Remote Sensing of Environment最新文献

Seasonal snow plays a crucial role in society and understanding trends in snow depth and mass is essential for making informed decisions about water resources and adaptation to climate change. However, quantifying snow depth in remote, mountainous areas with complex topography remains a significant challenge. The increasing availability of high-resolution synthetic aperture radar (SAR) observations from active microwave satellites has prompted opportunistic use of the data to retrieve snow depth remotely across large spatial and frequent temporal scales and at a high spatial resolution. Nevertheless, these novel SAR-based snow depth retrieval methods face their own set of limitations, including challenges for shallow snowpacks, high vegetation cover, and wet snow conditions. In response, here we introduce a machine learning approach to enhance SAR-based snow depth estimation over the European Alps. By integrating Sentinel-1 SAR imagery, optical snow cover observations, and topographic, forest cover and snow class information, our machine learning retrieval method more accurately estimates snow depth at independent in-situ measurement sites than current methods. Further, our method provides estimates at 100 m horizontal resolution and is capable of better capturing local-scale topography-driven snow depth variability. Through detailed feature importance analysis, we identify optimal conditions for SAR data utilization, thereby providing insight into future use of C-band SAR for snow depth retrieval.

Urbanization has exerted considerable impacts on urban water systems and ecological environments, yet its effects on local meteorological drought remain under-explored. The primary challenge to local-scale drought analysis is the scarcity of meteorological datasets with sufficient spatial and temporal resolution. To address the research gap, we initially proposed a two-step fusion framework, integrating both surface (i.e., gridded data)-surface and point (i.e., in-situ data)-surface fusion. The framework was applied to generate daily precipitation and average/maximum/minimum air temperature at a 1 km resolution through the integration of high-resolution remotely sensed datasets across the Yangtze River Basin (YRB), China. The final fused data demonstrated excellent performance, achieving a PCC (RMSE) of 0.806 (5.414 mm/day), 0.993 (1.138 °C), 0.987 (1.443 °C), and 0.988 (1.376 °C) for precipitation and average/maximum/minimum air temperature, respectively. A comparison of our fused data with CPC, ERA5-Land, CMFD, CHIRPS, IMERG, and TMPA products confirmed its capability in capturing local-scale meteorological dynamics by improving spatial resolution from 0.1°-0.25° to 1 km. Utilizing these high-resolution datasets, we quantified urbanization's impacts on local drought across 52 major cities in the YRB. We found that urbanization significantly magnified extreme Standardized Precipitation Evapotranspiration Index (SPEI) and drought severity in 69.2% and 61.5% of these cities, respectively. The effects of urbanization on extreme SPEI were amplified by the increase of urbanization rates, with a slope of −0.24 (p < 0.05). To further examine the spatial patterns of urbanization-induced local drought, we proposed a drought spatial field identification method, utilizing it in three representative urban regions: Chengdu, Wuhan, and the Yangtze River Delta. Our findings revealed that urbanization led to more intense peak drought intensity and average drought severity. In addition, urban drought fields showed lower effective radius, indicating more concentrated drought towards urban regions. While urbanization is projected to continue alongside rapid population growth in the future, the advanced application of remote sensing data and technology in this study not only improves our understanding of urban water resource challenges but also equips urban planners with the necessary data to develop effective drought mitigation strategies.

Changes in the net carbon sink of boreal forests constitute a major source of uncertainty in the future global carbon budget and, hence, climate change projections. The annual net ecosystem exchange of carbon dioxide (CO₂) controlling the terrestrial carbon stock results from the small difference between respiratory CO₂ release and the photosynthetic CO₂ uptake by vegetation. The boreal forest, and the boreal biome in general, is regarded as a persistent and even increasing net carbon sink. However, decreases in photosynthetic CO₂ uptake and/or concurrent increases in respiratory CO₂ release under a changing climate may turn boreal forests from a net sink to a net source of CO₂. Here, we assessed the interannual variability of the boreal forest net CO₂ sink-source strength and its two component fluxes from 1981 to 2018. Our remote sensing approach - trained by net CO₂ flux observations at eddy covariance sites across the circumpolar boreal forests - employs satellite-derived retrievals of snowmelt timing, landscape freeze-thaw status, and yearly maximum estimates of the normalized difference vegetation index as a proxy for peak vegetation productivity. Our results suggest that for the period 2000–2018, the mean annual evergreen boreal forest CO₂ photosynthetic uptake (gross primary productivity) was $2.8\pm$0.2 Pg C y⁻¹ ($1.6\pm$0.1 Pg C y⁻¹ for Eurasia and $1.2\pm$0.1 Pg C y⁻¹ for North America). In contrast to earlier studies results obtained here do not indicate a clear increasing trend in the circumpolar evergreen boreal forest CO₂ sink. The increase in photosynthetic CO₂ uptake is compensated by increasing respiratory releases with both component fluxes showing considerable interannual variabilities.

