Remote Sensing of Environment最新文献

Achieving the Global Climate Observing System goal of 10 m resolution leaf area index (LAI) maps is critical for applications related to climate adaptation, sustainable agriculture, and ecosystem monitoring. Five strategies for producing 10 m LAI maps from Sentinel-2 (S2) imagery are evaluated: i. bi-cubic interpolation of 20 m resolution S2 LAI maps from the Simplified Level 2 Prototype Processor Version 1 (SL2PV1) as currently performed by the Sentinel Applications Platform (SNAP), ii. applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using bi-cubic interpolation (BICUBIC), iii. Applying SL2PV1 to S2 reflectance bands spatially downscaled to 10 m using Area to Point Regression Kriging (ATPRK), iv. using a recalibrated version of SL2PV1 (SL2PV2) requiring only three S2 10m bands, and iv) a novel use of the previously developed Active Learning Regularization (ALR) approach to locally approximate the SL2PV1 algorithm using only 10 m bands.

Algorithms were assessed in terms of per-pixel accuracy and spatial metrics when comparing 10 m LAI maps produced using either actual S2 imagery or S2 imagery synthesized from airborne hyperspectral imagery to reference 10 m LAI maps traceable to in-situ fiducial reference measurements at 10 sites across the continental US. ATPRK and ALR algorithms had the lowest precision error of ∼0.15 LAI, compared to 0.19 LAI for SNAP and BICUBIC and 0.35 LAI for SL2PV2, and ranked highest in terms of local correlation and Structural Similarity Index measure as well as qualitative agreement with reference maps. SL2PV2 LAI showed evidence of saturation over forests related to decreased sensitivity of input visible reflectance. All algorithms had a similar uncertainty of ∼0.55 LAI compared to traceable reference maps, due to the trade-off between bias and precision. However, ATPRK and ALR uncertainty reduced to 0.11 LAI and 0.16 LAI, respectively, when compared to reference maps that ignored canopy clumping. These results suggest that both ATPRK and ALR are suitable for producing 10 m S2 LAI maps assuming bias due to local clumping can be corrected in the underlying SL2PV1 algorithm.

Shoreline variations, triggered by climate change, eustatism, and tectonic, drive the coastal landscape evolution over multiple spatial and temporal scales. Among the many different existing coast types, sandy coasts are the most sensitive to coastal erosion and accretion processes and, at the same time, often host valuable anthropogenic assets. The rapid and ongoing evolution of these coastal environments poses challenges for their management, necessitating cost-effective and highly reliable methods for measuring these changes. Many remotely sensed shoreline extraction methods have been proposed in the literature, providing valuable tools for improving coastal management. Even if these methodologies allow the demarcation of the shoreline, its pixelated shape usually requires refinement through subsequent smoothing or vector generalization processes. It is important to note that the position of the thus extracted coastline is not a direct result of a measured physical quantity but rather a product of these refinement techniques. To address this problem, we developed a sub-pixel resolution method for extracting shorelines from remotely sensed images of sandy beaches, leveraging the radiometric signature of the shoreline. Validated through precise Global Navigation Satellite System field surveys for positioning the beach foreshore, this method was successfully applied to three beaches in Sicily, in the central Mediterranean, all exhibiting similar microtidal conditions. Its robust design allows for application across various satellite images, employing a straightforward radiometric interpolation method adaptable to different spatial resolutions. This method would be a valuable tool for coastal managers in detecting and mitigating coastal erosion and developing and maintaining anthropogenic coastal assets.

