Solar cadasters are excellent tools for determining the most suitable rooftops and areas for PV deployment in urban environments. There are several open models that are available to compute the solar potential in cities. The Solar Energy on Building Envelopes (SEBE) is a powerful model incorporated in a geographic information system (QGIS). The main input for these tools is the digital surface model (DSM). The accuracy of the DSM can contribute significantly to the uncertainty of the solar potential, since it is the basis of the shading and sky view factor computation. This work explores the impact of two different methodologies for creating a DSM to the solar potential. Solar potential is estimated for a small area in a university campus in Madrid using photogrammetry from google imagery and LiDAR data to compute different DSM. Large differences could be observed in the building edges and in the areas with a more complex and diverse topology that resulted in significant differences in the solar potential. The RSMD at a measuring point in the building rooftop can range from 10% to 50% in the evaluation of results. However, the flat and clear areas are much less affected by these differences. A combination of both techniques is suggested as future work to create an accurate DSM. Full article

As the most relevant indicator of global warming, the ocean heat content (OHC) change is tightly linked to the Earth’s energy imbalance. Therefore, it is vital to study the OHC and heat absorption and redistribution. Here we analyzed the characteristics of global OHC variations based on a previously reconstructed OHC dataset (named OPEN) with four other gridded OHC datasets from 1993 to 2021. Different from the other four datasets, the OPEN dataset directly obtains OHC through remote sensing, which is reliable and superior in OHC reconstruction, further verified by the Clouds and the Earth’s Radiant Energy System (CERES) radiation flux data. We quantitatively analyzed the changes in the upper 2000 m OHC of the oceans over the past three decades from a multisource and multilayer perspective. Meanwhile, we calculated the global ocean heat uptake to quantify and track the global ocean warming rate and combined it with the Oceanic Niño Index to analyze the global evolution of OHC associated with El Niño–Southern Oscillation variability. The results show that different datasets reveal a continuously increasing and non-decaying global ocean warming from multiple perspectives, with more heat being absorbed by the subsurface and deeper ocean over the past 29 years. The global OHC heating trend from 1993 to 2021 is 7.48 ± 0.17, 7.89 ± 0.1, 10.11 ± 0.16, 7.78 ± 0.17, and 12.8 ± 0.26 × 10²² J/decade according to OPEN, IAP, EN4, Ishii, and ORAS5, respectively, which shows that the trends of the OPEN, IAP, and Ishii datasets are generally consistent, while those of EN4 and ORAS5 datasets are much higher. In addition, the ocean warming characteristics revealed by different datasets are somewhat different. The OPEN OHC dataset from remote sensing reconstruction shows a unique remote sensing mapping advantage, presenting a distinctive warming pattern in the East Indian Ocean. Meanwhile, the OPEN dataset had the largest statistically significant area, with 85.6% of the ocean covered by significant positive trends. The significant and continuous increase in global ocean warming over the past three decades, revealed from remote sensing reconstruction, can provide an important reference for projecting ocean warming in the context of global climate change toward the United Nations Sustainable Development Goals. Full article

This paper investigates the challenging problem of direction-of-arrival (DOA) estimation in impulsive noise and presents a fast iterative shrinkage-thresholding algorithm (FISTA)-based approach to tackle the difficulty. More specifically, the underlying noise is modelled as the superposition of outliers in the white Gaussian noise. Leveraging on the spot-sparse characteristic of the outlier matrix, the FISTA is conducted on each snapshot of the array output. With the estimated outlier matrix and the coarse on-grid DOA estimates, an alternating optimization method is developed to retrieve the final off-grid DOA estimates. Simulation results show that the proposed method outperforms existing methods in terms of resolution capability and estimation accuracy especially in severe noise environments. Full article

Maintaining the integrity and stability of oasis ecosystems is an important topic in the field of ecological research. Assessment of ecosystem services and their changes can provide important support for the sustainable development of oases. This study took the Dunhuang oasis in the hyper-arid area as the research object and used 1990, 2010, and 2020 Landsat series satellite images to complete the land use interpretation by random forest classification. Then we estimated the ecosystem services value (ESV) by using benefit transfer method, and predicted the trend of ecosystem service value changes under three scenarios using the Analytic Hierarchy Process method and the patch generation land use simulation model (AHP-PLUS model). The results showed that the vegetation areas of the Dunhuang Oasis first decreased and then increased during 1990–2020. The decrease was largely due to the expansion of built-up land and farmland, and the increase was mainly contributed by the implementation of ecological protection policies. The path of changes in the ESV of the Dunhuang Oasis during 1990–2020 was well consistent with that of vegetation areas, with a maximum of 9068.15×10⁶ yuan (in 1990) and a minimum of 6271.46×10⁶ yuan (in 2010). Spatial autocorrelation analysis showed that urbanization reduced ESV, and the implementation of ecological policies enhanced ESV. The ESV of the Dunhuang Oasis for the year 2030 under the ecological conservation scenario could reach 7631.07×10⁶ yuan, which is 381.1×10⁶ yuan higher that under the economic development scenario. The ecological conservation scenario is the optimal option to achieve sustainable development of the Dunhuang Oasis. We suggested that the government should continuously enhance the protection of forests and waterbodies, reasonably restrict production and domestic water consumption, and efficiently increase the proportion of ecological water consumption. In addition, this study improved the evaluation method of oasis ESV based on the proportion of Normalized Difference Vegetation Index (NDVI) of grasslands with different coverage, which is important for improving the environment in arid areas. Full article

The Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided global long-term observations of mass transport in the Earth system with applications in numerous geophysical fields. In this paper, we targeted the in-orbit performance of the GRACE key instruments, the ACCelerometers (ACC) and the MicroWave ranging Instrument (MWI). For the ACC data, we followed a transplant approach analyzing the residual accelerations from transplanted accelerations of one of the two satellites to the other. For the MWI data, we analyzed the post-fit residuals of the monthly GFZ GRACE RL06 solutions with a focus on stationarity. Based on the analyses for the two test years 2007 and 2014, we derived stochastic models for the two instruments and a combined ACC+MWI stochastic model. While all three ACC axes showed worse performance than their preflight specifications, in 2007, a better ACC performance than in 2014 was observed by a factor of 3.6 due to switched-off satellite thermal control. The GRACE MWI noise showed white noise behavior for frequencies above 10 mHz around the level of $1.5\times {10}^{-6} \mathrm{m}/\sqrt{\mathrm{Hz}}$. In the combined ACC+MWI noise model, the ACC part dominated the frequencies below 10 mHz, while the MWI part dominated above 10 mHz. We applied the combined ACC+MWI stochastic models for 2007 and 2014 to the monthly GFZ GRACE RL06 processing. This improved the formal errors and resulted in a comparable noise level of the estimated gravity field parameters. Furthermore, the need for co-estimating empirical parameters was reduced. Full article

The conservation, restoration and sustainable use of wetlands is the target of several international agreements, among which are the Sustainable Development Goals (SDGs). Earth Observation (EO) technologies can assist national authorities in monitoring activities and the environmental status of wetlands to achieve these targets. In this study, we assess the capabilities of the Sentinel-2 instrument to model Gross Primary Productivity (GPP) as a proxy for the monitoring of ecosystem health. To estimate the spatial and temporal variation of GPP, we develop an empirical model correlating in situ measurements of GPP, eight Sentinel-2 derived vegetation indexes (VIs), and different environmental drivers of GPP. The model automatically performs an interdependency analysis and selects the model with the highest accuracy and statistical significance. Additionally, the model is upscaled across larger areas and monthly maps of GPP are produced. The study methodology is applied in a marsh ecosystem located in Doñana National Park, Spain. In this application, a combination of the red-edge chlorophyll index (CLr) and rainfall data results in the highest correlation with in situ measurements of GPP and is used for the model formulation. This yields a coefficient of determination (R²) of 0.93, Mean Absolute Error (MAE) equal to 0.52 gC m⁻² day⁻¹, Root Mean Squared Error (RMSE) equal to 0.63 gC m⁻² day⁻¹, and significance level p < 0.05. The model outputs are compared with the MODIS GPP global product (MOD17) for reference; an enhancement of the estimation of GPP is found in the applied methodology. Full article

The classification of airborne LiDAR data is a prerequisite for many spatial data elaborations and analysis. In the domain of power supply networks, it is of utmost importance to be able to discern at least five classes for further processing—ground, buildings, vegetation, poles, and catenaries. This process is mainly performed manually by domain experts with the use of advanced point cloud manipulation software. The goal of this paper is to find a set of features which would divide space well enough to achieve accurate automatic classification on all relevant classes within the domain, thus reducing manual labor. To tackle this problem, we propose a single multi-class approach to classify all four basic classes (excluding ground) in a power supply domain with single pass-through, using one network. The proposed solution implements random forests and gradient boosting to create a feature-based per-point classifier which achieved an accuracy and F1 score of over 99% on all tested cases, with the maximum of 99.7% for accuracy and 99.5% for F1 score. Moreover, we achieved a maximum of 81.7% F1 score for the most sparse class. The results show that the proposed set of features for the LiDAR data cloud is effective in power supply line classification. Full article

