess e.g. forest biomass or carbon flux.

The urban heat island (UHI) effect, a phenomenon of local warming over urban areas, is the most well-known impact of urbanization on climate. Globally consistent estimates of the UHI intensity (UHII) are crucial for examining this phenomenon across time and space. However, publicly available UHII datasets are limited and have several constraints: (1) they are for clear-sky surface UHII, not all-sky surface UHII and canopy (air temperature) UHII; (2) the estimation methods often neglect anthropogenic disturbance, introducing uncertainties in the estimated UHII. To address these issues, this study proposes a new dynamic equal-area (DEA) method that can minimize the influence of various confounding factors on UHII estimates through a dynamic cyclic process. Utilizing the DEA method and leveraging various gridded temperature data, we develop a global-scale (>10,000 cities), long-term (over 20 years by month), and multi-faceted (clear-sky surface, all-sky surface, and canopy) UHII dataset. Based on these estimates, we provide a comprehensive analysis of the UHII and its trends in global cities. The UHII is found to be greater than zero in >80% of cities, with global annual average magnitudes around 1.0 °C (day) and 0.8 °C (night) for surface UHII, and close to 0.5 °C for canopy UHII. Furthermore, an interannual upward trend in UHII is observed in >60% of cities, with global annual average trends exceeding 0.1 °C/decade (day) and over 0.06 °C/decade (night) for surface UHII, and slightly surpassing 0.03 °C/decade for canopy UHII. Notably, there exists a positive correlation between the magnitude and trend of UHII, suggesting that cities with stronger UHII tend to experience faster growth in UHII. Additionally, discrepancies in UHII are found between different temperature data, stemming not only from distinctions in data types (surface or air temperature) but also from differences in data acquisition times (Terra or Aqua), weather conditions (clear-sky or all-sky), and processing methodologies (with or without gap filling). Overall, our proposed method, dataset, and analysis results have the potential to provide valuable insights for future urban climate studies. The UHII dataset is publicly available at https://doi.org/10.6084/m9.figshare.24821538.

Extracting river networks that are both accurate and topologically connected is important for applications that involve correct routing of material, for example water and sediment, through such networks. We combined water and sediment extraction using radar and multispectral imagery from Sentinel-1 and Sentinel-2 to create both water and sediment masks over a range of study areas. These were then used to condition topographic Digital Elevation Models (DEMs) by lowering the elevation of pixels with both water and sediment present, in a process known as stream burning. We examined how stream burning could improve accuracy of extracted networks and identified the most effective method of burning for optimal results. We find deeper burning depths improved accuracy, with diminishing returns: we suggest burning 40 to 50 meters. We find sediment burning improves accuracy in humid and temperate landscapes, but arid landscapes should be burned using only water pixels. We find accuracy of extracted networks is significantly better on the COP30 global topographic dataset compared to the NASADEM dataset, mainly due to the time of collection. The AW3D30 DEM and FABDEM datasets have accuracies just below that of the COP30 DEM.