Search Results (545)

Phosphorus (P) availability is a major constraint on plant growth in many forest ecosystems, yet the strategies by which different tree species acquire and utilize various forms of soil phosphorus remain poorly understood. This study investigated how coexisting tree species with contrasting mycorrhizal types, specifically arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) associations, respond to different phosphorus forms under field conditions. An in situ root bag experiment was conducted using four phosphorus treatments (control, inorganic, organic, and mixed phosphorus) across four subtropical tree species. A comprehensive set of fine root traits, including morphological, physiological, and mycorrhizal characteristics, was measured to evaluate species-specific phosphorus foraging strategies. The results showed that AM species were more responsive to phosphorus form variation than ECM species, particularly under inorganic and mixed phosphorus treatments. Significant changes in root diameter (RD), root tissue density (RTD), and acid phosphatase activity (RAP) were observed in AM species, often accompanied by higher phosphorus accumulation in fine roots. For example, RD in AM species significantly decreased under the Na₃PO₄ treatment (0.94 mm) compared to the control (1.18 mm), while ECM species showed no significant changes in RD across treatments (1.12–1.18 mm, p > 0.05). RTD in AM species significantly increased under Na₃PO₄ (0.030 g/cm³) and Mixture (0.021 g/cm³) compared to the control (0.012 g/cm³, p < 0.05), whereas ECM species exhibited consistently low RTD values across treatments (0.017–0.020 g/cm³, p > 0.05). RAP in AM species increased significantly under Na₃PO₄ (1812 nmol/g/h) and Mixture (1596 nmol/g/h) relative to the control (1348 nmol/g/h), while ECM species showed limited variation (1286–1550 nmol/g/h, p > 0.05). In contrast, ECM species displayed limited trait variation across treatments, reflecting a more conservative acquisition strategy. In addition, trait correlation analysis revealed stronger coordination among root traits in AM species. And AM species exhibited high variability across treatments, while ECM species maintained consistent trait distributions with limited plasticity. These findings suggest that AM and ECM species adopt fundamentally different phosphorus acquisition strategies. AM species rely on integrated morphological and physiological responses to variable phosphorus conditions, while ECM species maintain stable trait configurations, potentially supported by fungal symbiosis. Such divergence may contribute to functional complementarity and species coexistence in phosphorus-limited subtropical forests. Full article

Snow plays a crucial role in global climate regulation, hydrological processes, and environmental change, making the accurate acquisition of snow depth data highly significant. In this study, we used Sentinel-1 radar data and employed a simulated annealing algorithm to select the optimal influencing factors from radar backscatter characteristics and spatiotemporal geographical parameters within the study area. Snow depth retrieval was subsequently performed using both random forest (RF) and Support Vector Machine (SVM) models. The retrieval results were validated against in situ measurements and compared with the long-term daily snow depth dataset of China for the period 2017–2019. The results indicate that the RF model achieves better agreement with the measured data than existing snow depth products. Specifically, in the Xinjiang region, the RF model demonstrates superior performance, with an R² of 0.92, a root mean square error (RMSE) of 2.61 cm, and a mean absolute error (MAE) of 1.42 cm. In contrast, the SVM regression model shows weaker agreement with the observations, with an R² lower than that of the existing snow depth product (0.51) in Xinjiang, and it performs poorly in other regions as well. Overall, the SVM model exhibits deficiencies in both predictive accuracy and spatial stability. This study provides a valuable reference for snow depth retrieval research based on active microwave remote sensing techniques. Full article

Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs for bathymetry estimation, including raw reflectance, log-linear, and log-ratio transforms of the raw reflectance value in WV-2 images. In addition, we investigated the contribution of each spectral band and found that utilizing all eight spectral bands in WV-2 images offers the best solution for handling complex water quality conditions. We compared CatBoostOpt with linear regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, gradient boosting, and deep convolutional neural network (DCNN). CatBoostOpt with log-ratio transformed reflectance achieved the best performance with an average root mean square error (RMSE) of 0.34 and coefficient of determination (${\mathrm{R}}^2$) of 0.87. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article

