The goal of the work was to estimate, quantitatively, vegetation state and productivity using AVHRR-based Vegetation Condition Index (VCI). The VCI algorithm includes application of post-launch calibration to visible channels, calculation of NDVI from channels' reflectance, removal of high-frequency noise from NDVI's annual time series, stratification of ecosystem resources, and separation of ecosystem and weather components in the NDVI value. The weather component was calculated by normalizing the NDVI to the difference of the extreme NDVI fluctuations (maximum and minimum), derived from multi-year data for each week and land pixel. The VCI was compared with wheat density measured in Kazakhstan. Six test fields were located in different climatic (annual precipitation 150 to 700 mm) and ecological (semi-desert to steppe-forest) zones with elevations from 200 to 700 m and a wide range of NDVI variation over space and season from 0.05 to 0.47. Plant density (PD) was measured in wheat fields by calculating the number of stems per unit area. PD deviation from year to year (PDD) was expressed as a deviation from median density calculated from multi-year data. The correlation between PDD and VCI for all stations was positive and quite strong (r2 > 0.75) with the Standard Errors of Estimates (SEE) of PDD less than 16 percent; for individual stations, the SEE was less than 11 percent. The results indicate that VCI is an appropriate index for monitoring weather impact on vegetation and for assessment of pasture and crop productivity in Kazakhstan. Because satellite observations provide better spatial and temporal coverage, the VCI-based system will provide efficient tools for management of water resources and the improvement of agricultural planning. This system will serve as a prototype in the other parts of the world where ground observations are limited or not available.
ASJC Scopus subject areas
- Computers in Earth Sciences