Remote sensing in the visible to the mid-wave infrared spectral range for mapping of grasslands and assessment of grass biomass

Abstract

Permanent grasslands (meadows and pastures) are the most common agricultural land use type covering 34% (0.65 million hectares) of agricultural land in Latvia. The Common Agriculture Policy (CAP) stipulates that the EU Member States have to designate permanent grasslands, ensure that farmers do not convert or plough them and that the ratio of permanent grasslands to the total agricultural area does not decrease by more than 5% in order to receive support payments. Mapping of grasslands and assessment of their biomass (productivity) is of interest for evaluation of bioeconomical potential. Field sampling is the most precise approach assessment of biomass but it is expensive and timeconsuming when applied to a larger territory. In contrast, remote sensing can provide large coverage and mapping of grass biomass distribution for further use in the assessment of the available fodder for livestock and/or the optimal location for biomass-based renewable energy production sites. The study was carried out in Cesis Municipality in Latvia using airborne flying laboratory ARSENAL – the constellation of hyperspectral imagers in the visible to mid-wave infrared (400-5000 nm) spectral range, topographic LiDAR and high-resolution RGB camera for simultaneous multi-sensor data acquisition. Hyperspectral data were used for both mapping of grasslands and assessment of grass biomass. Different spectral ranges and machine learning algorithms were tested in order to find the best one. The performance of Sentinel-2 like spectral bands also was tested for further possible further use of multispectral satellite data.