This research focused on studying the soil improvement ability provided by the roots of smooth cordgrass, Spartina alterniflora, flourishing in the dredged soil of the Sabine Refuge Marsh Creation Project in the coastal area of Louisiana, USA. Vane shear tests were conducted in the created marshland to obtain the in situ undrained shear strength of the soil vegetated with Spartina alterniflora. Direct shear tests were performed on undisturbed rooted soil samples to investigate the overall effect of the roots on soil shear strength. Laboratory tensile tests were conducted on the roots of Spartina alterniflora to estimate their tensile strength. In this research, the W&W model and the fiber bundle model (FBM), were adopted, and modified ones were proposed to study the correlation between root-enhanced soil cohesion and the nominal tensile strength of the roots. The model outcomes were compared with field and laboratory measurements. The research results showed that the roots of Spartina alterniflora significantly increased soil shear strength, with an increase in cohesion of up to 130% at one location. The increases varied at different locations depending on the root area ratio (RAR), soil sample depth, and root tensile strength. Full article

Ice thickness is a key parameter for glacier mass estimations and glacier dynamics simulations. Multiple physical models have been developed by glaciologists to estimate glacier ice thickness. However, obtaining internal and basal glacier parameters required by physical models is challenging, often leading to simplified models that struggle to capture the nonlinear characteristics of ice flow and resulting in significant uncertainties. To address this, this study proposes a convolutional neural network (CNN)-based deep learning model for glacier ice thickness estimation, named the Coordinate-Attentive Dense Glacier Ice Thickness Estimate Model (CADGITE). Based on in situ ice thickness measurements in the Swiss Alps, a CNN is designed to estimate glacier ice thickness by incorporating a new architecture that includes a Residual Coordinate Attention Block together with a Dense Connected Block, using the distance to glacier boundaries as a complement to inputs that include surface velocity, slope, and hypsometry. Taking ground-penetrating radar (GPR) measurements as a reference, the proposed model achieves a mean absolute deviation (MAD) of 24.28 m and a root mean square error (RMSE) of 37.95 m in Switzerland, outperforming mainstream physical models. When applied to 14 glaciers in High Mountain Asia, the model achieves an MAD of 20.91 m and an RMSE of 27.26 m compared to reference measurements, also exhibiting better performance than mainstream physical models. These comparisons demonstrate the good accuracy and cross-regional transferability of our approach, highlighting the potential of using deep learning-based methods for larger-scale glacier ice thickness estimation. Full article

Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately capture the local characteristics of wave energy at specific sites. This study proposes a supervised machine-learning (ML) approach to estimate long-term wave energy at locations with only short-term in situ measurements. The method involves training ML models using concurrent short-term buoy data and ERA5 reanalysis data, enabling the extension of wave energy estimates over longer periods using only reanalysis inputs. As a case study, hourly mean significant wave height and energy period data from 2000 to 2023 were analyzed, collected by a deep-water buoy off the coast of Gran Canaria (Canary Islands, Spain). Among the ML techniques evaluated, Multiple Linear Regression (MLR) and Support Vector Regression yielded the most favorable error metrics. MLR was selected due to its lower computational complexity, greater interpretability, and ease of implementation, aligning with the principle of parsimony, particularly in contexts where model transparency is essential. The MLR model achieved a mean absolute error (MAE) of 2.56 kW/m and a root mean square error (RMSE) of 4.49 kW/m, significantly outperforming the direct use of ERA5 data, which resulted in an MAE of 4.38 kW/m and an RMSE of 7.1 kW/m. These findings underscore the effectiveness of the proposed approach in enhancing long-term wave energy estimations using limited in situ data. Full article

Recent research in agricultural remote sensing mainly focuses on how soil background affects canopy reflectance and the inversion of LAI, while often overlooking the influence of the weed layer. The coexistence of crop and weed layers forms two-layered vegetation canopies in tall crops such as sugarcane and maize. Although radiative transfer models can simulate the weed layer’s influence on canopy reflectance and LAI inversion, few experimental investigations use in situ measurement data to verify these effects. Here, we propose a practical background modification scheme in which black material with near-zero reflectance covers the weed layer and alters the background spectrum of crop canopies. We conduct an experimental investigation in a sugarcane field with different background properties (i.e., bare soil and a weed layer). Tower-based and UAV-based hyperspectral measurements examine the spectral differences in sugarcane canopies with and without the black covering. We then use LAI measurements to evaluate the weed layer’s impact on LAI inversion from UAV-based hyperspectral data through a hybrid inversion method. We find that the weed layer significantly affects the canopy reflectance spectrum, changing it by 13.58% and 42.53% in the near-infrared region for tower-based and UAV-based measurements, respectively. Furthermore, the weed layer substantially interferes with LAI inversion of sugarcane canopies, causing significant overestimation. Estimated LAIs of sugarcane canopies with a soil background generally align well with measured values (root mean square error (RMSE) = 0.69 m²/m²), whereas those with a weed background are considerably overestimated (RMSE = 2.07 m²/m²). We suggest that this practical background modification scheme quantifies the weed layer’s influence on crop canopy reflectance from a measurement perspective and that the weed layer should be considered during the inversion of crop LAI. Full article