The Photochemical Reflectance Index (PRI) and solar induced fluorescence (SIF) provide information on plant photosynthetic activity. PRI and SIF are both strongly influenced by irradiance, but uncertainties related to the interpretation of these light responses at large spatial scales remain, partly due to a shortage of suitable data from aircraft or satellite platforms. The goal of this study was to explore interpretations of the PRI- and SIF-light responses of trees owing to species, functional types (evergreen and deciduous) and season. Using airborne hyperspectral and ultraspectral imagery in a North American urban forest, we derived PRI, SIF, and albedo (an indicator of illumination) at the 1-m pixel level. We then quantified crown-level PRI and SIF light responses of ten different tree species at three time points from late-summer to autumnal senescence using hierarchical models. Our results confirmed that both PRI and SIF were strongly influenced by illumination with PRI decreasing and SIF increasing with illumination. Both slope and intercept of the PRI-albedo relationship changed with season, but the pattern varied among species and functional types. SIF values decreased during autumnal senescence for all species, but evergreen species exhibited less seasonal decline in the slope of SIF-albedo relationship compared to deciduous species. The PRI and SIF light responses derived from the airborne imagery offer complementary information on dynamic photosynthesis responses presumably due to varying canopy structure, pigmentation and photoprotection among species and functional types. From airborne platforms, PRI- and SIF-light responses can be used to explore the contrasting physiological responses of individual tree crowns, providing a spatially and temporally explicit view of dynamic plant traits related to photoregulation and a novel view of functional diversity for entire landscapes.

It is important to know the amount of systematic and random uncertainties in any state variable to improve its geophysical application potential. The expected high-resolution (200 [m]) soil moisture product from the NASA-ISRO Synthetic Aperture Radar (NISAR) mission is no exception. Thus, knowing the quality of the soil moisture retrievals through the estimation of various error sources is imperative. The estimation error sources in soil moisture retrievals can be obtained by various methods. In situ measurements provide a reliable estimate of the uncertainty of soil moisture retrievals. However, in situ measurements are available only for limited locations, as they are typically very tedious and expensive to obtain. Thus, an analytical approach has been developed to obtain an estimate of the uncertainty in the soil moisture retrievals that vary in space and time across grid-cells. This uncertainty estimation is specifically developed for the multi-scale algorithm of the upcoming NISAR mission, which will provide soil moisture retrievals at 200 [m] resolution. The multi-scale algorithm for the NISAR mission disaggregates the coarser resolution soil moisture (∼9 [km]) to high-resolution (∼200 [m]) using NISAR L-band SAR measurements. However, uncertainty in high-resolution soil moisture retrievals might be introduced due to errors in input datasets (e.g., coarse resolution soil moisture, instrument error of SAR, etc.) and multi-scale algorithm parameters. Therefore, this study carried out a detailed sensitivity analysis of input datasets and algorithm parameters using the proposed approach. The sensitivity analysis shows that error in the input coarse resolution soil moisture is one of the primary drivers of uncertainty in the high-resolution soil moisture retrievals. The other portion of the uncertainty comes from errors in the algorithm parameters, and noise in SAR co-pol and cross-pol backscatter observations. Furthermore, the approach was tested on the UAVSAR L-band data time-series that had been simulated to closely match the expected characteristics of NISAR (e.g., spatial resolution and noise). The uncertainty estimates in UAVSAR-based high-resolution retrievals were compared with the SMAPVEX-12 in situ measurements. The uncertainties estimated for different crops were found to be close to the ubRMSE metric, which is also lower than the NISAR mission accuracy goal (0.06 [m³/m³]).

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations. Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and quantified the impacts of using different plot-level trait metrics on the evaluation with sPlotOpen: community weighted mean (CWM) and top-of-canopy weighted mean (TWM). We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the use of PFT information combined with remote sensing-derived land cover products in one group while the other group mostly relied on environmental predictors alone. The maps that used PFT and corresponding land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables. Upscaled maps of both groups were moderately correlated to sPlotOpen data aggregated to the grid-cell level (R = 0.2–0.6) when processing sPlotOpen in a way that is consistent with the respective trait upscaling approaches, including the plot-level trait metric (CWM or TWM) and the scaling to the grid cells with or without accounting for fractional land cover. The impact of using TWM or CWM was relevant, but considerably smaller than that of the PFT and land cover information. The maps using PFT and land cover information better reproduce the between-PFT trait differences of sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation.

Our findings highlight the importance of explicitly accounting for within-grid-cell trait variation, which has important implications for applications using existing maps and future upscaling efforts. Remote sensing information has great potential to reduce uncertainties related to scaling from in-situ observations to grid cells and the regression-based mapping steps involved in the upscaling.

