Abstract
Ruminant fodder production in agricultural lands in latitudes above the Arctic Circle is constrained by short and hectic growing seasons with a 24-hour photoperiod and low growth temperatures. The use of remote sensing to measure crop production at high latitudes is hindered by intrinsic challenges, such as a low sun elevation angle and a coastal climate with high humidity, which influences the spectral signatures of the sampled vegetation. We used a portable spectrometer (ASD FieldSpec 3) to assess spectra of grass crops and found that when applying multivariate models to the hyperspectral datasets, results show significant predictability of yields (R2 > 0.55, root mean squared error (RMSE) < 180), even when captured under sub-optimal conditions. These results are consistent both in the full spectral range of the spectrometer (350–2500 nm) and in the 350–900 nm spectral range, which is a region more robust against air moisture. Sentinel-2A simulations resulted in moderately robust models that could be used in qualitative assessments of field productivity. In addition, simulation of the upcoming hyperspectral EnMap satellite bands showed its potential applicability to measure yields in northern latitudes both in the full spectral range of the satellite (420–2450 nm) with similar performance as the Sentinel-2A satellite and in the 420–900 nm range with a comparable reliability to the portable spectrometer. The combination of EnMap and Sentinel-2A to detect fields with low productivity and portable spectrometers to identify the fields or specific regions of fields with the lowest production can help optimize the management of fodder production in high latitudes.
Highlights
Ruminant milk and meat production above the Arctic Circle (~66.34◦ N) takes place under unique climatic conditions, with long winters and short intense growing seasons
Information on yields can help improve the management of the individual grass fields by e.g., assisting in decisions on stocking rates, fertilising, timing of harvest according to yield, identifying and intervening on low yielding areas and purchase of appropriate supplemental feed
Crop production is a parameter that has received most attention in modeling, first by applying vegetation indices (VIs) such as the simple ratio (SR) or the Normalized Difference Vegetation Index (NDVI), which have afterwards been replaced by more advanced VIs, e.g., Enhanced Vegetation Index (EVI) or Soil Adjusted Vegetation Index (SAVI), which are more resilient and offer more robustness [5,6]
Summary
Ruminant milk and meat production above the Arctic Circle (~66.34◦ N) takes place under unique climatic conditions, with long winters and short intense growing seasons. The major advantage that EnMap brings is the improvement from multispectral data (13 bands) obtained from Sentinel 2 to hyperspectral data (240 narrow bands), which is currently costly to obtain This will open up new avenues to develop more robust models to measure biomass [29], and improve the remote-sensed measurements of other parameters such as plant nitrogen [30] or water stress [31] at regional scales due to its high spectral resolution (a total of 240 bands in the 400–2450 nm range [24]). We suggest a two-stage workflow to use both Sentinel-2 and the upcoming EnMap satellite data as a screening tool to detect fields with reduced productivity, which can subsequently be assessed in further detail with very high-resolution hyperspectral tools (i.e., portable spectrometers or UAV-mounted sensors) to create detailed maps of the variability in productivity within fields
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