Search Results (14,276)

The quality of the image data and the potential to invert crop growth parameters are essential for effectively using unmanned aerial vehicle (UAV)-based sensor systems in precision agriculture (PA). However, the existing research falls short in providing a comprehensive examination of sensor data quality and the inversion potential of crop growth parameters, and there is still ambiguity regarding how the quality of data affects the inversion potential. Therefore, this study explored the application potential of RGB and multispectral (MS) images acquired from three lightweight UAV platforms in the realm of PA: the DJI Mavic 2 Pro (M2P), Phantom 4 Multispectral (P4M), and Mavic 3 Multispectral (M3M). The reliability of pixel-scale data quality was evaluated based on image quality assessment metrics, and three winter wheat growth parameters, above-ground biomass (AGB), plant nitrogen content (PNC) and soil and plant analysis development (SPAD), were inverted using machine learning models based on multi-source image features at the plot scale. The results indicated that the RGB image quality from the M3M outperformed that of the M2P, while the MS image quality was marginally superior to that of the P4M. Nevertheless, these advantages in pixel-scale data quality did not improve inversion accuracy for crop parameters at the plot scale. Spectral features (SFs) derived from the P4M-based MS sensor demonstrated significant advantages in AGB inversion (R² = 0.86, rRMSE = 27.47%), while SFs derived from the M2P-based RGB camera exhibited the best performance in SPAD inversion (R² = 0.60, rRMSE = 7.67%). Additionally, combining spectral and textural features derived from the P4M-based MS sensor yielded the highest accuracy in PNC inversion (R² = 0.82, rRMSE = 14.62%). This study clarified the data quality of three prevalent UAV mounted sensor systems in PA and their influence on parameter inversion potential, offering guidance for selecting appropriate sensors and monitoring key crop growth parameters. Full article

The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also confuse the end-users when it comes to identifying the most appropriate product to address their requirements. Data fusion methods can help to combine competing and/or complementary maps in order to capitalize on their strengths while overcoming their limitations. We assessed the potential of the Dempster–Shafer Theory (DST) to enhance oil palm mapping in Sumatra (Indonesia) by combining four land-cover maps, hereafter named DESCALS, IIASA, XU, and MAPBIOMAS, according to the first author’s name or the research group that published it. The application of DST relied on four steps: (1) a discernment framework, (2) the assignment of mass functions, (3) the DST fusion rule, and (4) the DST decision rule. Our results showed that the DST decision map achieved significantly higher accuracy (Kappa = 0.78) than the most accurate input product (Kappa = 0.724). The best result was reached by considering the probabilities of pixels to belong to the OP class associated with DESCALS map. In addition, the belief (i.e., confidence) and conflict (i.e., uncertainty) maps produced by DST evidenced that industrial plantations were detected with higher confidence than smallholder plantations. Consequently, Kappa values computed locally were lower in areas dominated by smallholder plantations. Combining land-use products with DST contributes to producing state-of-the-art maps and continuous information for enhanced land-cover analysis. Full article

One of the most significant urban challenges focuses on addressing the effects of urban overheating as a consequence of climate change. Several methods have been developed to characterize urban heat islands (UHIs); however, the most widely used involve complex planning, huge time consumption, and substantial human and technical resources on field monitoring campaigns. Therefore, this study aims to provide an easily accessible and affordable remote sensing method for locating urban hotspots and addresses a multi-criteria assessment of urban heat-related parameters, allowing for a comprehensive city-wide evaluation. The novelty is based on leveraging the potential of the last Landsat 9 satellite, the application of kernel spatial interpolation, and GIS open access data, providing very high-resolution land surface temperature images over urban spaces. Within GIS workflow, the city is divided into LCZs, thermal hotspots are detected, and finally, it is analyzed to understand how urban factors, such as urban boundaries, building density, and vegetation, affect urban scale LST, all using graphical and analytical cross-assessment. The methodology has been tested in Seville, a representative warm Mediterranean city, where variations of up to 10 °C have been found between homogeneous residential areas. Thermal hotspots have been located, representing 11% of the total residential fabric, while results indicate a clear connection between the urban factors studied and overheating. The conclusions support the possibility of generating a powerful affordable tool for future research and the design of public policy renewal actions in vulnerable areas. Full article

