Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery

2022 ◽  
Vol 305 ◽  
pp. 117834
Author(s):  
Alfredo Nespoli ◽  
Alessandro Niccolai ◽  
Emanuele Ogliari ◽  
Giovanni Perego ◽  
Elena Collino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Machine Learning Techniques ◽  
Solar Irradiation ◽  
Cloud Type ◽  
Learning Techniques ◽  
Type Classification

Experiments on Machine Learning Techniques for Soil Classification Using Sentinel-2 Products

2020 ◽  
Author(s):  
Victor Bacu ◽  
Teodor Stefanut ◽  
Dorian Gorgan
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Data ◽  
Field Measurements ◽  
Soil Classification ◽  
Machine Learning Techniques ◽  
Classification Models ◽  
Spatial Features ◽  
Learning Techniques ◽  
Sentinel 2

<p>Agricultural management relies on good, comprehensive and reliable information on the environment and, in particular, the characteristics of the soil. The soil composition, humidity and temperature can fluctuate over time, leading to migration of plant crops, changes in the schedule of agricultural work, and the treatment of soil by chemicals. Various techniques are used to monitor soil conditions and agricultural activities but most of them are based on field measurements. Satellite data opens up a wide range of solutions based on higher resolution images (i.e. spatial, spectral and temporal resolution). Due to this high resolution, satellite data requires powerful computing resources and complex algorithms. The need for up-to-date and high-resolution soil maps and direct access to this information in a versatile and convenient manner is essential for pedology and agriculture experts, farmers and soil monitoring organizations.</p><p>Unfortunately, the satellite image processing and interpretation are very particular to each area, time and season, and must be calibrated by the real field measurements that are collected periodically. In order to obtain a fairly good accuracy of soil classification at a very high resolution, without using interpolation methods of an insufficient number of measurements, the prediction based on artificial intelligence techniques could be used. The use of machine learning techniques is still largely unexplored, and one of the major challenges is the scalability of the soil classification models toward three main directions: (a) adding new spatial features (i.e. satellite wavelength bands, geospatial parameters, spatial features); (b) scaling from local to global geographical areas; (c) temporal complementarity (i.e. build up the soil description by samples of satellite data acquired along the time, on spring, on summer, in another year, etc.).</p><p>The presentation analysis some experiments and highlights the main issues on developing a soil classification model based on Sentinel-2 satellite data, machine learning techniques and high-performance computing infrastructures. The experiments concern mainly on the features and temporal scalability of the soil classification models. The research is carried out using the HORUS platform [1] and the HorusApp application [2], [3], which allows experts to scale the computation over cloud infrastructure.</p><p> </p><p>References:</p><p>[1] Gorgan D., Rusu T., Bacu V., Stefanut T., Nandra N., “Soil Classification Techniques in Transylvania Area Based on Satellite Data”. World Soils 2019 Conference, 2 - 3 July 2019, ESA-ESRIN, Frascati, Italy (2019).</p><p>[2] Bacu V., Stefanut T., Gorgan D., “Building soil classification maps using HorusApp and Sentinel-2 Products”, Proceedings of the Intelligent Computer Communication and Processing Conference – ICCP, in IEEE press (2019).</p><p>[3] Bacu V., Stefanut T., Nandra N., Rusu T., Gorgan D., “Soil classification based on Sentinel-2 Products using HorusApp application”, Geophysical Research Abstracts, Vol. 21, EGU2019-15746, 2019, EGU General Assembly (2019).</p>

scholarly journals Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat

Climate ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 5 ◽  
Author(s):  
Vivek Shandas ◽  
Jackson Voelkel ◽  
Joseph Williams ◽  
Jeremy Hoffman
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Machine Learning Techniques ◽  
Metropolitan Region ◽  
Landscape Features ◽  
Ambient Temperatures ◽  
Air Temperatures ◽  
Learning Techniques ◽  
Urban Heat

The emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the cities of Baltimore, Maryland, and Richmond, Virginia, and the District of Columbia using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90th percentile of historic averages. Our results suggest that the urban microclimate was highly variable across all of the cities—with differences of up to 10 °C between coolest and warmest locations at the same time—and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data.

scholarly journals Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

2019 ◽  
Vol 11 (6) ◽  
pp. 617 ◽  
Author(s):  
Sidrah Hafeez ◽  
Man Wong ◽  
Hung Ho ◽  
Majid Nazeer ◽  
Janet Nichol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Satellite Data ◽  
Coastal Waters ◽  
Machine Learning Techniques ◽  
Blue Band ◽  
Chl A ◽  
Learning Techniques ◽  
Reflectance Data

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

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