Use Of Satellite Data Research Articles

Estimation of PM2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite

The use of satellite data to estimate PM2.5 is an appropriate approach for long-term, substantial monitoring and assessment. To estimate PM2.5, the majority of the algorithms now in use utilize the top-of-atmosphere (TOA) reflectance or aerosol optical depth (AOD) derived from scalar satellite data. However, there is relatively little research on the retrieval of PM2.5 using multi-angle polarized data. With its directional polarimetric camera (DPC), the Chinese new-generation satellite Gaofen 5B (henceforth referred to as GF-5B) offers a unique opportunity to close this gap in multi-angle polarized observation data. In this research, we utilized TOA data from the DPC payload and applied the gradient boosting machine method to simulate the impact of the observation angle, wavelength, and polarization information on the accuracy of PM2.5 retrieval. We identified the optimal conditions for the effective estimation of PM2.5. The quantitative results indicated that, under these optimal conditions, the PM2.5 concentrations retrieved by GF-5B showed a strong correlation with the ground-based data, achieving an R2 of 0.9272 and an RMSE of 7.38 µg·m−3. By contrast, Himawari-8’s retrieval accuracy under similar data conditions consisted of an R2 of 0.9099 and RMSE of 7.42 µg·m−3, indicating that GF-5B offers higher accuracy. Furthermore, the retrieval results in this study demonstrated an R2 of 0.81 when compared to the CHAP dataset, confirming the feasibility and effectiveness of the use of GF-5B for PM2.5 retrieval and providing support for PM2.5 estimation through multi-angle polarized data.

Use of satellite data to determine the cloud optical depths present during overirradiance conditions

Abstract Overirradiance conditions can negatively impact the operation of photovoltaic systems if no protective measures have been implemented, leading to potential damages and economic losses in photovoltaic generation plants. Current simulation models attempt to understand the mechanism of overirradiance conditions. However, their observations still differ significantly from experimental ones, emphasizing the need to better understand the two main hypotheses that account for overirradiance events: reflection at the edges of thick clouds and Mie scattering in thin clouds. This paper studies the qualitative correlation between the global tilted irradiance measured by a spectroradiometer on the surface and the optical depth of the clouds measured by the GOES-16 satellite to shed more light on this phenomenon. Our results show a good qualitative correlation between the global tilted irradiance and the optical depth of the clouds present during overirradiance events. We also show that all overirradiance conditions occurred when thick clouds were present. These results indicate that the overirradiance events analyzed have been produced predominantly by reflections at the edges of thick clouds, supporting the hypothesis that the increase in global irradiance is mainly due to a substantial increase in direct irradiance.

A Method for Extracting High-Resolution Building Height Information in Rural Areas Using GF-7 Data.

Building height is important information in disaster management and damage assessment. It is also a key parameter in studies such as population modeling and urbanization. Relatively few studies have been conducted on extracting building height in rural areas using imagery from China's Gaofen-7 satellite (GF-7). In this study, we developed a method combining photogrammetry and deep learning to extract building height using GF-7 data in the rural area of Pingquan in northern China. The deep learning model DELaMa was proposed for digital surface model (DSM) editing based on the Large Mask Inpainting (LaMa) architecture. It not only preserves topographic details but also reasonably predicts the topography inside the building mask. The percentile value of the normalized digital surface model (nDSM) in the building footprint was taken as the building height. The extracted building heights in the study area are highly consistent with the reference building heights measured from the ICESat-2 LiDAR point cloud, with an R2 of 0.83, an MAE of 1.81 m and an RMSE of 2.13 m for all validation buildings. Overall, the proposed method in this paper helps to promote the use of satellite data in large-scale building height surveys, especially in rural areas.

Use of satellite data to increase public health advisories for poor air quality in the sugarcane growing region of Florida

Use of satellite data to increase public health advisories for poor air quality in the sugarcane growing region of Florida

A Comparison of Regression Methods for Inferring Near‐Surface NO2 With Satellite Data

