Predictive Geoscience: Machine Learning Integrating Globe, Gravity, Magnetics, and Remote Sensing Data

Modelling species distributions with remote sensing data: bridging disciplinary perspectives

The magnetic data and gravity data have been recovered from magnetic tapes and earlier paper maps for Iraq. The magnetic data belonging to the State Company of Geology and Mining of Iraq (GEOSURV) were recovered by Frontier Processing Company and the U.S. Geological Survey (USGS) from the tapes originally flown by the French firm Compagnie General de Geophysique (CGG) in 1974. GETECH reprocessed the magnetic data using Geosoft and GETECH in‐house micro‐levelling software to its full glory which displays as a comprehensive and high quality dataset and shows fine detail along the profiles. GETECH recovered the legacy gravity data from the original field data sheets archived by IPC (Iraq Petroleum Company). These gravity data have been compiled with more recent GEOSURV sponsored surveys thus allowing for the first time a comprehensive digital and unified national gravity database to be constructed with full principal facts. Figure 1 shows the final aeromagnetic and gravity data coverage of Iraq. The only part of Iraq lacking gravity and aeromagnetic data coverage is the mountainous areas of the Kurdish Region of northeastern Iraq. Joint interpretation of the magnetic and gravity data will help to guide future geophysical investigations by GEOSURV, whose ultimate aim is to discover economical mineral and energy resources.

Urban poverty is a major obstacle to the healthy development of urbanization. Identifying and mapping urban poverty is of great significance to sustainable urban development. Traditional data and methods cannot measure urban poverty at a fine scale. Besides, existing studies often ignore the impact of the built environment and fail to consider the equal importance of poverty indicators. The emerging multi-source big data provide new opportunities for accurately measuring and monitoring urban poverty. This study aims to map urban poverty spatial at a fine scale by using multi-source big data, including social sensing and remote sensing data. The urban core of Zhengzhou is selected as the study area. The characteristics of the community’s living environment are quantified by accessibility, block vitality, per unit rent, public service infrastructure, and socio-economic factors. The urban poverty spatial index (SI) model is constructed by using the multiplier index of the factors. The SOM clustering method is employed to identify urban poverty space based on the developed SI. The performance of the proposed SI model is evaluated at the neighborhood scale. The results show that the urban poverty spatial measurement method based on multi-source big data can capture spatial patterns of typical urban poverty with relatively high accuracy. Compared with the urban poverty space measured based on remote sensing data, it considers the built environment and socio-economic factors in the identification of the inner city poverty space, and avoids being affected by the texture information of the physical surface of the residential area and the external structure of the buildings. Overall, this study can provide a comprehensive, cost-effective, and efficient method for the refined management of urban poverty space and the improvement of built environment quality.

This study explores the estimation of ground-level NO2 concentrations in Mexico City using an integrated approach of machine learning (ML) and remote sensing data. We used the NO2 measurements from the Sentinel-5P satellite, along with ERA5 meteorological data, to evaluate a pre-trained machine learing model. Our findings indicate that the model captures the spatial and temporal variability of NO2 concentrations across the urban landscape. Key meteorological parameters, such as temperature and wind speed, were identified as significant factors influencing NO2 levels. The model’s adaptability was further tested by incorporating additional variables, such as atmospheric boundary layer height. In order to compare the model’s performance to alternative ML models, we estimated the ground-level NO2 using the state-of-the-art TimeGPT. The results demonstrate that our baseline model has the best performance with a mean normalised root mean square error of 84.47%. This research underscores the potential of combining satellite observations with ML for scalable air quality monitoring, particularly in low- and middle-income countries with limited ground-based infrastructure. The study provides critical insights for air quality management and policy-making, aiming to mitigate the adverse health and environmental impacts of NO2 pollution.

