Search Results (3,806)

This study investigates the complex interaction of biophysical and meteorological factors that drive evapotranspiration (ET) in saline environments. Leveraging a total of 182 cloud-free Landsat 5/8 time-series data from 1988 to 2019, we employed the Surface Energy Balance System (SEBS) model to quantify ET and investigate its relationships with soil salinity, vegetation cover, groundwater depth, and landscape metrics. We validated the predicted ET at two experimental sites using ET observation calculated by a water balance model. The result shows an R² of 0.78 and RMSE of 0.91 mm for the SEBS predicted ET, indicating high accuracy of the ET estimation. We detected abandoned saline farmland patches across Hetao and extracted the normalized difference vegetation index (NDVI), salinization index (SI), and the predicted ET for analysis. The results indicate that ET is negatively correlated with SI with a Pearson correlation coefficient (r) up to −0.7, while ET is positively correlated with NDVI (r = 0.4). In addition, we designed a control-variable experiment in the Yichang subdistrict to investigate the effects of groundwater depth, land aggregation index, soil salinity index, and the area of abandoned saline farmland patches on ET. The results indicate that increased NDVI could significantly enhance ET, while smaller saline farmland patches exhibited greater sensitivity to groundwater recharge, with higher averaged ET than larger patches. Moreover, we analyzed factor importance using Lasso regression and Random Forest (RF) regression. The result shows that the ranking of the importance of the features is consistent for both methods and for all the features, with NDVI being the most important (with an RF importance score of 0.4), followed by groundwater table depth (GWTD), and the influence of the surface area of abandoned saline farmland being the weakest. We found that smaller patches of abandoned saline farmland were more sensitive to changes in groundwater levels induced by nearby irrigation, affecting their averaged ET more dynamically than larger patches. Decreasing patch size over time indicates ongoing changes in land management and ecological conditions. This study, through a multifactor analysis of ET in abandoned saline farmland and its intrinsic factors, provides a reference for evaluating the dry drainage efficiency of abandoned saline farmland in a dry drainage system. Full article

Urbanization has greatly altered Earth’s surface form, and land use changes can lead to significant changes in carbon emissions. However, how these changes affect ecosystems remains unclear. Therefore, this study calculated the carbon absorption and emissions in 31 Chinese provinces using high-resolution (300 m) land use data. Subsequently, a carbon flow model was used to evaluate the carbon transfer that occurred from the changes in land use in every province between 2000 and 2020. The standard deviation ellipse analytic techniques were also employed to research the spatiotemporal evolution features of carbon flow in various economic zones. Furthermore, the flux and utility analysis approaches in ecological network analysis were used to quantitatively examine the interaction relationship between two carbon metabolism land uses. The results revealed that the continuous expansion of China’s construction land has reduced the area of agricultural land, resulting in industrial land (53.14%) and urban land (39.38%) being the main contributors to the total carbon emissions. Among them, the five eastern provinces of Hebei, Jiangsu, Zhejiang, Shandong, and Guangdong had carbon emissions of more than 100 million tons. From 2000 to 2020, the center of gravity of the carbon flow in construction land had shifted significantly from Henan Province to Gansu Province. The ecological relationship of exploitation and control dominated the two land use types. It is mostly found in Xinjiang, Qinghai, Gansu, Inner Mongolia, and Ningxia provinces. The findings could provide relevant policy implications for the Chinese government to mitigate carbon metabolism on land. Full article

With global climate change causing temperature increases, even cooler regions like Finland are facing increasing heat risks. The city of Lahti is expected to experience a higher-than-average temperature increase, making heat risk mitigation essential. This study aims to assess present and future heat risks in Lahti using exposure and social vulnerability indicators to identify heat risk hotspots and provide strategies for mitigation within the city’s urban planning framework. The method utilizes a combination of Land Surface Temperature (LST) data (2014–2024), climate projections, and microclimate analysis to identify heat risk in the city. Geographic Information Systems (GIS) and ENVI-met modeling were employed to assess the relationship between land surface temperatures (LST), urban structure, and green infrastructure. Risk assessments were conducted using social and environmental vulnerability indicators, and future projections were based on a combined SSP2-4.5 scenario. The results show a significant increase in high-risk areas by 2040, rising from 9.79% to 23.65% of Lahti’s core urban area. Although the current urban planning framework of the city (Masterplan 2035) is effective in terms of maintaining exposure levels, the continued increase in projected air temperatures, as modeled based on outputs of the EC-Earth3-veg GCM, remains a concern. Microclimate modeling confirmed that urban greenery significantly reduces heat stress and improves thermal comfort. To address future heat risks, Lahti must integrate more green infrastructure into its urban design and identify seasonal heat mitigation methodologies. Additionally, the findings emphasize the need for adaptive planning strategies to mitigate rising temperatures and ensure urban resilience. Full article