Ocean temperature and salinity are core elements influencing ocean dynamics and biogeochemical cycles, critical to climate change and ocean process studies. In recent years, Argo floats and satellite remote sensing data have provided key support for observing and reconstructing three-dimensional (3D) ocean temperature and salinity. However, due to the challenges and high costs of in situ observations and the limitation of satellite measurements to surface data, effectively combining multi-source data to enhance the reconstruction accuracy of 3D temperature and salinity remains a significant challenge. In this study, we propose a VI-UNet model that incorporates a Vision Transformer module into UNet model and apply it to reconstruct 3D temperature and salinity in the equatorial oceans (20°S–20°N, 20°E–60°W) at depths from 1 to 6000 m using sea surface data acquired by satellites. In addition, we also investigate the impact of incorporating significant wave height (SWH) on the reconstruction of temperature and salinity. The results demonstrate that the VI-UNet model performs remarkably well in reconstructing temperature and salinity, achieving maximum reductions in root mean square error (RMSE) of up to 40% and 100%, respectively. Additionally, incorporating SWH enhances model accuracy, particularly in the upper 1000 m. Full article

Based on a 2-year in situ nitrogen fertilization experiment, this study aims to establish a critical nitrogen concentration (CNC) dilution curve model for cotton under straw incorporation, analyze the effects of the nitrogen application rate on the cotton yield and nitrogen use efficiency (NUE), and determine the optimal nitrogen application rate by integrating the nitrogen nutrition index (NNI). The experiment setup was a randomized block design with five nitrogen application levels under a straw incorporation: 0, 60, 120, 180, and 240 kg N ha⁻¹ (denoted as N0, N60, N120, N180, and N240, respectively). The cotton dry matter accumulation and nitrogen concentration were measured at the flowering and boll stage, peak boll stage, and boll opening stage. The CNC dilution curve was developed using the data from 2021 and validated with those of 2022. Results showed that the cotton biomass and seed cotton yield at the boll opening stage increased with nitrogen application rates up to 180 kg N ha⁻¹. However, no further increase was found in the yield with an N rate higher than 180 kg N ha⁻¹. The CNC dilution curve was formulated as y = 3.4921x^−0.416 (R² = 0.8741). The validation using 2022 data yielded a root mean square error (RMSE) of 0.21% and a normalized RMSE (nRMSE) of 13.40%, confirming the model’s robustness. The NNI, calculated based on the CNC, indicated that an application rate of 120 kg N ha⁻¹ maintained NNI values close to one across all growth stages, reflecting an optimal nitrogen status. Significant positive correlations were observed between the NNI and both the seed cotton yield and harvest index (p < 0.05). Nitrogen use efficiency parameters, including the agronomic NUE (NUEa), nitrogen partial factor productivity (NPFP), and internal NUE (NUEi), exhibited quadratic declines with the increasing nitrogen input. Within the range of 120–240 kg N ha⁻¹, the highest NPFP was achieved at 120 kg N ha⁻¹. In conclusion, the critical nitrogen dilution curve model combined with the NNI effectively diagnoses the nitrogen status in cotton under straw incorporations. Considering the NNI, yield, and nitrogen utilization efficiency, the recommended nitrogen application rate for cotton in a cotton–rape rotation system with a straw incorporation is 120 kg N ha⁻¹. Full article