We discuss determining a finite sample linear time-invariant (FS-LTI) system’s impulse response function, h[n], in the frequency domain when the input testing function is a uniform window function with a width of L and the output is limited to a finite number of effective samples, M. Assuming that the samples beyond M are all zeros, the corresponding infinite sample LTI (IS-LTI) system is a marginally stable system. The ratio of the discrete Fourier transforms (DFT) of the output to input of such an FS-LTI system, H0[k], cannot be directly used to find h[n] via inverse DFT (IDFT). Nevertheless, H0[k] contains sufficient information to determine the system’s impulse response function (IRF). In the frequency-domain approach, we zero-pad the output array to a length of N. We present methods to recover h[n] from H0[k] for two scenarios: (1) N≥max(L,M+1) and N is a coprime of L, and (2) N≥L+M+1. The marginal stable system discussed here is an artifact due to the zero-value assumption on unavailable samples. The IRF obtained applies to any LTI system up to the number of effective data samples, M. In demonstrating the equivalence of H0[k] and h[n], we derive two interesting DFT pairs. These DFT pairs can be used to find trigonometric sums that are otherwise difficult to prove. The frequency-domain approach makes mitigating the effects of interferences and random noise easier. In an example application in radar remote sensing, we show that the frequency-domain processing method can be used to obtain finer details than the range resolution provided by the radar system’s transmitter. Full article

Groundwater modeling in data-scarce regions faces significant challenges due to the lack of comprehensive, high-quality data, impacting model accuracy. This systematic review of Scopus-indexed papers identifies various approaches to address these challenges, including coupled hydrological-groundwater models, machine learning techniques, distributed hydrological models, water balance models, 3D groundwater flow modeling, geostatistical techniques, remote sensing-based approaches, isotope-based methods, global model downscaling, and integrated modeling approaches. Each methodology offers unique advantages for groundwater assessment and management in data-poor environments, often combining multiple data sources and modeling techniques to overcome limitations. However, these approaches face common challenges related to data quality, scale transferability, and the representation of complex hydrogeological processes. This review emphasizes the importance of adapting methodologies to specific regional contexts and data availability. It underscores the value of combining multiple data sources and modeling techniques to provide robust estimates for sustainable groundwater management. The choice of method ultimately depends on the specific objectives, scale of the study, and available data in the region of interest. Future research should focus on improving the integration of diverse data sources, enhancing the representation of complex hydrogeological processes in simplified models, and developing robust uncertainty quantification methods tailored for data-scarce conditions. Full article

The Global Change Observation Mission-Climate (GCOM-C) satellite, launched in December 2017, is equipped with the Second-generation Global Imager (SGLI) sensor, featuring a moderate spatial resolution of 250 m and 19 spectral bands, including the unique 380 nm band. After six years in orbit, a comprehensive evaluation of SGLI products and their temporal consistency is needed. Remote sensing reflectance (Rrs) is the primary product for monitoring water quality, forming the basis for deriving key oceanic constituents such as chlorophyll-a (Chla) and total suspended matter (TSM). The Japan Aerospace Exploration Agency (JAXA) provides Rrs products through two platforms, G-Portal and JASMES, each employing different atmospheric correction methodologies and assumptions. This study aims to evaluate the SGLI full-resolution Rrs products from G-Portal and JASMES at regional scales (Japan and East Asia) and assess G-Portal Rrs products globally between January 2018 and December 2023. The evaluation employs in situ matchups from NASA’s Aerosol Robotic Network-Ocean Color (AERONET-OC) and cruise measurements. We also assess the retrieval accuracy of two water quality indices, Chla and TSM. The AERONET-OC data analysis reveals that JASMES systematically underestimates Rrs values at shorter wavelengths, particularly at 412 nm. While the Rrs accuracy at 412 nm is relatively low, G-Portal’s Rrs products perform better than JASMES at shorter wavelengths, showing lower errors and stronger correlations with AERONET-OC data. Both G-Portal and JASMES show lower agreement with AERONET-OC and cruise datasets at shorter wavelengths but demonstrate improved agreement at longer wavelengths (530 nm, 565 nm, and 670 nm). JASMES generates approximately 12% more matchup data points than G-Portal, likely due to G-Portal’s stricter atmospheric correction thresholds that exclude pixels with high reflectance. In situ measurements indicate that G-Portal provides better overall agreement, particularly at lower Rrs magnitudes and Chla concentrations below 5 mg/m³. This evaluation underscores the complexities and challenges of atmospheric correction, particularly in optically complex coastal waters (Case 2 waters), which may require tailored atmospheric correction methods different from the standard approach. The assessment of temporal consistency and seasonal variations in Rrs data shows that both platforms effectively capture interannual trends and maintain temporal stability, particularly from the 490 nm band onward, underscoring the potential of SGLI data for long-term monitoring of coastal and oceanic environments. Full article