The carbon sink over land plays a key role in the mitigation of climate change by removing carbon dioxide (CO₂) from the atmosphere. Accurately assessing the land sink capacity across regions should contribute to better future climate projections and help guide the mitigation of global emissions towards the Paris Agreement. This study estimates terrestrial CO₂ fluxes over India using a high-resolution global inverse model that assimilates surface observations from the global observation network and the Indian subcontinent, airborne sampling from Brazil, and data from the Greenhouse gas Observing SATellite (GOSAT) satellite. The inverse model optimizes terrestrial biosphere fluxes and ocean-atmosphere CO₂ exchanges independently, and it obtains CO₂ fluxes over large land and ocean regions that are comparable to a multi-model estimate from a previous model intercomparison study. The sensitivity of optimized fluxes to the weights of the GOSAT satellite data and regional surface station data in the inverse calculations is also examined. It was found that the carbon sink over the South Asian region is reduced when the weight of the GOSAT data is reduced along with a stricter data filtering. Over India, our result shows a carbon sink of 0.040 ± 0.133 PgC yr⁻¹ using both GOSAT and global surface data, while the sink increases to 0.147 ± 0.094 PgC yr⁻¹ by adding data from the Indian subcontinent. This demonstrates that surface observations from the Indian subcontinent provide a significant additional constraint on the flux estimates, suggesting an increased sink over the region. Thus, this study highlights the importance of Indian sub-continental measurements in estimating the terrestrial CO₂ fluxes over India. Additionally, the findings suggest that obtaining robust estimates solely using the GOSAT satellite data could be challenging since the GOSAT satellite data yield significantly varies over seasons, particularly with increased rain and cloud frequency. Full article

Surface properties in complex urban environments can significantly impact local-level temperature gradients and distribution on several scales. Studying temperature anomalies and identifying heat pockets in urban settings is challenging. Limited high-resolution datasets are available that do not translate into an accurate assessment of near-surface temperature. This study developed a model to predict land surface temperature (LST) at a high spatial–temporal resolution in urban areas using Landsat data and meteorological inputs from NLDAS. This study developed an urban microclimate (UC) model to predict air temperature at high spatial–temporal resolution for inner urban areas through a land surface and build-up scheme. The innovative aspect of the model is the inclusion of micro-features in land use characteristics, which incorporate surface types, urban vegetation, building density and heights, short wave radiation, and relative humidity. Statistical models, including the Generalized Additive Model (GAM) and spatial autoregression (SAR), were developed to predict land surface temperature (LST) based on surface characteristics and weather parameters. The model was applied to urban microclimates in densely populated regions, focusing on Manhattan and New York City. The results indicated that the SAR model performed better (R² = 0.85, RMSE = 0.736) in predicting micro-scale LST variations compared to the GAM (R² = 0.39, RMSE = 1.203) and validated the accuracy of the LST prediction model with R² ranging from 0.79 to 0.95. Full article

Over the course of NASA’s Dawn Discovery mission, the onboard framing camera mapped Ceres across a wide wavelength spectrum at varying polar science orbits and altitudes. With increasing resolution, the uniqueness of the 92 km wide, young Occator crater became evident. Its central cryovolcanic dome, Cerealia Tholus, and especially the associated bright carbonate and ammonium chloride deposits—named Cerealia Facula and the thinner, more dispersed Vinalia Faculae—are the surface expressions of a deep brine reservoir beneath Occator. Understandably, this made this crater the target for future sample return mission studies. The planning and preparation for this kind of mission require the characterization of potential landing sites based on the most accurate topography and orthorectified image data. In this work, we demonstrate the capabilities of the freely available and open-source USGS Integrated Software for Imagers and Spectrometers (ISIS 3) and Ames Stereo Pipeline (ASP 2.7) in creating high-quality image data products as well as stereophotogrammetric (SPG) and multi-view shape-from-shading (SfS) digital terrain models (DTMs) of the aforementioned spectroscopically challenging features. The main data products of our work are four new DTMs, including one SPG and one SfS DTM based on High-Altitude Mapping Orbit (HAMO) (CSH/CXJ) and one SPG and one SfS DTM based on Low-Altitude Mapping Orbit (LAMO) (CSL/CXL), along with selected Extended Mission Orbit 7 (XMO7) framing camera (FC) data. The SPG and SfS DTMs were calculated to a GSD of 1 and 0.5 px, corresponding to 136 m (HAMO SPG), 68 m (HAMO SfS), 34 m (LAMO SPG), and 17 m (LAMO SfS). Finally, we show that the SPG and SfS approaches we used yield consistent results even in the presence of high albedo differences and highlight how our new DTMs differ from those previously created and published by the German Aerospace Center (DLR) and the Jet Propulsion Laboratory (JPL). Full article

