Terrestrial applications: An intelligent Earth-sensing information system

For Abstract see A82-2214

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Automatic methods for crop classification by merging satellite radar (sentinel 1) and optical (sentinel 2) . data and artificial intelligence analysis

Land use and land cover maps can support our understanding of coupled human- environment systems and provide important information for environmental modelling and water resource management. Satellite data are a valuable source for land use and land cover mapping. However, cloud-free or weather independent data are necessary to map cloud-prone regions. Merging radar with optical images would increase the accuracy of the study. Agricultural land cover is characterized by strong variations within relatively short time intervals. These dynamics are challenging for land cover classifications on the one hand, but deliver crucial information that can be used to improve the machine learning classifier’s performance on the other hand. A parcel-based map of the main crop classes of the Netherlands was produced implementing a script on GEE and using Copernicus data. The machine-learning model used is a Random Forest Classifier. This was done by combining time series of radar and multispectral images from Sentinel 1 and Sentinel 2 satellites, respectively. The results show the potential of providing useful information delivered by entirely open source data and uses a cloud computing-based approach. The algorithm combines the two satellites data of one year in a multibands image to feed in the classifier. Standard deviation and several vegetation indexes were added in order to have more variables for each 15-day-median image composite. The process paid particular attention to time variability of mean values of each field. This will provide useful information both for understanding differences among crops and variability over the phenology of the plant. The accuracy assessment demonstrates that several crop types (i.e. corn, tulip) can be better classified with both radar and optical images while others (i.e. sugar beet, barley) have an increased accuracy with only radar. The overall accuracy of RFC with optical and radar is 76% while it is 74% if only radar is used

Earth Resources: A continuing bibliography (issue 32)

This bibliography list 580 reports, articles and other documents introduced into the NASA scientific and technical information system. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Development, implementation and evaluation of satellite-aided agricultural monitoring systems

Research supporting the use of remote sensing for inventory and assessment of agricultural commodities is summarized. Three task areas are described: (1) corn and soybean crop spectral/temporal signature characterization; (2) efficient area estimation technology development; and (3) advanced satellite and sensor system definition. Studies include an assessment of alternative green measures from MSS variables; the evaluation of alternative methods for identifying, labeling or classification targets in an automobile procedural context; a comparison of MSS, the advanced very high resolution radiometer and the coastal zone color scanner, as well as a critical assessment of thematic mapper dimensionally and spectral structure

Advanced Sensors and Applications Study (ASAS)

The present EOD requirements for sensors in the space shuttle era are reported with emphasis on those applications which were deemed important enough to warrant separate sections. The application areas developed are: (1) agriculture; (2) atmospheric corrections; (3) cartography; (4) coastal studies; (5) forestry; (6) geology; (7) hydrology; (8) land use; (9) oceanography; and (10) soil moisture. For each application area. The following aspects were covered: (1) specific goals and techniques, (2) individual sensor requirements including types, bands, resolution, etc.; (3) definition of mission requirements, type orbits, coverages, etc.; and (4) discussion of anticipated problem areas and solutions. The remote sensors required for these application areas include; (1) camera systems; (2) multispectral scanners; (3) microwave scatterometers; (4) synthetic aperture radars; (5) microwave radiometers; and (6) vidicons. The emphasis in the remote sensor area was on the evaluation of present technology implications about future systems

Crop classification based on temporal signatures of Sentinel-1 observations over Navarre province, Spain

Crop classification provides relevant information for crop management, food security assurance and agricultural policy design. The availability of Sentinel-1 image time series, with a very short revisit time and high spatial resolution, has great potential for crop classification in regions with pervasive cloud cover. Dense image time series enable the implementation of supervised crop classification schemes based on the comparison of the time series of the element to classify with the temporal signatures of the considered crops. The main objective of this study is to investigate the performance of a supervised crop classification approach based on crop temporal signatures obtained from Sentinel-1 time series in a challenging case study with a large number of crops and a high heterogeneity in terms of agro-climatic conditions and field sizes. The case study considered a large dataset on the Spanish province of Navarre in the framework of the verification of Common Agricultural Policy (CAP) subsidies. Navarre presents a large agro-climatic diversity with persistent cloud cover areas, and therefore, the technique was implemented both at the provincial and regional scale. In total, 14 crop classes were considered, including different winter crops, summer crops, permanent crops and fallow. Classification results varied depending on the set of input features considered, obtaining Overall Accuracies higher than 70% when the three (VH, VV and VH/VV) channels were used as the input. Crops exhibiting singularities in their temporal signatures were more easily identified, with barley, rice, corn and wheat achieving F1-scores above 75%. The size of fields severely affected classification performance, with ~14% better classification performance for larger fields (>1 ha) in comparison to smaller fields (<0.5 ha). Results improved when agro-climatic diversity was taken into account through regional stratification. It was observed that regions with a higher diversity of crop types, management techniques and a larger proportion of fallow fields obtained lower accuracies. The approach is simple and can be easily implemented operationally to aid CAP inspection procedures or for other purposes. © 2020 by the authors.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (MINECO/FEDER-UE) through a project (CGL2016-75217-R) and a grant (BES-2017-080560). It was also partly founded by project PyrenEOS EFA 048/15, that has been 65% cofinanced by the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra programme (POCTEFA 2014-2020)

The evolution of the earth observation system in India

The Indian Earth Observations Programme has been applications- driven and national development has been its prime motivation. From the experimental satellite Bhaskara-I launched in 1979 to the recent Cartosat-2B launched in July 2010, India's Earth Observations capability has increased manifold. The Enhancement in observation capabilities are not only in spatial, spectral, temporal and radiometric resolutions, but also in their coverage and value-added products. The sensors built over this period provide observations over land, atmosphere and oceans in visible, infrared, thermal and microwave regions of the electro magnetic spectrum. Earth Observation data has been extensively used in inventories, monitoring and conservation plans of various natural resources of the country for societal benefits. An institutional mechanism for the absorption of technology at different levels of governance in the country has been built through the concept of the National Natural Resources Management System. The Establishment of various centres/institutions in different states, central agencies as well as academic and research institutions has helped capacity building in the area of remote sensing technology and applications programmes. The paper reviews the evolution of the Earth Observation System in the country in the last three decades and briefly discusses future directions