Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets
Authors: Schucknecht Anne , Seo Bumsuk , Krämer Alexander , Asam Sarah , Atzberger Clement , Kiese Ralf 
Published: June 2022

Abstract. Grasslands are an important part of pre-Alpine and Alpine
landscapes. Despite the economic value and the significant role of
grasslands in carbon and nitrogen (N) cycling, spatially explicit
information on grassland biomass and quality is rarely available. Remotely
sensed data from unmanned aircraft systems (UASs) and satellites might be an
option to overcome this gap. Our study aims to investigate the potential of
low-cost UAS-based multispectral sensors for estimating above-ground biomass
(dry matter, DM) and plant N concentration. In our analysis, we compared two
different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three
statistical models (linear model; random forests, RFs; gradient-boosting
machines, GBMs), and six predictor sets (i.e. different combinations of raw
reflectance, vegetation indices, and canopy height). Canopy height
information can be derived from UAS sensors but was not available in our
study. Therefore, we tested the added value of this structural information
with in situ measured bulk canopy height data. A combined field sampling and
flight campaign was conducted in April 2018 at different grassland sites in
southern Germany to obtain in situ and the corresponding spectral data. The
hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were
optimized, and all model setups were run with a 6-fold cross-validation.
Linear models were characterized by very low statistical performance
measures, thus were not suitable to estimate DM and plant N concentration
using UAS data. The non-linear ML algorithms showed an acceptable regression
performance for all sensor–predictor set combinations with average (avg; cross-validated, cv)
Rcv2 of 0.48, RMSEcv,avg of 53.0 g m2, and
rRMSEcv,avg (relative) of 15.9 % for DM and with Rcv,avg2 of
0.40, RMSEcv,avg of 0.48 wt %, and rRMSEcv, avg of
15.2 % for plant N concentration estimation. The optimal combination of
sensors, ML algorithms, and predictor sets notably improved the model
performance. The best model performance for the estimation of DM
(Rcv2=0.67, RMSEcv=41.9 g m2,
rRMSEcv=12.6 %) was achieved with an RF model that utilizes all
possible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ
sensor data (Rcv2=0.47, RMSEcv=0.45 wt %,
rRMSEcv=14.2 %). DM models with the spectral input of REM
performed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors
was most influential on model performance, while the effect of the chosen ML
algorithm was generally lower. The addition of canopy height to the spectral
data in the predictor set significantly improved the DM models. In our
study, calibrating the ML algorithm improved the model performance
substantially, which shows the importance of this step.