Resumen
- Gross primary productivity (GPP) is an essential parameter to assess the efficiency of terrestrial ecosystems on carbon transfer. Although GPP is regularly measured with eddy covariance (EC) systems, these are restricted to the tower footprint area, and remote sensing (RS) products have estimated GPP using multispectral vegetation indexes (VIs) from farms to whole ecosystems. Indeed, nowadays, unmanned aerial vehicle (UAV)-based RS technology is becoming more accessible. Accordingly, we propose the estimation of GPP using VIs at high spatial resolutions using UAVs and multi-spectral cameras. A small typical farm in Colombia was cultivated with carrot as our base crop. An EC system was installed to estimate GPP and was used as a reference. A total of 24 VIs from UAV-based RS products were selected and compared with the GPP of the EC system. A cross-validation process was performed, and seven VIs obtained a high R2 score (0.76–0.78). The accumulated GPP, estimated with the best index (NIRv) was 520.3 g C m−2, while the GPP-EC estimate was 580.4 g C m−2 (10.3% error). This work showed that it is possible to estimate the GPP of carrot crops using UAV-based RS, VIs, and linear regression models, which can be used in further research on GPP using UAVs.