e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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Spectral matching techniques to determine historical Land-use/Land-cover (LULC) and irrigated areas using time-series 0.1-degree AVHRR pathfinder datasets


This study established spectral matching techniques (SMTs) to determine land-use and land-cover (LULC) and irrigated area classes from historical time-series (HTS-LULC) AVHRR 0.1-degree pathfinder satellite sensor data. The approach for HTS-LULC mapping and characterization was to develop "target" spectra from: (a) Recent Time Series for which LULC and irrigated area classes (RTS-LULC) were mapped using extensive ground-truth data, and (b) ideal locations, which are known endmembers even during historical time-periods of interest, as determined based on existing knowledge base including agricultural census data. The HTS-LULC for the period of 1982 to 1985 and BTS-LULC for the period of 1996 to 1999 were established using monthly continuous timeseries AVHRR mega-file data of 192 bonds (48 months 4 AVHRR bands per month) each for the HTS and RTS time periods. The study was conducted in the Krishna river basin (India), which has a large area (267,088 km(2)) with numerous irrigation projects and high population density. The quantitative and qualitative SMTs were used to identify and label HTS LULC classes. The identification and labeling process begins with qualitative spectral matching technique which visually matches the time-series NDVI spectra of known RTS-LULC classes and/or ideal endmember classes with time-series spectra of HTS-LULC classes. This helps identify classes of similar spectral characteristics in terms of shape and magnitude over time. The quantitative SMTs involved: (a) spectral correlation similarity (SCS), as a shape measure, (b) Euclidian distance (E-d), as distance measure, (c) spectral similarity value (SSV) as a combination of shape and distance measure, and (d) modified spectral angle similarity (MSAS) as a hyperangle measure. The quantitative and qualitative SMT methods and techniques lead to assigning HTS-LULC classes that match RTS-LULC names. In all, an aggregated seven HTS-LULC that were spectrally similar to the seven RTS-LULC classes and/or ideal endmember classes were identified and labeled, The SSV was the best method, followed by SCS. The validity of SMTs in identifying HTS LULC classes were determined based on calculations of irrigated areas. The 1982 to 1985 HTS irrigated area was 2,975,800 hectares which was 8.5 percent higher than the non-remote sensing based irrigated area estimate for 1984 (2,743,638 hectares) by India's Central Board of Irrigation and Power (CBIP). The results show that the irrigated areas in Krishna basin increased by 29.7 percent in 1996 to 1999 (3,860,500 hectares) relative to 1982 to 1985 (2,975,800 hectares), mostly concentrated in the northwestern portion of the basin. The results clearly imply the strengths of the spectral matching techniques in identifying and labeling LULC and irrigated area classes from the historical satellite sensor data for which little or no ground truth data is available.

  • LK
  • IN
  • CGIAR_IWMI_Int_Water_Manag_Inst (IN)
Data keywords
  • knowledge
  • knowledge based
Agriculture keywords
  • agriculture
Data topic
  • information systems
  • semantics
  • sensors
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
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    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.