In the realm of hydrological engineering, integrating extensive geospatial raster data from remote sensing (Big Data) with sparse field measurements offers a promising approach to improve prediction accuracy in groundwater studies. In this study, we integrated multisource data by applying the LMC to model the spatial relationships of variables and then utilized block support regularization with collocated block cokriging (CBCK) to enhance our predictions. A critical engineering challenge addressed in this study is support homogenization, where we adjusted punctual variances to block variances and ensure consistency in spatial predictions. Our case study focused on mapping groundwater table depth to improve water management and planning in a mixed land use area in Southeast Brazil that is occupied by sugarcane crops, silviculture (Eucalyptus), regenerating fields, and natural vegetation. We utilized the 90 m resolution TanDEM-X digital surface model and STEEP (Seasonal Tropical Ecosystem Energy Partitioning) data with a 500 m resolution to support the spatial interpolation of groundwater table depth measurements collected from 56 locations during the hydrological year 2015–16. Ordinary block kriging (OBK) and CBCK methods were employed. The CBCK method provided more reliable and accurate spatial predictions of groundwater depth levels (RMSE = 0.49 m), outperforming the OBK method (RMSE = 2.89 m). An OBK-based map concentrated deeper measurements near their wells and gave shallow depths for most of the points during estimation. The CBCK-based map shows more deeper predicted points due to its relationship with the covariates. Using covariates improved the groundwater table depth mapping by detecting the interconnection of varied land uses, supporting the water management for agronomic planning connected with ecosystem sustainability. Full article

In the realm of global climate change and environmental protection, the precise estimation of forest ecosystem carbon density is essential for devising effective carbon management and emission reduction strategies. This study employed forest inventory, soil carbon, and remote sensing data combined with three models—Random Forest (RF), Geographically Weighted Regression (GWR), and the innovative Geographically Weighted Random Forest (GWRF) model—integrated with remote sensing technology to develop a framework for assessing the regional spatial distribution of the forest vegetation carbon density (FVC) and forest soil carbon density (FSC). The findings revealed that the GWRF model outperformed the other models in estimating both the FVC and FSC. The data indicated that the FVC in Heilongjiang Province ranged from 4.91 t/ha to 72.39 t/ha, with an average of 40.88 t/ha. In contrast, the average FSC was 182.29 t/ha, with a range of 96.01 t/ha to 255.09 t/ha. Additionally, the forest ecosystem carbon density (FEC) varied from 124.36 t/ha to 302.18 t/ha, averaging 223.17 t/ha. Spatially, the FVC, FSC, and FEC exhibited a consistent growth trend from north to south. The results of this study demonstrate that machine learning models that consider spatial relationships can improve predictive accuracy, providing valuable insights for the future spatial modeling of forest carbon storage. Full article