AbstractNitrogen dioxide (NO2) is an atmospheric pollutant emitted from anthropogenic and natural sources. Human exposure to high NO2 concentrations causes cardiovascular and respiratory illnesses. The Environmental Protection Agency operates ground monitors across the U.S. which take hourly measurements of NO2 concentrations, providing precise measurements for assessing human pollution exposure but with sparse spatial distribution. Satellite‐based instruments capture NO2 amounts through the atmospheric column with global coverage at regular spatial resolution, but do not directly measure surface NO2. This study compares regression methods using satellite NO2 data from the TROPospheric Ozone Monitoring Instrument (TROPOMI) to estimate annual surface NO2 concentrations in varying geographic and land use settings across the continental U.S. We then apply the best‐performing regression models to estimate surface NO2 at 0.01° by 0.01° resolution, and we term this estimate as quasi‐NO2 (qNO2). qNO2 agrees best with measurements at suburban sites (cross‐validation (CV) R2 = 0.72) and away from major roads (CV R2 = 0.75). Among U.S. regions, qNO2 agrees best with measurements in the Midwest (CV R2 = 0.89) and agrees least in the Southwest (CV R2 = 0.65). To account for the non‐Gaussian distribution of TROPOMI NO2, we apply data transforms, with the Anscombe transform yielding highest agreement across the continental U.S. (CV R2 = 0.77). The interpretability, minimal computational cost, and health relevance of qNO2 facilitates use of satellite data in a wide range of air quality applications.

Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks

Inland water bodies are critical ecosystems that serve several functions including the provision of freshwater, regulation of climate and hydrological flows, and pollution control. Therefore, effective monitoring and management of these water resources is critical for sustainable water supply systems. This study evaluated the possible use of satellite data to estimate water quality parameters (WQPs) in an inland water body. The study also used artificial neural network (ANN) models in addition to the satellite data to determine the optimum coagulant dose for water treatment. The use of earth observations and machine learning methods has not been done extensively in developing countries, specifically, in water quality monitoring and management. The study utilized empirical multivariate regression modelling (EMRM) of the spectral reflectances from satellite data for the retrieval of Chla-a, Turbidity, and total suspended solids (TSS) concentrations in an inland water body. Using MLP-ANN modelling, the extracted spectral reflectance values from the selected sampling points in the reservoir were used as model inputs for the prediction of treated WQPs. A second MLP-ANN model was developed to predict the optimum coagulant dose required for raw water treatment. The R2 values achieved with AN model 1 were 0.81, 0.76, and 0.81 respectively for TSS, turbidity, and Chl-a, and 0.99 for the optimum coagulant dose. The study concluded that spectral reflectance from medium resolution satellite data products can be used to estimate WQPs from inland water bodies. Further, the ANN models demonstrate that extracted water quality data from satellite images can be used to inform ANN models for water quality predictions, and for the optimization of water treatment plant operations.

SATELLITE MONITORING OF DRAGOMAN MARSH FOR THE PERIOD 2018-2023

In this study, monitoring of the Dragoman Marsh was conducted for the period 2018-2023. The Dragoman Marsh is distinguished as the largest natural karst wetland area in Bulgaria. Monitoring of this natural formation is essential for studying its dynamic processes and ensuring its protection. The use of satellite data represents a key tool in this context. The data from the Sentinel 2 satellite being utilized for this purpose. Following appropriate processing of this data, classification of the object by years and seasons - spring and summer-autumn - was performed, using indices such as NDVI, NDWI, and TCT based index - NDGI. The obtained results contribute to a better understanding of the development of the wetland area and provide guidance for its conservation and management.

Satellitengestützte Gewinnung pflanzlicher Rohstoffe für die Herstellung hochwertiger pharmazeutischer Produkte

AbstractFor the worldwide sourcing of medicinal plants as a high-quality raw material for pharmaceutical processing, the use of satellite data and drones is of great importance for quality assurance, risk assessment and risk mitigation, for finding new raw material occurrences, research and development and as part of GACP-compliant documentation and audits.In many cases, recording the exact location data using GPS provides the basis for subsequent evaluations and assessments of risks to the product and the environment. In audits, they provide exact and quickly available documentation of the production site or region for wild collection, the location of primary processing and the transportation routes of the sensitive fresh matter for subsequent drying.Screenings are also based on this method and allow geographical “mapping” in order to identify certain characteristics of populations or individual plants, e. g., as eco- or chemotypes. These can be propagated and cultivated as “elite crops” for specific requirements.Remote sensing satellite images visualize structures, colours and plant species in natural habitats and enable the identification of medicinal plant occurrences in unknown areas. Recognition requires training with data that must be obtained on site.