As one of the most important staple foods globally, rice sustains nearly half of the world's population. Accurate and timely paddy rice mapping is, thus, essential for rice-related policy-making to ensure food security in the context of anthropogenic, environmental and climate changes. However, paddy rice mapping remains a challenging task since it usually has similar spectral characteristics to other land covers. In this research, for the first time, an entirely new approach, called RiceTColour, was proposed for mapping rice fields within the Commission Internationale de l'Eclairage (CIE) colour space based on their unique spectra during the rice transplanting period as observed in remotely sensed imagery. We demonstrate that transplanted rice fields, representing a mixture of soil, water and rice seedlings, consistently exhibit relatively low spectral values in both SWIR and NIR bands across various geographical locations, leading to their unique dark green colours in the false-colour image composed of SWIR, NIR and Red bands. Based upon this, we transformed these three spectral bands into the CIE colour space where paddy rice was found to be readily and completely separated from the other land covers. Straightforward, but specific classification criteria were established within the CIE colour space to differentiate paddy rice from the other land covers. The proposed RiceTColour, thus, represents a new approach for paddy rice identification, that is mapping paddy rice using the CIE colour space based upon the previous underexplored remotely sensed spectra of paddy fields during the transplanting season. The effectiveness of the proposed method was investigated over five rice-planting regions distributed across different geographical regions, characterised by different climates, rice cropping intensities, irrigation schemes and cultural practices. Specifically, the mapping criteria established in a training site (S1) were directly generalised to the other four sites (S2 to S5) for paddy rice mapping. Experimental results demonstrated that the RiceTColour method consistently achieved the most accurate and balanced classifications across all five sites compared with four benchmark comparators: a SAR-based method, an index-based method and two supervised classifier-based methods. In particular, the RiceTColour method performed relatively stable, producing an overall accuracy exceeding 95% in the training site (S1) as well as the four generalised sites (S2 to S5), which is an encouraging result. Such efficient yet stable rice mapping results across various rice-planting regions suggest a very strong generalisation capability of the proposed RiceTColour method. In consideration of the relatively large planting area of paddy rice fields globally, the proposed parameter-free, efficient, and generalisable RiceTColour method, thus, holds great potential for widespread application in various rice-planting areas worldwide.

Spaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by generating broad-scale measurements sensitive to the vertical arrangement of plant matter as a supplement to in situ measurements. Lidar measurables are not directly relatable to most physical attributes of interest, including biomass, and therefore must be related through statistical models. Further, GEDI observations are not spatially complete, necessitating methods to convert the incomplete samples to predictions of area averages/totals. Such methods can face challenges in equatorial and persistently cloudy areas, such as Indonesia, where the density of quality observations is diminished. We developed and implemented a hierarchical model to produce gap-free maps of aboveground biomass density (AGBD) at various resolutions within the lowlands of Jambi province, Indonesia. A biomass model was trained between local field plots and a metric from GEDI waveforms simulated with coincident airborne laser scanning (ALS) data. After selecting a locally suitable ground-finding algorithm setting, we trained an error model depicting the discrepancies between the simulated and GEDI-observed waveforms. Finally, a geostatistical model was used to model the spatial distribution of the on-orbit GEDI observations. These three models were nested into a single hierarchical model, relating the spatial distribution of GEDI observations to field-measured AGBD. The model allows spatially complete predictions at arbitrary resolutions while accounting for uncertainties at each stage of the relationship. The model uncertainties were low relative to the predicted biomass, with a median relative standard deviation of 8% at the 1 km resolution and 26% at the 100 m resolution. The spatially consistent information on AGBD provided by our model is beneficial in support of sustainable forest management, carbon sequestration initiatives and the mitigation of climate change. This is particularly relevant in a dynamic tropical landscape like Jambi, Indonesia in order to understand the impacts of land-use transformations from forests to cash crops like oil palm and rubber. More generally, we advocate for the use of hierarchical models as a framework to account for multiple stages of relationships between field and sensor data and to provide reliable uncertainty audits for final predictions.

Effective monitoring of soil and vegetation properties on a global scale is essential for better understanding climate changes, hydrological dynamics, and ecological processes. Passive microwave remote sensing at C-band radio frequency, with long observation period and relatively high penetration capability, has been widely used to retrieve soil moisture (SM) and vegetation optical depth (C-VOD). The retrieval process is generally achieved by inversion of the τ-ω radiative transfer model, which depends on crucial parameters such as effective scattering albedo (ω) and soil roughness (H_R) for accurate retrievals. Current SM/C-VOD retrieval algorithms, such as the Land Parameter Retrieval Model (LPRM), predominantly rely on globally-constant ω and H_R values, ignoring the inherent sensitivity of those parameters to varying soil conditions and vegetation types. To evaluate the impact of ω and H_R variables on SM and C-VOD retrievals and to improve their accuracy, this study proposed and evaluated a novel retrieval approach from AMSR2 C-band observations during 2017–2020 using the C-band Microwave Emission of the Biosphere (C-MEB) model. We evaluated two new retrieval algorithms, considering either a globally-constant calibration or a land cover-based calibration of ω and H_R. As a benchmark for the calibration, we optimized the values of ω and H_R by evaluating the retrieved SM against in situ measurements from the International Soil Moisture Network (ISMN) and OzNet hydrological monitoring networks. The main originality compared to previous algorithms is that i) it includes a comprehensive calibration exploring the optimal values of ω and H_R, applicable globally or tailored to specific land cover; ii) field SM measurements were leveraged to constrain the calibrated value of ω and H_R.

For the globally-constant calibration, the optimal values of ω = 0.05 and H_R = 0.1 were found to yield the best results. For the land cover-based calibration, an inverse relationship between ω/H_R and canopy height was observed, with ω ranging from 0.04 to 0.06 and H_R ranging from 0.1 to 0.7 for heights between 0 and 30 m. The algorithm employing a land cover-based calibration (INRAE Bordeaux 2, IB2) exhibited better performance than the one utilizing a globally-constant calibration (INRAE Bordeaux 1, IB1) in evaluating retrieved SM against in situ measurements, as well as in evaluating C-VOD vs various vegetation variables including aboveground biomass (AGB), tree cover, canopy height and several optical vegetation indices. Comparison with LPRM suggested that our IB2 C-VOD retrievals present improved performances in terms of both spatial and temporal results with all considered vegetation variables (spatial correlation (R) between various vegetation variables and C-VOD of 0.76–0.83 for IB2 vs 0.69–0.79 for LPRM), and exhibited lower saturation effects