Science of Remote Sensing最新文献

Soil moisture (SM) is an essential climate variable that directly and indirectly affects vegetation growth and survival through land‒atmosphere interactions. Alpine vegetation on the Tibetan Plateau is part of a unique ecosystem that is vulnerable to changes in environmental factors such as SM; consequently, this makes this ecosystem extremely sensitive to climate change. This study investigated the potential of synthetic aperture radar (SAR) vegetation indices based on Sentinel-1 data for retrieving SM at high spatial resolution (10 m) over an alpine grassland ecosystem in the Nagqu region. Several SAR vegetation indices, including the dual polarization SAR vegetation index (DPSVI), modified dual polarization SAR vegetation index (mDPSVI), dual polarimetric radar vegetation index (DpRVI), polarimetric radar vegetation index (PRVI), and radar vegetation index (RVI), were used in the semiempirical water cloud model (WCM) to determine which indices provide better SM retrievals in this alpine ecosystem. In addition, the potential of the distributed random forest (DRF) machine learning algorithm was explored using the same variables as the WCM together with several ecohydrological parameters from different data sources. The recursive feature elimination algorithm was used to establish the optimized DRF model. Among the vegetation indices based on SAR data, DPSVI, DpRVI, and PRVI showed similar results, with DPSVI performing slightly better than the other SAR indices, with a correlation coefficient (R²) of 0.70 and root mean squared error (RMSE) of 0.04 m³m^-3. A comparison of the optimized DRF with the best fitted WCM reveals that the DRF algorithm outperformed the WCM, including having more predictors (10 variables) in the model. The results show that the overall accuracies in terms of the R² values and the RMSEs of both the WCMs and the DRF models were 0.52–0.75 and 0.08 m³ m⁻³ to 0.04 m³ m⁻³, respectively, which was validated over in situ SM measurements in the Nagqu region.

Estimating soil moisture from microwave brightness temperature is extremely challenging in densely vegetated areas. The soil moisture retrieved from the Soil Moisture Active Passive (SMAP) measurements tends to be consistently overestimated, sometimes exceeding the saturation level of mineral soils. Therefore, the retrieved soil moisture cannot detect or monitor climate extremes, such as floods and droughts for forests, natural resource management, and climate change research. We hypothesize that the main issue is that the scattering albedo (ω) and the optical depth (τ) are parameterized solely with NDVI (Normalized Difference Vegetation Index), neglecting the polarization characteristics from vegetation structure. This study proposes a weighting factor between scattering and optical thickness, a function of MPDI (Microwave Polarization Difference Index), and applies it to both parameters simultaneously to increase the scattering effect and decrease the attenuation effect in high MPDI. The validation results based on the Climate Reference Network revealed that considering MPDI is critical in reducing soil moisture overestimation errors and obtaining more accurate soil moisture over forested regions. This results in correlation improving from 0.36 to 0.44, a decrease in ubRMSE from 0.179 to 0.125 cm³cm⁻³, and bias lowering from 0.127 to 0.060 cm³cm⁻³ in comparison with the SMAP measurements over forested regions.

This study addressed the complex challenges associated with landslide detection along the Karakoram Highway (KKH), where tectonic events and data availability limitations posed significant obstacles. To overcome these hurdles, the research framework encompassed several critical components. First, it tackled the issue of multicollinearity through the application of statistical measures such as Variable Inflation Factor (VIF) and Information Gain (IG). Secondly, the study emphasized the importance of selecting a study area that would comprehensively represent the multivariate landscape, with KKH serving as an illustrative example. In striving for an equilibrium between implementation ease and algorithmic performance, the research favored the adoption of Random Forest (RF) and Extremely Randomized Trees (EXT) over XGBoost. Lastly, to fine-tune the algorithms and optimize their parameters, the study employed Particle Swarm Optimization (PSO) and evaluated their performance using metrics like the Area Under the Curve (AUC). Remarkably, this comprehensive approach yielded accuracy rates exceeding 90% for all algorithms tested (RF, EXT, and K-Nearest Neighbor (KNN)), with specific AUC values of 0.967, 0.968, and 0.914, respectively. These findings offer invaluable insights into enhancing disaster prevention strategies and informing land-use planning efforts along the KKH highway.

