Analysis of Climate Change and Food Security

Space Research Institute NASU-NSAU


WP1 — “Data Collection, Preparation and Harmonization”

The objective of this WP is to collect, prepare and harmonize all necessary data that will be used within the project. In particular, long-term remote sensing, weather station and agricultural data will be used in this project. Land remote sensing data will be taken from the observations produced by the Advanced Very High Resolution Radiometer (AVHRR) flown on NOAA polar-orbiting satellites. Radiances in the visible (VIS), near infrared (NIR) and infrared (IR) channels will be converted to the Normalized Difference Vegetation Index (NDVI=(NIR-VIS)/(NIR+VIS)), brightness temperature (BT) and Vegetation health (VH) indices (VCI, TCI, VHI). The VH is an innovative approach to monitoring climate and agriculture. The 30-year weekly data at 4 km resolution are available at NOAA’s archive. Meteorological data will be presented by 30-year monthly precipitation and temperature for weather stations which will be aggregated over administrative regions selected for analysis. Ukrainian team has this data in the institutional archive; additional data collection and update will be required. Annual agricultural data will be represented by 30-year grain yield values for major grain crops and regions. Both PIs have the archive with the data.
Satellite data processing will include raw data collection, channel calibration (pre- and post-launch), removing of high frequency noise, calculation of climatology and VH indices. NOAA has developed new Vegetation Health (VH) methodology to monitor global drought from NOAA operational environmental satellites. In recent years, the VH has been used widely around the world and proved to be effective way of global and regional drought monitoring and impact assessment on food supply [F. Kogan, 2002: World Droughts in the New Millennium from AVHRR-based Vegetation Health Indices. Eos, 83, No 48, 26 November, 557-564; Salazar,L., F. Kogan and L. Roytman 2008: Using vegetation health indices and partial least square method for estimation of corn yield. Int. J. Rem. Sens.. Vol 29, Nos 1-2, Jan. 175-189; M. Vargas, F. Kogan, W. Guo 2009: Removing noise from AVHRR derived NDVI due to volcanic stratospheric aerosols using statistical methods. Geophysical Research Letters, Vol. 36, 1-4].
Tasks within WP1 are as follows:
1.1. Collection of in situ data.
1.2. Collection of remoter sensing data.
1.3. Data pre-processing, preparation and harmonization.
1.4. Development of archive of the 30-year satellite and in situ data records.
Milestones & Deliverables within WP1 (KO = project start, Q = quarter):
D1.1 – KO + Q2: Preliminary list of collected data and products.
D1.2 – KO + Q2: Metadata on collected data and products.
D1.3 – KO + Q3: Database design and architecture.
D1.4 – KO + Q3: First database prototype and final list of data and products.
D1.5 – KO + Q4: Interfaces and tools to enable access to and visualization of the collected data.
D1.6 – KO + Q4: Final version of database.
M1.1 – KO + Q4: Long-term remote sensing and in situ (meteorological and agricultural) data sets systems will be developed, quality control (QC) and putting the data on electronic media.
M1.2 – KO + Q4: Approximated 30-year regional trends in annual and seasonal climate (precipitation and temperature), vegetation (NDVI and BT) and agricultural (yield and production) parameters.