Because of climate change and human activity, North and Central Africa are experiencing a significant water shortage. Recent advancements in earth observation technologies have made widespread groundwater monitoring possible. To examine spatial and temporal mass fluctuations caused by groundwater variations in Chad, gravity solutions from the Gravity Recovery and Climate Experiment (GRACE), climatic model outputs, and precipitation data are integrated. The results are as follows: (1) The investigated region experienced average annual precipitation (AAP) rates of 351.6, 336.22, and 377.8 mm yr⁻¹, throughout the overall investigation period (04/2002–12/2021), Period I (04/2002–12/2011), and Period II (01/2012–12/2021), respectively. (2) Using the three gravity solutions, the average Terrestrial Water Storage Variations (ΔTWS) values are estimated to be +0.26 ± 0.04, +0.006 ± 0.10, and +0.64 ± 0.12 cm yr⁻¹, for the overall study period, periods I, and II, respectively. (3) Throughout the full period, periods I, and II, the groundwater storage fluctuations (ΔGWS) are calculated to be +0.25 ± 0.04, +0.0001 ± 0.099, and +0.62 ± 0.12 cm yr⁻¹, respectively after removing the soil moisture (ΔSMS) and Lake Chad water level trend values. (4) The country receives an average natural recharge rate of +0.32 ± 0.04, +0.068 ± 0.099, and +0.69 ± 0.12 cm yr⁻¹, throughout the whole period, Periods I, and II, respectively. (5) The southern mountainous regions of Erdi, Ennedi, Tibesti, and Darfur are receiving higher rainfall rates that may recharge the northern part of Chad through the stream networks; in addition to the Lake Chad and the higher rainfall over southern Chad might help recharge the central and southern parts of the country. (6) A preferred groundwater flow path from the Kufra (Chad and Libya) to the Dakhla basin (Egypt) appears to be the Pelusium mega shear system, which trends north-east. The findings suggest that GRACE is useful for monitoring changes in groundwater storage and recharge rates across large areas. Our observation-based methodology provides a unique understanding of monthly ground-water patterns at the state level, which is essential for successful interstate resource allocation, future development, and policy initiatives, as well as having broad scientific implications for arid and semiarid countries. Full article

The uncertainty from the under-estimation and over-estimation of forest aboveground biomass (AGB) is an urgent problem in optical remote sensing estimation. In order to more accurately estimate the AGB of Pinus densata forests in Shangri-La City, we mainly discuss three non-parametric models—the artificial neural network (ANN), random forests (RFs), and the quantile regression neural network (QRNN) based on 146 sample plots and Sentinel-2 images in Shangri-La City, China. Moreover, we selected the corresponding optical quartile models with the lowest mean error at each AGB segment to combine as the best QRNN (QRNNb). The results showed that: (1) for the whole biomass segment, the QRNNb has the best fitting performance compared with the ANN and RFs, the ANN has the lowest R² (0.602) and the highest RMSE (48.180 Mg/ha), and the difference between the QRNNb and RFs is not apparent. (2) For the different biomass segments, the QRNNb has a better performance. Especially when AGB is lower than 40 Mg/ha, the QRNNb has the highest R² of 0.961 and the lowest RMSE of 1.733 (Mg/ha). Meanwhile, when AGB is larger than 160 Mg/ha, the QRNNb has the highest R² of 0.867 and the lowest RMSE of 18.203 Mg/ha. This indicates that the QRNNb is more robust and can improve the over-estimation and under-estimation in AGB estimation. This means that the QRNNb combined with the optimal quantile model of each biomass segment provides a method with more potential for reducing the uncertainties in AGB estimation using optical remote sensing images. Full article

The retreat of glaciers has altered hydrological processes in cryospheric regions and affects water resources at the basin scale. It is necessary to elucidate the contributions of environmental changes to evapotranspiration (ET) variation in cryospheric-dominated regions. Considering the upper reach of the Shule River Basin as a typical cryospheric-dominated watershed, an extended Budyko framework addressing glacier change was constructed and applied to investigate the sensitivity and contribution of changes in environmental variables to ET variation. The annual ET showed a significant upward trend of 1.158 mm yr⁻¹ during 1982–2015 in the study area. ET was found to be the most sensitive to precipitation (P), followed by the controlling parameter (w), which reflects the integrated effects of landscape alterations, potential evapotranspiration (ET₀), and glacier change (∆W). The increase in P was the dominant factor influencing the increase in ET, with a contribution of 112.64%, while the decrease in w largely offset its effect. The contributions of P and ET₀ to ET change decreased, whereas that of w increased when considering glaciers using the extended Budyko framework. The change in glaciers played a clear role in ET change and hydrological processes, which cannot be ignored in cryospheric watersheds. These findings are helpful for better understanding changes in water resources in cryospheric regions. Full article