<p>The nature of the Estonian crust was studied using global topography, magnetic data, and gravity data to estimate its tectonic regime. The Estonian Precambrian crystalline basement, composed of Paleo- to MesoProterozoic metamorphic and igneous rocks, is covered by a Paleozoic sedimentary rock deposit 100–780 m thick. To visualize crustal sources of the Estonian basement, we employed spectrum analysis of magnetic and gravity data, as well as two-dimensional (2D) forward modeling of gravity data. The gravimetric data was also evaluated to identify the depth of the Moho and Conrad discontinuities in Estonia. The magnetic data has also been evaluated to calculate the Curie point depth, which was then utilized to predict heat flow values inside the research zone. The subsurface of Estonia is divided into six petrological-structural zones: Tallinn, Alutaguse, Johvi, West-Estonian, Tapa and South-Estonian. To assess the structural variations of the crust at these locations, profiles of topographic, gravity, magnetic and heat flow data were constructed in each of the petrological-structural zones. The spectrum analysis and 2D gravity forward models yielded residual and regional gravity anomaly maps that show a significant amplitude potential maximum across the precambrian Rapakivi granitoid plutons and the Paldiski-Pskov tectonic zone. The Curie point depth reveals values ranging from 7 to 26 km, whereas the Moho depth suggests values ranging from 48 to 72 km and the Conrad depth values ranging from 14 to 20 km.</p>

Abstract. The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6) and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6). An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore.

Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R2 and RMSE of the model based on source III were better than the R2 and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R2 by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R2 of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R2 of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R2 of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work.

The large-scale quantification of accurate evapotranspiration (ET) time series has substantially been developed in recent decades using automated approaches based on remote sensing data. However, there are still several model-related uncertainties that require precise assessment. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and meteorological data from the Global Land Data Assimilation System (GLDAS) were used to estimate long-term daily actual ET based on three endmember selection procedures: two land cover-based models, one with (WF) and the other without (WOF) morphological functions, and the Allen method (with the default percentiles) for 2270 Landsat images. Models were evaluated for 23 flux tower sites with four main vegetation cover types as well as different climate types. Results showed that endmember selection with morphological functions (WF_ET) generally performed better than the other endmember approaches. Climate-based classification assessment provided the clearest discrimination between the performance of the different endmember selection approaches for the humid category. For humid zones, the land cover-based methods, especially WF, appropriately outperformed Allen. However, the performance of the three approaches was similar for sub-humid, semi-arid and arid climates together; the Allen approach was therefore recommended to avoid the need for dependency on land cover maps. Tower-by-tower validation also showed that the WF approach performed best at 12 flux tower sites, the WOF approach best at 5 and the Allen approach best at 6, suggesting that the use of land cover maps alone does not explain the differences between the performance of the land cover-based models and the Allen approach. Additionally, the satisfactory error metrics results when comparing the EC estimations with EC measurements, with root mean square error (RMSE) ≈ 0.91 and 1.59 mm·day−1, coefficient of determination (R2) ≈ 0.71 and 0.41, and bias percentage (PBias) ≈ 2% and 60% for crop and non-crop flux tower sites, respectively, supports the use of GLDAS meteorological forcing datasets with the different automated ET estimation approaches. Overall, given that the thorough evaluation of different endmember selection approaches at large scale confirmed the validity of the WF approach for different climate and land cover types, this study can be considered an important contribution to the global retrieval of long time series of ET.

This study evaluates the effectiveness of stone bunds in enhancing soil moisture, vegetation health, and crop yields in Ghana’s semi-arid Upper East Region, an important area for agricultural productivity in West Africa. In this region, agricultural practices are heavily impacted by erratic rainfall and poor soil moisture retention, threatening food security. Despite the known benefits of traditional soil conservation practices like stone bunds, their effectiveness in this context has not been fully quantified. Field and remote sensing data were used to evaluate the influence of stone bunds on soil moisture dynamics, vegetation growth, and crop yield. Experimental plots with and without stone bunds were monitored for climate, soil water infiltration, and soil moisture and analyzed using the NDVI from Sentinel-2 satellite imagery over two growing seasons under sorghum production (2022–2023). The results indicated that stone bunds enhanced soil moisture retention and increased infiltration rates. The NDVI analysis consistently revealed higher vegetation health and growth in the plots with stone bunds, particularly during critical growth periods. The intermediate results of the conducted experiment indicated that stone bunds increased sorghum yields by over 35% compared to the control plots. The substantial agronomic benefits of stone bunds as a soil and water conservation strategy were evident, improving soil water infiltration, water retention, vegetation health, and crop yields. The findings support the broader adoption of stone bunds in semi-arid regions to enhance agricultural productivity and resilience against climate variability. Further research is recommended to explore the long-term impacts and the integration of stone bunds with other sustainable farming practices to optimize rainfed agricultural outcomes.

Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data

Imaging Pleistocene volcanic edifices along the Egyptian Red Sea margin: Insights from reflection seismics and 3D constrained inversion of gravity and magnetic data

Understanding the structural framework controlling the sedimentary basins from the integration of gravity and magnetic data: A case study from the east of the Qattara Depression area, Egypt

Objective – To investigate the evolution of the Urban Expansion Index (UEI) in the Northwest mesoregion of Minas Gerais (MG), Brazil, between 1990 and 2020, using Remote Sensing (RS) data from the MapBiomas project. Methodology – The UEI was calculated based on the proportion of urbanized area in relation to other land use classes in each municipality. The values were normalized to enable temporal and spatial comparisons across municipalities and then classified into five urbanization categories. Spatial analysis was conducted through the creation of thematic maps, allowing a detailed visualization of urban growth trends and geospatial variations over time. Originality/relevance – The study provides a systematic approach to monitoring urban expansion at the regional scale, integrating RS data and spatial analysis with a focus on territorial management. It emphasizes the application of a specific index (UEI) and its spatial categorization to interpret urban dynamics. Results – The findings reveal significant variations in the level of urbanization among the 19 municipalities analyzed. Vazante recorded the highest UEI in 2020, followed by Paracatu and Unaí, indicating substantial urban growth. In contrast, Formoso and Guarda-Mor maintained the lowest levels of urbanization, reflecting limited urban development. The data suggest that economic factors such as agriculture, mining, and infrastructure investments influence urbanization patterns in the region. Theoretical/methodological contributions – The application of the UEI and the use of MapBiomas data demonstrate the effectiveness of Remote Sensing in analyzing regional urban expansion. The study contributes methodologically by proposing a useful tool for comparative analysis and territorial planning. Social and environmental contributions – The results underscore the importance of using RS data to support public policy formulation aimed at urban planning and the promotion of sustainable urban development, tailored to the local characteristics of the Northwest mesoregion of Minas Gerais.

The aim of this study was to analyse the sets of atmospherically corrected Remote Sensing (RS) data in order to assess the variability of bare arable land between different soil blocks in the selected area and evaluate the suitability of this approach for locally targeted management. The study was carried out on the territory of parts of South Moravian, Vysocina and Zlín regions. The RS data was analyzed by two models developed in the Arc GIS 10.22 environment using the Normalized Differential Vegetation Index (NDVI) and the Principal component analysis (PCA) analyzing bare soil (BS) with more than 50 % and more than 95 % of the block representation. A layer of agricultural land from the LPIS system was used to delimit arable land areas. For correct determinig of BS value for NDVI were carried out the terrestrial measurements in the monitored area by using the GreenSeeker handheld crop sensor and The FieldSpec® HandHeld 2 spectrometer. Based on these measurements and image dates, 0.2 NDVI was selected as the limit value. As a more suitable source for identifying BS, RapidEye appears to be able to identify an average of 26 % of the observed area as bare ground compared to Sentinel 2 data (22.5 % of the observed area) in models with more than 50 % By representing the BS in a block (NDVI 50, PCA 50). In these versions of the model was more variable soil (VS) indicated by RapidEye. With more than 95 % of the BS in the block (NDVI 95, PCA 95) was found more variable soils by Sentinel 2. This method of indirectly identifying soil variability can assist in the application of fertilizer or soil treatment in the area of site‑specific management.