SenSPa – Sentinels for Sustainable Pasture Management

Grasslands are a major part of the global ecosystem, covering 37 % of the earth’s terrestrial area.

For a variety of reasons, mostly related to overgrazing and the resulting problems of soil erosion and weed encroachment, many of the world’s natural grasslands are in poor condition and showing signs of degradation.

There is general agreement that effective management of grasslands would make a significant contribution to global food security and mitigating greenhouse gas emissions. However putting in place effective monitoring systems supporting management policies is complex. Considering only the use of grasslands for pasture, governmental authorities, policy makers, land managers and livestock farmers have to take decisions about sustainable pasture management according to the rangeland productivity and status.

However, collecting field data regarding the current condition of vegetation (plant cover, forage production) is time and labour intensive.

This project is developing prototype capabilities to prove systematic information at a range of scales (local, national, regional) to support estimation of the primary grassland status indicators characteristics such as sward height, biomass, quality, phenological stage, productivity level, species composition.

Sentinel 2 measurement of the reflectance at visible and infrared wavelengths can enable discrimination of different grassland status at national and local scales, relying on the efficient coupling of remote sensing data with in-situ data for the development of efficient predictive models. Especially reflectance at the red edge part of the spectrum, where there is a rapid increase in reflectance from the red to NIR reflectance, has a strong correlation with the grass chlorophyll content of the canopy and the leaves. Inclusion of measurements made in a red-edge channel are expected to be a reliable indicator for grassland status, relating to foliar chlorophyll content, vegetation stress, plant chlorophyll concentration, and leaf area index.

Additionally, the project will investigate the use of time series of imagery acquired through the growing season to provide maximum information on yields and management. The prototype capabilities are being developed, demonstrated and validated for grassland areas in Azerbaijan.