This study explores the potential of integrating satellite retrievals of surface soil moisture (SSM) and vegetation conditions into the Noah-MP land surface model. In total, five data assimilation (DA) experiments were carried out. One of the experiments only assimilates SSM retrievals from the Soil Moisture Active Passive mission, two experiments only assimilate retrievals of vegetation conditions: either optical retrievals of leaf area index (LAI) from the Copernicus Global Land Service, or X-band microwave-based retrievals of vegetation optical depth (VOD) from the Advanced Microwave Scanning Radiometer 2. Additionally, two joint DA experiments are performed, each incorporating SSM and one of the vegetation products. The DA experiments are compared with a model-only run, and all experiments are evaluated using independent ground reference data of soil moisture, evapotranspiration, net ecosystem exchange and gross primary production (GPP). Assimilating only SSM improves estimates of the soil moisture profile (median SSM anomaly correlation improves with 0.02 compared to a model-only run), whereas assimilating LAI predominantly improves GPP estimates (reduction in median RMSD of 0.024 gC m⁻² day⁻¹ compared to a model-only run). The joint assimilation of SSM and vegetation conditions captures both of these improvements in a single, physically consistent analysis product. The DA increments show that this combined setup allows one satellite product to compensate for potential degradations introduced into the system by the other product. Furthermore, the joint SSM and VOD DA experiment has the smallest ensemble spread in its estimates (21% reduction in SSM spread compared to a model-only run). Overall, our results underline the potential of multi-sensor and multivariate DA, in which information from different sources is combined to improve the estimates of several land surface states and fluxes simultaneously.

Land surface temperature (LST) and its diurnal variability are key to understanding the land-atmosphere interactions, hydrological processes and climate change. However, at any given point in time approximately half of the Earth's surface is covered by clouds. This restricts the availability of LST through satellite remote sensing, which works best under clear skies. However, in situ observations continue to monitor atmospheric conditions beneath the clouds that could complement satellite measurements during cloudy conditions. The present study explores a novel approach to estimate hourly LST during the daylight hours using remotely sensed surface solar absorption and in situ observations of daily LST extremes (maximum and minimum) together with an adaptive non-linear fitting approach. A learning algorithm trained against in-situ measurements of LST extrema and diurnal cycle of surface solar absorption together with the associated linear correlation between the two parameters, is used to estimate an optimized set of parameters to approximate hourly LST for each day during the daylight hours between sunrise and sunset. Results show that the method captures the intra-day variability of LST very well under most sky conditions with rms errors below 1.5 K.