SatelliteChangeNet: Deep Learning approach for Detection & Prediction

In geoscience, Detection is a useful method for analyzing land surface changes using data from Earth observation and for uncovering links between human activities and environmental phenomena. Detection in remote sensing is a rapidly evolving area of interest that is relevant for a number of fields. Recent years have seen a large number of publications and progress, even though the challenge is far from solved. This review focuses on deep learning applied to the task of Detection in multispectral remote-sensing images. In this work, SatelliteChangeNet addresses the growing need for an accurate and effective method to monitor and predict changes in satellite imagery, which is important for many purposes such as environmental monitoring, urban planning, and agriculture and disaster management. The changes always aim to show the struggle with the body and its different structures and lead to the search for a deep learning process. The program focuses on the use of satellite data for exploration of new areas, urban development analysis, environmental management damage, and change and prediction with important applications in agriculture. Water resources and farmland provide a lot of information about our planet. Analyzing these changes over time is important for understanding land use, environmental change, and natural hazards

Ratcheting up private standards by exploiting coopetition: The curious case of RSPO’s adoption of zero-deforestation criteria

At its November 2018 annual meeting, the Roundtable on Sustainable Palm Oil (RSPO) approved a new version of its foundational rules that advocates argued made it a zero-deforestation standard. Using a combination of over 160 key informant interviews and archival analysis, we argue that actors in the RSPO orbit made the High Carbon Stock Approach - a new method for defining forest boundaries that features heavy use of satellite data - attractive by blending cooperative and competitive strategies in a pattern known as coopetition. Critical stakeholders pushed leading companies into a competition for legitimacy that spurred innovations outside of the Roundtable setting. In turn, however, these firms also used relationships established through the RSPO Roundtable - cooperating - to elaborate guidelines and tools for defining forest areas, pursuing supply chain traceability and engaging in supplier transformation. This cooperation helped zero-deforestation strategies to emerge outside the organization. In response, stakeholders with sunk investments in RSPO membership reincorporated those external tools into the standard by framing them as consistent with the institution's fundamental logics. This case provides a compelling example of how coopetition can lead to norm emergence in a contentious governance arena, allowing a combination of internal and external actors to steer institutional development. We argue that this strategy could be employed to stimulate increased rigor in environmental standards.

Current hydrological ecosystem services and potential impacts of business-as-usual land abandonment at the national scale for Spain

This research demonstrates the opportunities and challenges for decision-makers in the use of satellite data driven hydrological model such as WaterWorld to assess current hydrological ecosystem services at the national scale and evaluate the impact of land use scenarios for the future. Spain has been selected as a case study in view of the negative consequences for hydrological ecosystem services caused by land abandonment over the last few decades, which, if action is not taken soon, will make Spain the European Union country most affected by this problem in the coming years. The results show that continued land abandonment will exacerbate the already geographically polarised situation of land abandonment of agricultural areas in the north of the country due to their low economic viability, in contrast to intensification of irrigated area in the most arid regions of Spain, leading to an increase in water balances in the already wet areas and a decrease in the arid areas. However, there is much to be gained from restoration of nature in Spain. Beyond the obvious benefits for biodiversity, climate change mitigation and recreation, abandonment of current agricultural land and its return to natural cover will lead to improvements in water quality ecosystem services. This model used, which is easily replicable to other countries given the availability of a user-friendly interface and embedded global data, can help analyse hydrological ecosystem services in any other country in the world. Like all models and measurements, WaterWorld has limitations so results should be viewed critically, but can still contribute to discussions on land management for sustainability, alongside other source of information.

Eyes in the sky on Tigray, Ethiopia - Monitoring the impact of armed conflict on cultivated highlands using satellite imagery

The war in Tigray, Ethiopia has triggered a massive humanitarian crisis, displacing millions. Yet, the impact on cultivated land and local food production remains poorly understood, impeding effective aid. Leveraging Sentinel-2 satellite imagery and a decision tree algorithm with Normalized Difference Vegetation Index (NDVI) time series, we developed a model to map well-cultivated cropland, defined as fields judged by field surveyors to have satisfactory to optimal crop condition in 2021–2022 field observations. Assessing satellite estimated well-cultivated land in highland croplands (> 1200 m a.s.l), we found a net loss of 543 sq. km (95% CI: 81 sq. km) well-cultivated land in highland croplands equivalent to ≈ 8% of the average total surveyed cropland estimate from Central Statistical Agency between 2015 and 2019 (ESS, 2023a) in potential highland cropland. The net loss was positively associated with the density of recorded conflict incidents and sub-regions with high numbers of internally displaced persons (IDPs), consistent with a causal effect of the conflict on cultivated land.Employing a two-way fixed effect model causal analysis with rainfall covariates, we quantified the impact of conflict incidents on cultivated land during the pre-war (2019/20) and in-war (2021) periods. Results indicated a ≈ 6.17 sq. km (SE: 2.06) additional loss per unit increase in conflict incidents during the growing season (June to October), eight times higher than total incidents occurring throughout the entire study period. We estimated the kilocalories lost due to loss of well-cultivated croplands in 2021 could have supported at least 90% of all recorded IDPs in Tigray as of June 2021, discounting for Western Tigray. Our study showcases the utility of satellite data, coupled with local agricultural knowledge, for timely and cost-effective information crucial for aid agencies and long-term rehabilitation initiatives in smallholder farming contexts.