Groundwater level (GWL) in dry areas is an important parameter for understanding groundwater resources and environmental sustainability. Remote sensing data combined with machine learning algorithms have become one of the important tools for groundwater level modeling. However, the effectiveness of the above-based model in the plains of the arid zone remains underexplored. Fortunately, soil salinity and soil moisture may provide an optimized solution for GWL prediction based on the application of apparent conductivity (ECa, mS/m) using electromagnetic induction (EMI). This has not been attempted in previous studies in oases in arid regions. The study proposed two strategies to predict GWL, included an ECa-based GWL model and a remote sensing-based GWL model (RS_GWL), and then compared and explored their performances and cooperation possibilities. To this end, this study first constructed the ECa prediction model and the RS_GWL with ensemble machine learning algorithms using environmental variables and field observations (474 ECa reads and 436 groundwater level observations from a mountain–oasis–desert system, respectively). Subsequently, a strategy to improve the prediction accuracy of GWL was proposed by comparing the correlation between GWL observations and the two models. The results showed that the RS_GWL prediction model explains 30% and 90% of the spatial variability in the two value domain intervals, GWL < 10 m and GWL > 10 m, respectively. The R² of the modeling and the validation of the ECa was 79% and 73%, respectively. Careful analysis of the scatter plots between predicted ECa and GWL revealed that when ECa varies between 0–600 mS/m, 600–800 mS/m, 800–1100 mS/m, and >1100 mS/m, the fluctuation ranges of the corresponding GWL correspond to 0–31 m, 0–15 m, 0–10 m, and 0–5 m. Finally, combining the spatial variability of ECa and RS_GWL spatial distribution map, the following optimization strategies were finally established: GWL < 5 m (in natural land with ECa > 1100 mS/m), GWL < 5 m (occupied by farmland from RS_GWL) and GWL > 10 m (from RS_GWL), and 3 < GWL < 10 m (speculated). In conclusion, this study has demonstrated that the integration of EMI technology has significantly improved the precision of forecasting shallow GWL in oasis plain regions, outperforming the outcomes achieved by the use of remote sensing data alone. Full article

Global climate fluctuations pose challenges not only to natural environments but also to the conservation and transmission of human cultural and historical heritage. World Heritage Sites are pivotal regions for studying climate change impacts and devising adaptation strategies, with remote sensing technology showcasing significant utility in monitoring these impacts, especially in the Mediterranean region’s diverse and sensitive climate context. Although existing work has begun to explore the role of remote sensing in monitoring the effects of climate change, detailed analysis of the spatial distribution and temporal trends of landscape stability remains limited. Leveraging remote sensing data and its derived products, this study assessed climate change impacts on the Causses and Cévennes Heritage Site, a typical Mediterranean heritage landscape. Specifically, this study utilized remote sensing data to analyze the trends in various climatic factors from 1985 to 2020. The landscape stability model was developed utilizing land cover information and landscape indicators to explore the landscape stability and its distribution features within the study area. Finally, we adopted the Geographical Detector to quantify the extent to which climatic factors influence the landscape stability’s spatial distribution across different periods. The results demonstrated that (1) the climate showed a warming and drying pattern during the study period, with distinct climate characteristics in different zones. (2) The dominance of woodland decreased (area proportion dropped from 76% to 66.5%); transitions primarily occurred among woodland, cropland, shrubland, and grasslands; landscape fragmentation intensified; and development towards diversification and uniformity was observed. (3) Significant spatiotemporal differences in landscape stability within the heritage site were noted, with an overall downward trend. (4) Precipitation had a high contribution rate in factor detection, with the interactive enhancement effects between temperature and precipitation being the most prominent. The present study delivers a thorough examination of how climate change affects the Causses and Cévennes Heritage Landscape, reveals its vulnerabilities, and offers crucial information for sustainable conservation efforts. Moreover, the results offer guidance for the preservation of similar Mediterranean heritage sites and contribute to the advancement and deepening of global heritage conservation initiatives. Full article

Los Angeles coastal waters are an ecologically important marine habitat and a famed recreational area for tourists. Constant surveillance is essential to ensure compliance with established health standards and to address the persistent water quality challenges in the region. Remotely sensed datasets are increasingly being applied toward improved detection of water quality by augmenting monitoring programs with spatially intensive and accessible data. This study evaluates the potential of satellite remote sensing to augment traditional monitoring by analyzing the relationship between in situ and satellite-derived turbidity data. Field measurements were performed from July 2021 to March 2024 to build synchronous matchup datasets consisting of satellite and field data. Correlation analysis indicated a positive relationship between satellite-derived and field-measured turbidity (R² = 0.451). Machine learning models were assessed for predictive accuracy, with the random forest model achieving the highest performance (R² = 0.632), indicating its robustness in modeling complex turbidity patterns. Seasonal trends revealed higher turbidity during wet months, likely due to stormwater runoff from the Ballona Creek watershed. Despite limitations from cloud cover and spatial resolution, the findings suggest that integrating satellite data with machine learning can enhance large-scale, efficient turbidity monitoring in coastal waters. Full article