As an important remote sensing technology, millimeter-wave radar is used for environmental sensing in many fields due to its advantages of all-day, all-weather operation. With the increasing use of radars, inter-radar interference becomes increasingly critical. Severe mutual interference degrades radar signal quality and affects the performance of post-processing, e.g., synthetic aperture radar (SAR) imaging and target tracking. Aiming to deal with mutual interference, we propose an interference mitigation method based on variational mode decomposition (VMD). With the characteristics that the target is a single-frequency sine wave and the interference is a broadband signal, VMD is used for decomposing the radar received signal and separating the target from the interference. Interference mitigation is then implemented in each decomposed mode, and an interference-free signal is obtained through the reconstruction process. Simulation results of multi-target scenarios demonstrate that the proposed method outperforms existing decomposition-based methods. This conclusion is also confirmed by the experimental results on real data. Full article

Prefabricated steel box girders (SBGs) are widely adopted in bridge engineering due to their light weight and low lifecycle cost. To smoothly assemble SBG components on a construction site, it is necessary to inspect their geometric quality and ensure that all the as-is SBG components have the correct dimensions. However, the traditional inspection method is time-consuming and error-prone. This study developed a non-contact geometric quality assessment technique based on 3D laser scanning to accurately assess the locations and dimensions of SBG components. First, a robust normal-based region-growing algorithm was developed to divide the SBG components into segments with different labels. The scanned data related to the T ribs were then extracted through the proposed subtraction algorithm after the identification of the steel cabin. Lastly, the required items for geometric quality inspection were calculated based on the extracted as-is SBG components. The feasibility of the proposed geometric quality assessment method was validated through a real SBG project. Field test results showed that the developed inspection technique could assess the geometric quality of prefabricated SBG components in a more accurate and efficient manner compared to traditional measurement approaches. Full article

Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height (${\sigma }_{dbh}$) and the number of trees ($N$), were predicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space-borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height $h_v$ and ground phase ${\phi }_0$. The Fourier–Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of ${\sigma }_{dbh}$ and $N$, thirteen geometrical and physical parameters were defined ($P1-P13$). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting ${\sigma }_{dbh}$ and $N$. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R²* of 0.25 for ${\sigma }_{dbh}$, whereas only 22% of the variation in $N$ was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies. Full article

Cultural traces under forests are one of the main problems affecting the identification of archaeological sites in densely forested areas, so it is full of challenges to discover ancient tombs buried under dense vegetation. The covered ancient tombs can be identified by studying the time-series features of the vegetation covering the ancient tombs on the multi-time series remote sensing images because the ancient tombs buried deep underground have long-term underground space structures, which affect the intrinsic properties of the surface soil so that the growth status of the covering vegetation is different from that of the vegetation in the area without ancient tombs. We first use the highly detailed DSM data to select the ancient tombs that cannot be visually distinguished on the optical images. Then, we explored and constructed the temporal features of the ancient tombs under the forest and the non-ancient tombs in the images, such as the radar timing-series features of Sentinel 1 and the multi-spectral and vegetation index timing-series features of Sentinel 2. Finally, based on these features and machine learning, we designed an automatic identification algorithm for ancient tombs under the forest. The method has been validated in Baling Mountain in Jingzhou, China. It is very feasible to automatically identify ancient tombs covered by surface vegetation by using the timing-series features of remote sensing images. Additionally, the identification of large ancient tombs or concentrated ancient tombs is more accurate, and the accuracy is improved after adding radar features. The paper concludes with a discussion of the current limitations and future directions of the method. Full article

Accurate atmospheric visibility prediction is of great significance to public transport safety. However, since it is affected by multiple factors, there still remains difficulties in predicting its heterogenous spatial distribution and rapid temporal variation. In this paper, a recursive neural network (RNN) prediction model modified with the frame-hopping transmission gate (FHTG), feature fusion module (FFM) and reverse scheduled sampling (RSS), named SwiftRNN, is developed. The new FHTG is used to accelerate training, the FFM is used for extraction and fusion of global and local features, and the RSS is employed to learn spatial details and improve prediction accuracy. Based on the ground-based monitoring data of atmospheric visibility from the China Meteorological Information Center during 1 January 2018 to 31 December 2020, the SwiftRNN model and two traditional ConvLSTM and PredRNN models are performed to predict hourly atmospheric visibility in central and eastern China at a lead of 12 h. The results show that the SwiftRNN model has better performance in the skill scores of visibility prediction than those of the ConvLSTM and PredRNN model. The averaged structural similarity (SSIM) of predictions at a lead up to 12 h is 0.444, 0.425 and 0.399 for the SwiftRNN, PredRNN and ConvLSTM model, respectively, and the averaged image perception similarity (LPIPS) is 0.289, 0.315 and 0.328, respectively. The averaged critical success index (CSI) of predictions over 1000 m fog area is 0.221, 0.205 and 0.194, respectively. Moreover, the training speed of the SwiftRNN model is 14.3% faster than the PredRNN model. It is also found that the prediction effect of the SwiftRNN model over 1000 m medium grade fog area is significantly improved along with lead times compared with the ConvLSTM and PredRNN model. All above results demonstrate the SwiftRNN model is a powerful tool in predicting atmospheric visibility. Full article