A Feature Selection Method Based on Relief Feature Ranking with Recursive Feature Elimination for the Inversion of Urban River Water Quality Parameters Using Multispectral Imagery from an Unmanned Aerial Vehicle

The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the water quality parameters of small-scale water bodies due to their spatial resolution constraints and limited input features, this study focuses on the Zao River in Xi’an City. Leveraging UAV multispectral imagery, a feature selection method based on Relief Feature Ranking with Recursive Feature Elimination (Relief F-RFE) is proposed to determine the quality parameters of the typical urban pollution in water (dissolved oxygen (DO), total nitrogen (TN), turbidity, and chemical oxygen demand (COD). By constructing a potential feature set and utilizing optimal feature combinations, inversion models are developed for the four water quality parameters using three machine learning (ML) algorithms (Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM). The inversion accuracies of the different models are compared, and the spatial distribution of the four water quality parameters is analyzed. The results show that the models constructed based on UAV-based multispectral remote sensing imagery perform well in inferring the water quality parameters of the Zao River. The SVR algorithm, based on Relief F-RFE feature selection, achieves a higher accuracy, with RMSE values of 7.19 mg/L, 1.14 mg/L, 3.15 NTU, and 4.28 mg/L, respectively. The methods and conclusions of this study serve as a reference for research on the inversion of water quality parameters in urban rivers.

Satellite remote sensing for boundaries determination of agricultural lands

The main aspects of the use of satellite data for agricultural lands boundaries determination in the regions of the Russian Federation are discussed in the article. The paper also contains the information about the satellite data and the products based on it, and also proposes possible scenarios for using the results of satellite data processing.

Технології ДЗЗ у прецизійному землеробстві

One of the urgent tasks of modern agricultural production is the problem of further development for ensuring the growing demand for food and other agricultural products on an innovative platform of precision agriculture. Precision farming involves the use of geographic information technologies, earth remote sensing (ERS) technologies, the Internet of Things (IoT), big data technologies and artificial intelligence (AI) aimed at increasing crop production and reducing costs. The use of ERS technologies for precision farming tasks is due to the unprecedented availability of high spatial diversity images (spatial, spectral and temporal) and, accordingly, the ability to monitor crops, in irrigation management, nutrient input, pest and plant disease control, yield forecasting, etc. The article provides an overview of the most commonly used ERS systems used in precision farming technologies. This makes it possible to optimize the use of agricultural resources for each section of the field and improve plant productivity based on data on soil structure, pH, nutrients and yield maps, plant protection products (PPP), irrigation in accordance with soil properties, topography, meteorological conditions and a number of other factors. The use of satellite data requires further research aimed at improving the technology for storing and processing such data, namely cloud computing and machine learning. Given the certain complexity of image processing and the need for certain training of specialists, it is important to test simple but reliable algorithms for the application of remote sensing in real time, which will allow the wider implementation of ERS technologies in precision farming.

Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review

The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, slow adoption of sustainable farming, a need for climate-resilient food systems, resource inequity, and the protection of small-scale farmers’ practices. These issues are integral to food security and environmental health. Remote sensing technologies can assist precision agriculture in effectively addressing these complex problems by providing farmers with high-resolution lenses. The use of vegetation indices (VIs) is an essential component of remote sensing, which combines the variability of spectral reflectance value (derived from remote sensing data) with the growth stage of crops. A wide array of VIs can be used to classify the crops and evaluate their state and health. However, precisely this high number leads to difficulty in selecting the best VI and their combination for specific objectives. Without thorough documentation and analysis of appropriate VIs, users might find it difficult to use remote sensing data or obtain results with very low accuracy. Thus, the objective of this review is to conduct a critical analysis of the existing state of the art on the effective use of VIs for the discrimination and monitoring of several important agricultural crops (wheat, corn, sunflower, soybean, rape, potatoes, and forage crops), grasslands and meadows. This data could be highly useful for all the stakeholders involved in agricultural activities. The current review has shown that VIs appear to be suitable for mapping and monitoring agricultural crops, forage crops, meadows and pastures. Sentinel-1 and Sentinel-2 data were the most utilized sources, while some of the frequently used VIs were EVI, LAI, NDVI, GNDVI, PSRI, and SAVI. In most of the studies, an array of VIs needed to be employed to achieve a good discrimination of crops or prediction of yields. The main challenges in using VIs are related to the variation of the spectral characteristics during the vegetation period and to the similarities of the spectral signatures of various crops and semi-natural meadows. Thus, further studies are needed to establish appropriate models for the use of satellite data that would prove to have greater accuracy and provide more relevant information for the efficient monitoring of agricultural crops.