It is important to carry out timely scientific assessments of surface subsidence in coal resource cities for ecological environmental protection. Traditional subsidence simulation methods cannot quantitatively describe the driving factors that contribute to or ignore the dynamic connections of subsidence across time and space. Thus, a novel spatio-temporal subsidence simulation model is proposed that couples random forest (RF) and cellular automaton (CA) models, which are used to quantify the contributions of driving factors and simulate the spatio-temporal dynamic changes in subsidence. The RF algorithm is first utilized to clarify the contributions of the driving factors to subsidence and to formulate transformation rules for simulation. Then, a spatio-temporal simulation of subsidence is accomplished by combining it with the CA model. Finally, the method is validated based on the Yongcheng coalfield. The results show that the depth–thickness ratio (0.242), distance to the working face (0.159), distance to buildings (0.150), and lithology (0.147) play main roles in the development of subsidence. Meanwhile, the model can effectively simulate the spatio-temporal changes in mining subsidence. The simulation results were evaluated using 2021 subsidence data as the basis data; the simulation’s overall accuracy (OA) was 0.83, and the Kappa coefficient (KC) was 0.71. This method can obtain a more realistic representation of the spatio-temporal distribution of subsidence while considering the driving factors, which provides technological support for land-use planning and ecological and environmental protection in coal resource cities. Full article

In recent years, Vision-Based Navigation (VBN) techniques have emerged as a fundamental component to enable autonomous spacecraft operations, particularly in challenging environments such as planetary landings, where ground control may be limited or unavailable. Developing and testing VBN algorithms requires the availability of a large number of realistic images of the application scenario; however, these are rarely available. This paper presents a novel rendering software tool to generate accurate synthetic optical images of the lunar surface by leveraging high-resolution Digital Terrain Models (DTMs). Unlike traditional ray-tracing algorithms, the method iteratively propagates camera rays to determine their intersection with the terrain surface defined by a Digital Elevation Model (DEM). The color information is then retrieved from the corresponding Digital Orthophoto Model (DOM) through the knowledge of the ray impact points, bypassing the need for the costly computation of shadows, reflections, and refractions effects. The rendering performance is demonstrated through a comprehensive selection of images of the lunar surface under different illumination conditions and camera orientations. Full article

Soil degradation and erosion pose significant threats to agricultural sustainability in fragile ecosystems, such as the Loess Plateau in northern China. This study examines the long-term impacts of fertilization regimes and land-use systems on soil health, focusing on soil aggregate stability, fertility, and crop productivity. Six treatment combinations were evaluated in our study, including three continuous alfalfa fields (AL-CK, AL-P, and AL-NPM) and three continuous wheat fields (WH-NPM, WH-NP, and WH-P), each representing a combination of land use and three fertilization treatments: (1) no fertilization (CK), (2) inorganic fertilization (120 kg ha⁻¹ N, 60 kg ha⁻¹ P-NP), and (3) a combination of organic and inorganic fertilization (75 t ha⁻¹ cow manure-NPM). Soil samples were collected from three depths (0–10 cm, 10–20 cm, and 20–30 cm) to assess physical and chemical properties. We evaluated the long-term effects of different fertilization treatments on soil stability, fertility, and crop yield to explore the interactions among soil’s physical and chemical properties under two land-use types and to assess the effectiveness of combined organic and inorganic fertilization strategies in improving soil health and mitigating erosion in vulnerable landscapes. The study revealed significant depth-specific variations with surface layers (0–10 cm) showing the greatest improvement under NPM treatments, particularly in continuous alfalfa fields, which exhibited higher soil fertility, improved soil structure, and crop yield. In contrast, continuous wheat fields with minimal fertilization demonstrated significantly lower soil quality and productivity. Using the combination of mineral fertilizers and organic amendments, such as cow manure, proved to be the most effective strategy for significantly enhancing nutrient availability and overall soil health. Partial Least Squares Modeling (PLS-M) and Mantel analysis highlighted the critical role of fertilization management in maintaining soil quality, boosting crop productivity, and mitigating erosion in high-risk areas. This study emphasizes the importance of integrated nutrient management for sustainable land use and soil conservation in erosion-prone regions. Full article