Anthropogenic heat is the heat generated by human activities such as industry, construction, transport, and metabolism. Accurate estimates of anthropogenic heat are essential for studying the impacts of human activities on the climate and atmospheric environment. Commonly applied methods for estimating anthropogenic heat include the inventory method, the energy balance equation method, and the building model simulation method. In recent years, the rapid development of computer technology and the availability of massive data have made machine learning a powerful tool for estimating anthropogenic heat fluxes and assessing its effects. Multi-source remote sensing data have also been widely used to obtain more details of the spatial and temporal distribution characteristics of anthropogenic heat. This paper reviews the main approaches for estimating anthropogenic heat emissions. The typical algorithms of the abovementioned three methods are introduced, and their advantages and limitations are also evaluated. Moreover, the recent progress in the application of remote sensing data and machine learning are discussed as well. Based on big data and machine learning techniques, the research on feature engineering and model fusion will bring about major changes in data analysis and modeling of anthropogenic heat. More in-depth research of this issue is recommended to provide important support for curbing global warming, mitigating air pollution, and achieving the national goals of carbon peak and a carbon neutrality strategy. Full article

Soil salinization affects agricultural productivity and ecosystem health in Xinjiang, especially in arid areas. The region’s complex topography and limited agricultural data emphasize the pressing need for effective, large-scale monitoring technologies. Therefore, 1044 soil samples were collected from arid farmland in northern Xinjiang, and the potential effectiveness of soil salinity monitoring was explored by combining environmental variables with Landsat 8 and Sentinel-2. The study applied four types of feature selection algorithms: Random Forest (RF), Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Successive Projections Algorithm (SPA). These variables are then integrated into various machine learning models—such as Ensemble Tree (ETree), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and LightBoost—as well as deep learning models, including Convolutional Neural Networks (CNN), Residual Networks (ResNet), Multilayer Perceptrons (MLP), and Kolmogorov–Arnold Networks (KAN), for modeling. The results suggest that fertilizer use plays a critical role in soil salinization processes. Notably, the interpretable model KAN achieved an accuracy of 0.75 in correctly classifying the degree of soil salinity. This study highlights the potential of integrating multi-source remote sensing data with deep learning technologies, offering a pathway to large-scale soil salinity monitoring, and thereby providing valuable support for soil management. Full article

Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification and recognition of these images present significant challenges. In this paper, we propose a band selection method (GE-AP) based on multi-feature extraction and the Affine Propagation Clustering (AP) algorithm for dimensionality reduction of hyperspectral images, aiming to improve classification accuracy and processing efficiency. In this method, texture features of the band images are extracted using the Gray-Level Co-occurrence Matrix (GLCM), and the Euclidean distance between bands is calculated. A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. Through simulation and comparison experiments evaluating the overall classification accuracy (OA) and Kappa coefficient, it was found that the GE-AP method achieves the highest OA and Kappa coefficient compared to three other methods, with maximum increases of 8.89% and 13.18%, respectively. This verifies that the proposed method outperforms traditional single-information methods in handling spatial and spectral redundancy between bands, demonstrating good adaptability and stability. Full article

The expansion of agricultural frontiers in the Amazon region poses a significant threat to forest conservation and biodiversity persistence. This study focuses on Pará state, Brazil, aiming to identify and characterize the predominant production systems by combining remote sensing data and landscape structure metrics. A rule-based classification tree algorithm is applied to classify hexagonal cells based on land cover, deforestation patterns, and distance from dairy facilities. The results reveal three dominant production systems: Natural Region, Non-Intensive Beef, and Initial Front, with livestock production being prominent. The analysis indicates that there is a correlation between the productive area and deforestation, emphasizing the role of agriculture as a driver of forest loss. Moreover, road networks significantly influence production system spatial distribution, highlighting the importance of infrastructure in land use dynamics. The Shannon diversity index reveals that areas with production systems exhibit greater diversity in land use and land cover classes, reflecting a wider range of modifications. In contrast, natural vegetation areas show lower Shannon values, suggesting that these areas are more intact and are less affected by human activities. These findings underscore the urgent need for sustainable development policies that will mitigate threats to forest resilience and biodiversity in Pará state. Full article