Topographical changes are of fundamental interest to a wide range of Arctic science disciplines faced with the need to anticipate, monitor, and respond to the effects of climate change, including geohazard management, glaciology, hydrology, permafrost, and ecology. This study demonstrates several geomorphological, cryospheric, and biophysical applications of ArcticDEM – a large collection of publicly available, time-dependent digital elevation models (DEMs) of the Arctic. Our study illustrates ArcticDEM's applicability across different disciplines and five orders of magnitude of elevation derivatives, including measuring volcanic lava flows, ice cauldrons, post-failure landslides, retrogressive thaw slumps, snowdrifts, and tundra vegetation heights. We quantified surface elevation changes in different geological settings and conditions using the time series of ArcticDEM. Following the 2014–2015 Bárðarbunga eruption in Iceland, ArcticDEM analysis mapped the lava flow field, and revealed the post-eruptive ice flows and ice cauldron dynamics. The total dense-rock equivalent (DRE) volume of lava flows is estimated to be (1431 ± 2) million m³. Then, we present the aftermath of a landslide in Kinnikinnick, Alaska, yielding a total landslide volume of (400 ± 8) × 10³ m³ and a total area of 0.025 km². ArcticDEM is further proven useful for studying retrogressive thaw slumps (RTS). The ArcticDEM-mapped RTS profile is validated by ICESat-2 and drone photogrammetry resulting in a standard deviation of 0.5 m. Volume estimates for lake-side and hillslope RTSs range between 40,000 ± 9000 m³ and 1,160,000 ± 85,000 m³, highlighting applicability across a range of RTS magnitudes. A case study for mapping tundra snow demonstrates ArcticDEM's potential for identifying high-accumulation, late-lying snow areas. The approach proves effective in quantifying relative snow accumulation rather than absolute values (standard deviation of 0.25 m, bias of −0.41 m, and a correlation coefficient of 0.69 with snow depth estimated by unmanned aerial systems photogrammetry). Furthermore, ArcticDEM data show its feasibility for estimating tundra vegetation heights with a standard deviation of 0.3 m (no bias) and a correlation up to 0.8 compared to the light detection and ranging (LiDAR). The demonstrated capabilities of ArcticDEM will pave the way for the broad and pan-Arctic use of this new data source for many disciplines, especially when combined with other imagery products. The wide range of signals embedded in ArcticDEM underscores the potential challenges in deciphering signals in regions affected by various geological processes and environmental influences.

Glaciers are primarily monitored using medium-to-high resolution satellite data, undermining the potential of coarse-resolution data. In pursuance of this, high resolution 10 m super-resolved glacier maps derived from 56 m coarse-resolution AWiFS data are applied here to assess the facies, firn-line altitude, and frontal variations of the Gangotri and neighbouring glaciers, central Himalaya between 2005 and 2017. The wet and warming trends estimated over the study area appear to have caused excess firn (56.53 ± 6.22%) and ice (27.50 ± 3.03%) melting, contributing to the significant progression in fresh and slightly metamorphosed snow (12.09 ± 1.33%), wet-snow (21.79 ± 2.40%), ice-mixed debris (9.24 ± 1.02%) and supraglacial debris (2.49 ± 0.27%) during 2005–2016. Mean firn-line of the study glaciers has ascended from 5327 ± 23 m to 5376 ± 24 m at an average rate of 3.44 ± 0.45 m a⁻¹ during 2005–2016. Mean firn-line altitude ascent is the highest for the sparsely debris-covered (<10% debris) Arwa glacier followed by the extensively debris-covered (≥35% debris) Gangotri, Bhagirathi-Kharak and Satopanth glaciers. Contrastively, the moderately debris-covered (17–29% debris) Raktvarn and Chaturangi glaciers show slight variations in their mean firn-line altitudes. These firn-line variations are governed by the rising average annual temperature, glacier size and predominant glacier facie. All the glaciers show an overall tendency of termini retreat at variable rates during 2005–2017. The highest retreat rate is estimated for the Gangotri glacier (12.01 ± standard deviation: 8.16 m a⁻¹) followed by Chaturangi (7.97 ± 5.79 m a⁻¹), Bhagirathi-Kharak (5.99 ± 9.26 m a⁻¹), Raktvarn (3.28 ± 2.28 m a⁻¹), Satopanth (1.89 ± 2.87 m a⁻¹), and Arwa (0.85 ± 1.90 m a⁻¹) glaciers. These retreat rates vary significantly with the exclusion of static points in the retreat estimation, revealing its subjective nature. The temporal facies maps obtained here have the potential for the hydrological modelling of meltwater production of the study glaciers.