Sea surface wind retrieval methods using synthetic aperture radar (SAR) are generally classified into two categories: the direct inversion method and the variational analysis method (VAM). Traditional VAM retrieves wind fields by integrating background wind information with SAR normalized radar cross-section (NRCS). Recent studies have shown that incorporating SAR Doppler centroid anomaly (DCA) as an additional observation for variational analysis can improve the accuracy of wind speed and direction retrieval. However, this method has yet to be systematically evaluated, particularly with respect to its applicability to Sentinel-1 SAR data. This study presents a comprehensive assessment based on 1803 Sentinel-1 vertical–vertical (VV) polarization level-2 Ocean (OCN) product scenes collocated with in situ measurements from the National Data Buoy Center (NDBC), yielding a total of 2826 matched data pairs. We systematically evaluate the performance of three distinct VAM configurations: VAM1 (J_NRCS), utilizing only NRCS; VAM2 (J_DCA), employing solely DCA; and VAM3 (J_NRCS+DCA), which combines both NRCS and DCA. The results demonstrate that VAM3 (J_NRCS+DCA) achieves the best performance, with the lowest root mean square error (RMSE) of 1.42 m/s for wind speed and 26.00° for wind direction across wind speeds up to 23.2 m/s, outperforming both VAM1 (J_NRCS) and VAM2 (J_DCA). Furthermore, the accuracy of background wind speed is identified as a critical factor affecting VAM performance. After correcting the background wind speed, the RMSE and bias of the retrieved wind speed decreased significantly across all VAMs. The most notable bias reduction was observed at wind speeds exceeding 10 m/s. These findings provide essential theoretical support for the operational application of Sentinel-1 OCN products in sea surface wind retrieval. Full article

Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management. Full article

Groundwater depletion has emerged as a pressing global challenge, yet the low spatial resolution (0.25°) of Gravity Recovery and Climate Experiment (GRACE) satellite data limits its application in regional groundwater monitoring. In this study, based on 0.25° spatial resolution groundwater storage anomalies (GWSAs) data derived from GRACE satellite observations and GLDAS hydrological model outputs, supplemented with hydrological data, humanities data, and other geographic parameters, we constructed a Stacking-based ensemble machine learning model that achieved a 1 km spatial resolution of GWSAs distribution data across the contiguous United States (CONUS) from 2010 to 2020. The ensemble model integrates eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) models using an Attention-Based Dynamic Weight Allocation (ADWA) approach, along with a ridge regression model. The results indicate that our ensemble model outperforms individual machine learning (ML) models, achieving a coefficient of determination (R²) of 0.929, root mean square error (RMSE) of 25.232 mm, mean absolute error (MAE) of 19.125 mm, and Nash–Sutcliffe efficiency (NSE) of 0.936, validated by 10-fold cross-validation. In situ measurements indicate that, compared with the original data, approximately 61.7% of the monitoring wells (266 out of 431) exhibit a higher correlation after downscaling, with the overall correlation coefficient increasing by about 18.7%, which suggests that the downscaled product exhibits an appreciable improvement in accuracy. The ensemble model proposed in this study, by integrating the advantages of various ML algorithms, is better able to address the complexity and uncertainty of groundwater storage variations, thus providing scientific support for the sustainable management of groundwater resources. Full article

Lake Kasumigaura, one of Japan’s largest lakes, presents significant challenges for remote sensing due to its eutrophic conditions and complex optical properties. Although the Global Change Observation Mission-Climate (GCOM-C)/Second-generation Global Imager (SGLI)-derived inherent optical properties (IOPs) offer water quality monitoring potential, their performance in such turbid inland waters remains inadequately validated. This study evaluated five established IOP retrieval algorithms, including the quasi-analytical algorithm (QAA_V6), Garver–Siegel–Maritorena (GSM), generalized IOP (GIOP-DC), Plymouth Marine Laboratory (PML), and linear matrix inversion (LMI), using measured remote sensing reflectance (Rrs) and corresponding IOPs between 2017–2018. The results demonstrated that the QAA had the highest performance for retrieving absorption of particles (a_p) with a Pearson correlation (r) = 0.98, phytoplankton (a_ph) with r = 0.97, and non-algal particles (a_nap) with r = 0.85. In contrast, the GSM algorithm exhibited the best accuracy for estimating absorption by colored dissolved organic matter (a_CDOM), with r = 0.87, along with the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE). Additionally, a strong correlation (r = 0.81) was observed between SGLI satellite-derived remote-sensing reflectance (R_rs) and in situ measurements. Notably, a high correlation was observed between the a_ph (443 nm) and the chlorophyll a (Chl-a) concentration (r = 0.84), as well as between the backscattering coefficient (b_bp) at 443 nm and inorganic suspended solids (r = 0.64), confirming that IOPs are reliable water quality assessment indicators. Furthermore, the use of IOPs as variables for estimating water quality parameters such as Chl-a and suspended solids showed better performance compared to empirical methods. Full article