Differences in exposures and resources to manage personal health contribute to persistent inequities in air pollution burden despite vast air quality improvements over the past 2–3 decades in the United States. These factors are, partly, linked to historic racist practices, such as redlining, a discriminatory housing policy that was practiced legally between 1935 and 1968. Using 100 m × 100 m resolution land-use regression predicted surfaces of PM_2.5 constituents (black carbon, nickel, vanadium, and copper) as pollution source indicators, we fit Bayesian generalized linear mixed-effects models to examine differences in source exposures over two study periods, 2008–2015 and 2016–2019, comparing (1) redlined to not redlined and (2) high-asthma to low-asthma neighborhoods. We examine redlining as an indicator of historical, and structural racism and asthma rates as an indicator of present-day community burden. Redlined areas saw near elimination of disparities in exposure to residual oil boilers and marine residual oil but persistent disparities in traffic. High-asthma neighborhoods continue to have disproportionately high exposures to both residual oil boilers and traffic, with no discernable disparities related to marine residual oil emissions. Overall exposure disparities are small, with PM_2.5 disparities by both asthma morbidity and redlining amounting to less than 1 µg/m³ and NO₂ disparities by asthma and redlining amounting to less than 2 ppb in the post-2016 period. For context, 2019 NYC average PM_2.5 and NO₂ were 8.5 µg/m³ and 20 ppb, respectively. Our findings suggest that local pollution policy should focus on reducing traffic and building boiler emissions in high-asthma neighborhoods to reduce exacerbations. Full article

The accurate modeling of complex freeze–thaw processes and hydrothermal dynamics within the active layer is challenging. Due to the uncertainty in hydrothermal simulation, it is necessary to thoroughly investigate the parameterization schemes in land surface models. The Noah-MP was utilized in this study to conduct 23,040 ensemble experiments based on 11 physical processes, which were aimed at improving the understanding of parameterization schemes and reducing model uncertainty. Next, the impacts of uncertainty of physical processes on land surface modeling were evaluated via Natural Selection and Tukey’s test. Finally, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to identify the optimal combination of parameterization schemes for improving hydrothermal simulation. The results of Tukey’s test agreed well with those of Natural Selection for most soil layers. More importantly, Tukey’s test identified more parameterization schemes with consistent model performance for both soil temperature and moisture. Results from TOPSIS showed that the determination of optimal schemes was consistent for the simulation of soil temperature and moisture in each physical process except for frozen soil permeability (INF). Further analysis showed that scheme 2 of INF yielded better simulation results than scheme 1. The improvement of the optimal scheme combination during the frozen period was more significant than that during the thawed period. Full article

Variations in vegetation indices derived from multispectral images and digital terrain models from satellite imagery have been successfully used for reclamation and hazard management in former mining areas. However, low spatial resolution and the lack of sufficiently detailed information on surface morphology have restricted such studies to large sites. This study investigates the application of small, unmanned aerial vehicles (UAVs) equipped with multispectral sensors for land cover classification and vegetation monitoring. The application of UAVs bridges the gap between large-scale satellite remote sensing techniques and terrestrial surveys. Photogrammetric terrain models and orthoimages (RGB and multispectral) obtained from repeated mapping flights between November 2023 and May 2024 were combined with an ALS-based reference terrain model for object-based image classification. The collected data enabled differentiation between natural forests and areas affected by former mining activities, as well as the identification of variations in vegetation density and growth rates on former mining areas. The results confirm that small UAVs provide a versatile and efficient platform for classifying and monitoring mining areas and forested landslides. Full article

