Green area index from an unmanned aerial system over wheat and rapeseed crops

By Aleixandre Verger, N. Vigneau, C. Chéron, J. Gilliot, Alexis Comar, Frédéric Baret. 2014

176 citations recorded by the source corpus.

Unmanned airborne systems (UAS) technology opens new horizons in precision agriculture for effective characterization of the variability in crop state at high spatial resolution and high revisit frequency. Green area index (GAI) is a key agronomic variable involved in many processes and used for decision making. This paper describes a physically based algorithm for estimating GAI from UAS acquisitions. The UPS plane platform used here was equipped with four cameras in green (550 nm), red (660 nm), red-edge (735 nm) and near infrared (790 nm). It provided multiple views by overlapping images along and between the tracks. A lookup table was generated to invert the PROSAIL radiative transfer model using the reflectances in the four bands and the specific view-sun angles for each individual image. The average of the ensemble of solutions corresponding to the individual images allows regularizing the solutions of the ill posed inverse problem. Around six images were required to get stable GAI estimates and the corresponding root mean square error (RMSE) value was used as a proxy for the associated uncertainties. Comparison with ground based measurements showed that the accuracy of UAS GAI estimates over wheat and rapeseed crops was around 0.2 in terms of RMSE. The use of normalized reflectances compared to absolute reflectances improved the performances of GAI estimates (0.17 compared to 0.26 GAI in terms of RMSE) particularly under unstable illumination conditions. High repeatability in the estimates from UPS flights at different acquisition times was observed. The use of the red-edge band normalized (absolute) reflectances brought 30% (10%) improvement of accuracy for the low to medium GAI values.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d0837a98c6a706150f98a0e66dc6c221c724daf0

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Citations in the corpus

Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images (2017, background): “They are often based on row detection and assume the weeds to be the vegetation between rows (Bossu et al. 2009; Jones et al. 2009; Peña et al. 2013; Vioix et al. 2003).”
Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review (2020, background): “Among all, vegetation indices and normalized difference vegetation index (NDVI), as well as spectral vegetation index (SVI), green area index (GAI) and high-resolution imagery have been evaluated to predict grain yield [93] , to monitor wheat breeding in a large trial [94] , to monitor key developme”
Unmanned Aircraft System (UAS) Technology and Applications in Agriculture (2019, methodology): “This method relies on estimating the biophysical variable called leaf area index (LAI) or green area index (GAI) which is generally performed through the use of lookup tables; though, iterative optimization techniques and artificial neural networks are also used [45,46].”
Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping (2017, methodology): “These distributions were bounded by the minimum and maximum values presented in Table 4 and determined according to the literature (Darvishzadeh et al. 2011; Dorigo 2012; Duan et al. 2014; Jacquemoud et al. 1995; Verger et al. 2014) and the reference measurements displayed in Table 2.”