MAFormer: A New Method for Radar Reflectivity Reconstructing Using Satellite Data

Radar reflectivity plays a crucial role in detecting heavy rainfall and is an important tool for meteorological analysis. However, the coverage of a single radar is limited, leading to the use of satellite data as a complementary source. Consequently, how to bridge the gap between radar and satellite data has become a growing research focus. In this paper, we present MAFormer, a novel model for reconstructing radar reflectivity using satellite data within the Transformer framework. MAFormer consists of two modules: the Axial Local Attention Module and the Mixup Global Attention Module, which extract both local saliency and global similarity. Quantitative and qualitative experiments demonstrate the effectiveness of our proposed method. Specifically, the MAFormer model exhibits notable advancements when compared to state-of-the-art deep learning techniques. It demonstrates an improvement ranging from 0.01 to 0.05 in terms of the Heidke skill score, indicating its superior performance. Additionally, MAFormer effectively mitigates false alarm rates by approximately 0.016 to 0.04, which further highlights its enhanced accuracy and reliability.

Application of data science in the prediction of solar energy for the Amazon basin: a study case

Abstract The need for renewable energy sources has challenged most countries to comply with environmental protection actions and to handle climate change. Solar energy figures as a natural option, despite its intermittence. Brazil has a green energy matrix with significant expansion of solar form in recent years. To preserve the Amazon basin, the use of solar energy can help communities and cities improve their living standards without new hydroelectric units or even to burn biomass, avoiding harsh environmental consequences. The novelty of this work is using data science with machine-learning tools to predict the solar incidence (W.h/m²) in four cities in Amazonas state (north-west Brazil), using data from NASA satellites within the period of 2013–22. Decision-tree-based models and vector autoregressive (time-series) models were used with three time aggregations: day, week and month. The predictor model can aid in the economic assessment of solar energy in the Amazon basin and the use of satellite data was encouraged by the lack of data from ground stations. The mean absolute error was selected as the output indicator, with the lowest values obtained close to 0.20, from the adaptive boosting and light gradient boosting algorithms, in the same order of magnitude of similar references.

Evaluating the spatial patterns of U.S. urban NOx emissions using TROPOMI NO2

Satellite nitrogen dioxide (NO2) datasets are increasingly used to evaluate nitrogen oxides (NOx) emissions inventories. Such studies often use a chemical transport model or a complex statistical framework involving an assumed NO2 lifetime, which can complicate the comparison. Here, we apply a novel method to compare inventory-based NOx emissions directly to Tropospheric Monitoring Instrument (TROPOMI) NO2 data without a chemical transport model by only using measurements during stagnant wind days. We oversample the satellite data over multiple years filtering to include data when near-surface wind speeds are <3.2 m/s, and then use this filtered dataset to evaluate the spatial representativeness of the 1 × 1 km2 inventory-based Neighborhood Emission Mapping Operation (NEMO). In nine out of ten US cities evaluated, spatial r2-values between NEMO NOx emissions and TROPOMI NO2 exceeded 0.73. This suggests that the 108 spatial surrogates used by NEMO to spatially disaggregate NOx emissions from the U.S. county-level (5–200 km length scale) to the neighborhood level (∼1 km length scale) are generally appropriate. However, areas with dense intermodal facilities, such as railyards and warehouses, appear to underestimate NOx emissions. Additionally, we find some evidence that NOx emissions in wealthy communities appear to be overestimated by the standard surrogates used to disaggregate the inventory. This work provides a basis for the direct use of satellite data for evaluating the spatial patterns of urban NOx emissions inventories.

Business Intelligence through Machine Learning from Satellite Remote Sensing Data

Several cities have been greatly affected by economic crisis, unregulated gentrification, and the pandemic, resulting in increased vacancy rates. Abandoned buildings have various negative implications on their neighborhoods, including an increased chance of fire and crime and a drastic reduction in their monetary value. This paper focuses on the use of satellite data and machine learning to provide insights for businesses and policymakers within Greece and beyond. Our objective is two-fold: to provide a comprehensive literature review on recent results concerning the opportunities offered by satellite images for business intelligence and to design and implement an open-source software system for the detection of abandoned or disused buildings based on nighttime lights and built-up area indices. Our preliminary experimentation provides promising results that can be used for location intelligence and beyond.