Quantifying and mapping evapotranspiration (ET) from land surfaces is crucial in the context of climate change. For decades, remote sensing data have been utilized to estimate ET, leading to the development of numerous algorithms. However, their application is still non-trivial, mainly due to practical constraints. This paper introduces geeSSEBI, a Google Earth Engine implementation of the S-SEBI (Simplified Surface Energy Balance Index) model, for deriving ET from Landsat data and ERA5-land radiation. The source code and a graphical user interface implemented as a Google Earth Engine application are provided. The model ran on 871 images, and the estimates were evaluated against multiyear data of four eddy covariance towers belonging to the ICOS network, representative of both forests and agricultural landscapes. The model showed an RMSE of approximately 1 mm/day, and a significant correlation with the observed values, at all the sites. A procedure to upscale the data to monthly is proposed and tested as well, and its accuracy evaluated. Overall, the model showed acceptable accuracy, while performing better on forest ecosystems than on agricultural ones, especially at daily and monthly timescales. This implementation is particularly valuable for mapping evapotranspiration in data-scarce environments by utilizing Landsat archives and ERA5-land radiation estimates. Full article

Northeast China, as a primary grain-producing region, has long drawn attention for its intensive groundwater extraction for irrigation. However, previous studies on the future spatiotemporal changes of groundwater storage (GWS) are lacking. Utilizing the Global Land Data Assimilation System Version 2.2 (GLDAS-2.2), which simulates groundwater storage (as Equivalent Water Height) using the Catchment Land Surface Model (CLSM-F2.5) and calibrates it with terrestrial water storage data from the GRACE satellite, we analyzed the spatiotemporal variations of GWS in northeast China and employed a Long Short-Term Memory (LSTM) neural network model to quantify the responses of GWS to future climate change. Maintaining current socio–economic factors and combining climate factors from four scenarios (SSP126, SSP245, SSP370, and SSP585) under the CMIP6 model, we predicted GWS from 2022 to 2100. The results indicate that historically, groundwater storage exhibits a decreasing trend in the south and an increasing trend in the north, with a 44° N latitude boundary. Under the four scenarios, the predicted GWS increments in northeast China are 0.08 ± 0.09 mm/yr in SSP126, 0.11 ± 0.08 mm/yr in SSP245, 0.12 ± 0.09 mm/yr in SSP370, and 0.20 ± 0.07 mm/yr in SSP585. Although overall groundwater storage has slightly increased and the model projections indicate a continued increase, the southern part of the region may not return to past levels and faces water stress risks. This study provides an important reference for the development of sustainable groundwater management strategies. Full article

Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest monitoring and crop type identification, among others. To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. Unfortunately, RS images acquired by optical sensors are frequently degraded by aerosol contamination, clouds and cloud shadows, resulting in missing observations and irregular observation patterns. To address these issues, efforts have been made to implement frameworks that generate meaningful representations from the irregularly sampled data streams and alleviate the deficiencies of the data sources and supervised algorithms. Here, we propose a conceptually and computationally simple representation learning (RL) approach based on autoencoders (AEs) to generate discriminative features for crop type classification. The proposed methodology includes a set of single-layer AEs with a very limited number of neurons, each one trained with the mono-temporal spectral features of a small set of samples belonging to a class, resulting in a model capable of processing very large areas in a short computational time. Importantly, the developed approach remains flexible with respect to the availability of clear temporal observations. The signal derived from the ensemble of AEs is the reconstruction difference vector between input samples and their corresponding estimations, which are averaged over all cloud-/shadow-free temporal observations of a pixel location. This averaged reconstruction difference vector is the base for the representations and the subsequent classification. Experimental results show that the proposed extremely light-weight architecture indeed generates separable features for competitive performances in crop type classification, as distance metrics scores achieved with the derived representations significantly outperform those obtained with the initial data. Conventional classification models were trained and tested with representations generated from a widely used Sentinel-2 multi-spectral multi-temporal dataset, BreizhCrops. Our method achieved $77.06\%$ overall accuracy, which is $\sim 6\%$ higher than that achieved using original Sentinel-2 data within conventional classifiers and even $\sim 4\%$ better than complex deep models such as OmnisCNN. Compared to extremely complex and time-consuming models such as Transformer and long short-term memory (LSTM), only a $3\%$ reduction in overall accuracy was noted. Our method uses only $6.8k$ parameters, i.e., $\sim 400x$ fewer than OmnicsCNN and $\sim 27x$ fewer than Transformer. The results prove that our method is competitive in terms of classification performance compared with state-of-the-art methods while substantially reducing the computational load. Full article