Intermittent surface water frequently transitioning between water and land over months and years, plays a crucial and increasingly significant role in both social and ecological systems. However, their vital and dramatic dynamics have mainly remained invisible due to monitoring limitations. We present a new remote sensing framework to capture the long-term monthly dynamics of surface water bodies, applying it to Poyang Lake, the largest freshwater lake in China. This framework employed a random forest classifier on all available Landsat data to identify monthly surface water bodies. Additionally, we developed a Spatial and Temporal Neighborhood Similarity-based Gap Filling method to restore water bodies obscured by clouds and ensure spatial integrity. Furthermore, we introduced an index to quantify the intermittency of surface water bodies on a scale from 0 to 1, allowing for the classification of water bodies into three categories: perennial, wet intermittent, and dry intermittent. Employing this framework, we reconstructed the most complete monthly 30-m surface water dataset for cloudy regions to date, covering April 1986 to September 2023, demonstrating a strong correlation (Spearman's rank correlation coefficient of 0.909) with observed water levels. The results reveal a landscape dominantly composed of intermittent water bodies (91.2%), with a rapidly shrinking trend of perennial water bodies at 1303.58 ha per year. Notably, 162,685 ha (21.9%) of water bodies transitioned toward drier and more intermittent statuses. Dry intermittent water bodies exhibited the most pronounced land-water transitions, with the highest water-to-land (82.5%) and land-to-water (89.9%) proportions among the three categories. By uncovering the hidden dynamics of intermittent surface water, and highlighting its prevalence, expansion, and vulnerability, this framework paves the way for a better understanding of these critical water dynamics across the globe.

Marine ecosystems are complex and dynamic in nature and influenced by various environmental factors such as temperature, salinity, ocean currents, nutrient availability, light penetration, and anthropogenic activities. Macronutrients (nitrate, phosphate, and silicate) and dissolved oxygen (DO) are crucial properties for determining the health, function, and dynamics of marine ecosystems. There are known limitations with the in-situ measurements that emphasize the importance of satellite-based models for estimating these properties on the required space and time scales. In this study, we present a number of robust Gaussian Process Regression (GPR) models comprising of 16 DO models and 24 macronutrients models for estimating the concentrations of global-scale ocean surface DO and macronutrients. These models were rigorously trained and tested using the large in-situ datasets. Model performance was assessed using independent in-situ data and it was found that the proposed models yielded high accuracies (Root Mean Square Difference (RMSD) in μmol kg⁻¹, Mean Absolute Difference (MAD) in μmol kg⁻¹, and coefficient of determination (R²)): DO: 8.276, 3.802, and 0.984; Nitrate: 0.827, 0.329, and 0.987; Phosphate: 0.068, 0.034, and 0.983; and Silicate: 1.921, 0.757, and 0.982. The optimal input parameters and kernel combinations for GPR models were identified as (i) sea surface temperature (SST), sea surface salinity (SSS), and latitude/longitude for DO, and (ii) SST, SSS, DO, and latitude/longitude for macronutrients. The satellite estimates based on the exponential kernel functions showed good agreement with in-situ data (RMSD, MAD, R², Slope, and Intercept: 9.794, 4.850, 0.948, 0.986, and 4.206 for the DO products, 1.711, 0.652, 0.824, 0.884, and 0.249 for the nitrate products, 0.127, 0.064, 0.805, 0.869, and 0.033 for the phosphate products, and 2.809, 1.067, 0.533, 0.622, and 1.117 for the silicate products). Further tests on World Ocean Atlas (WOA) 2018 SST and SSS data yielded similar results for the DO and macronutrients contents. To realize the importance of this study, we investigated the early and substantial spring bloom occurrences in the Gulf of Alaska in response to the DO and macronutrients contents as well as the monthly and interannual variations and anomalies of SST, SSS, DO, nitrate, phosphate, and silicate caused by the Pacific Decadal Oscillation (PDO) in the California Current System (CCS) and Oceanic Niño Index (ONI) in the Niño-3.4 region using climatological data (2002−2023). The proposed models will have important implications for remote sensing of regional and global biogeochemical properties and marine ecosystem dynamics.