Monitoring forest growth accurately is important for assessing and controlling forest carbon stocks that impact, for example, the atmospheric CO₂ concentration and, consequently, the climate change. In prior studies, forest growth monitoring with laser scanning methods has resulted in relatively high errors. However, the contribution of reference measurement error to uncertainty in growth resolution has rarely been analysed, and the reference measurements are usually considered mostly flawless. In this study, a seven-year-long growth of individual trees was estimated using both airborne and terrestrial laser scanning (ALS, TLS) that have emerged as potential candidates for digital forest reference measurements. The growth values were derived for diameter at breast height (DBH) and stem volume between the years 2014 and 2021 using an indirect approach. The values obtained with laser scanning were paired with manual field measurements and also with each other to study pairwise errors. The pairwise comparison showed that even though all the three measurement methods produced good Pearson correlation coefficients for one-time measurements (all above 0.88), the coefficients for growth measurements were significantly lower (0.19–0.44 for DBH and 0.47–0.66 for stem volume). The best correlation and root mean squared deviation (RMSD) for DBH growth (ρ = 0.44, RMSD = 0.98 cm) and stem volume growth (ρ = 0.66, RMSD = 0.052 m³) was observed between the manual field measurements and the ALS-based growth measurement method, in which the tree stem curve was obtained from the 2021 point cloud, and the stem curve was predicted backwards for the year 2014 according to height growth. The ALS method suffered less from outlying values than the TLS-based growth measurement method, in which the growth was computed based on the difference of stem curves derived separately for the years 2014 and 2021. The study showed that observing the stem curve is a potential method for short-period growth monitoring. Using the pairwise comparison results, we further derived estimates for the mean and standard deviation of measurement error of each individual measurement method. For the manual measurements, the standard deviation of error was found to be approximately 0.4 cm for DBH growth and 0.03 m³ for volume growth, which were the lowest of the three methods but not by a large margin. This highlights the need for more accurate reference data as the accuracy of laser scanning-based growth estimation methods continues to approach the accuracy of manual measurements.

In order to reveal the spatial and temporal distribution of COVID-19's economic impact on the Beijing-Tianjin-Hebei region, this study uses the NPP/VIIRS night light remote sensing data from January to September in 2020 to compare the development trend of COVID-19 and analyze its economic impact on the Beijing-Tianjin-Hebei region. At the same time, the regional economic resilience measurement algorithm is introduced by coupling the regional night light greyscale value to obtain the economic resilience data of various cities during the epidemic. The findings show that: 1. there are structural differences in the spatial distribution of COVID-19 outbreaks in the Beijing-Tianjin-Hebei region. Beijing-Tianjin-Hebei region present a "core-adjacent-external" structure and the spatial distribution pattern of Tianjin-Beijing-Shijiazhuang prominent in the inverted "L" shape. 2. There are differences in the economic resilience of the Beijing-Tianjin-Hebei region in the face of the epidemic, with high economic resilience in the core urban areas close to Beijing and Tianjin. Therefore, strengthening regional cooperation and establishing relatively stable economic ties with surrounding areas are the key to improving the overall economic resilience of Beijing-Tianjin-Hebei region.

This study developed a random forest approach for downscaling the coarse-resolution (36 km) soil moisture measured by The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission to 1 km spatial resolution, utilizing airborne remotely sensed data (radar backscatter and radiometer retrieved soil moisture), vegetation characteristics (normalized difference vegetation index), soil properties, topography, and ground soil moisture measurements from before the launch of SMAP for training a random forest model. The 36 km SMAP soil moisture product was then downscaled by the trained model to 1 km resolution using the information from SMAP. The downscaled soil moisture was evaluated using airborne retrieved soil moisture observations and ground soil moisture measurements. Considering the airborne retrieved soil moisture as a reference, the results demonstrated that the proposed random forest model could downscale the SMAP radiometer product to 1 km resolution with a correlation coefficient of 0.97, unbiased Root Mean Square Error of 0.048 m³ m⁻³ and bias of 0.016 m³ m⁻³. Accordingly, the downscaled soil moisture captured the spatial and temporal heterogeneity and demonstrated the potential of the proposed machine learning model for soil moisture downscaling.