With dedicated coastal processing strategies and advanced Delay-Doppler technique, the quality of altimeter data from Low-Resolution Mode (LRM) and Synthetic Aperture Radar (SAR) mode altimeters in coastal areas have been greatly improved. In this study, we present a new 20-Hz along-track sea level anomaly (SLA) dataset of Jason-3 within 100 km to the global coastlines using the modified SCMR (Seamless Combination of Multiple Retrackers) processing strategy. The new reprocessed Jason-3 dataset, along with Sentinel-6 Michael Freilich (MF) SAR mode data, are evaluated and validated over global coastal oceans. The evaluation results show that the modified SCMR has significantly increases the data availability by 16%–67% for Jason-3 when compared to the official SGDR MLE4 dataset, especially in the last 5 km to the coast. The resultant data availability retains >90% beyond 5 km to the coast and 80% within 5 km to the coast, which is slightly higher (2%–10%) than that obtained by the Sentinel-6 MF. Most importantly, the modified SCMR mitigates the hump artifacts observed for the SLA spectrums of LRM altimeters, which makes the noise level of 20-Hz SLA estimates from Jason-3 (5.52 cm) comparable with that from Sentinel-6 MF (5.42 cm). This result demonstrates that the modified SCMR strategy would improve the LRM altimeters' capability of monitoring the mesoscale eddies. The evaluation results also show that the Sentinel-6 MF SAR mode data obtain higher data precision but lower data availability than reprocessed Jason-3 LRM data, especially in the 0–5 km coastal strip and mid-to-high latitude (>40°N or < 40°S) regions. Although the quality of altimeter data in the 0–5 km coastal strip has been significantly improved, the validation results against tide gauges demonstrate that the degraded performance still occurs when compared to the results beyond 5 km offshore. The significant discrepancy between tide gauge records and altimeter data is found in places such as sheltered bays and archipelagos where the land contamination is severe, and thus the development of dedicated coastal retrackers and corrections for SAR mode altimeters is still of great importance. Finally, the good consistency between reprocessed Jason-3 LRM and Sentinel-6 MF SAR mode altimeter datasets has been found by examining the inter-mission SLA biases (−0.12 ± 0.01 m).

Sun-Induced chlorophyll Fluorescence (SIF) is the most promising remote sensing signal to monitor photosynthesis in space and time. However, under stress conditions its interpretation is often complicated by factors such as light absorption and plant morphological and physiological adaptations. To ultimately derive the quantum yield of fluorescence (Φ_F) at the photosystem from canopy measurements, the so-called escape probability (f_esc) needs to be accounted for.

In this study, we aim to compare Φ_F measured at leaf- and canopy-scale to evaluate the influence of stress responses on the two signals based on a potato mesocosm heat-drought experiment. First, we compared the performance of recently proposed reflectance-based approaches to estimate leaf and canopy red f_esc using data-supported simulations of the radiative transfer model SCOPE. While the leaf red f_esc showed a strong correlation (r² ≥ 0.76), the canopy red f_esc exhibited no relationship with the SCOPE retrieved red f_esc in our experiment. We therefore propose modifications to the canopy model to address this limitation.

We then used the modified models of red f_esc, along with an existing model for far-red f_esc to analyse the dynamics of leaf and canopy red and far-red fluorescence under increasing drought and heat stress conditions. By incorporating f_esc, we obtained a closer agreement between leaf and canopy measurements. Specifically, for red f_esc, the r² of the two variables increased from 0.3 to 0.50, and for far-red f_esc, from 0.36 to 0.48.

When comparing the dynamics of the quantum yield of red and far-red fluorescence (Φ_F,687 and Φ_F,760) under increasing stress, we observed a statistically significant decrease of both leaf and canopy Φ_F,687 as well as leaf Φ_F,760, as drought and heat conditions intensified. Canopy Φ_F,760, on the contrary, did not exhibit the same trend, since measurements under low stress conditions showed a wider spread and lower median than under high stress conditions. Finally, we analysed the sensitivity of Φ_F,687 and Φ_F,760 to changing solar incidence angle, by comparing the variability of the measurements without and with mesocosm rotation. Our results suggest that the variation in Φ_F,760 strongly increased with changing solar incidence angle. These findings highlight the need for further research to understand the causes of discrepancies between leaf and canopy scale Φ_F,760. On the contrary, the underutilised and understudied Φ_F,687 showed great potential in assessing