TY  - JOUR
A1  - Rosso, Pablo
A1  - Nendel, Claas
A1  - Gilardi, Nicolas
A1  - Udroiu, Cosmin
A1  - Chlebowski, Florent
T1  - Processing of remote sensing information to retrieve leaf area index in barley
T2  - Precision agriculture
N2  - Leaf area index (LAI) is a key variable in understanding and modeling crop-environment interactions. 
With the advent of increasingly higher spatial resolution satellites and sensors mounted on remotely piloted aircrafts (RPAs), the use of remote sensing in precision agriculture is becoming more common. 

Since also the availability of methods to retrieve LAI from image data have also drastically expanded, it is necessary to test simultaneously as many methods as possible to understand the advantages and disadvantages of each approach. 
Ground-based LAI data from three years of barley experiments were related to remote sensing information using vegetation indices (VI), machine learning (ML) and radiative transfer models (RTM), to assess the relative accuracy and efficacy of these methods.

The optimized soil adjusted vegetation index and a modified version of the Weighted Difference Vegetation Index performed slightly better than any other retrieval method. However, all methods yielded coefficients of determination of around 0.7 to 0.9. 
The best performing machine learning algorithms achieved higher accuracies when four Sentinel-2 bands instead of 12 were used. 

Also, the good performance of VIs and the satisfactory performance of the 4-band RTM, strongly support the synergistic use of satellites and RPAs in precision agriculture. One of the methods used, Sen2-Agri, an open source ML-RTM-based operational system, was also able to accurately retrieve LAI, although it is restricted to Sentinel-2 and Landsat data. 
This study shows the benefits of testing simultaneously a broad range of retrieval methods to monitor crops for precision agriculture.
KW  - leaf area index
KW  - vegetation indices
KW  - machine learning
KW  - radiative transfer models
Y1  - 2022
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/64151
SN  - 1385-2256
SN  - 1573-1618
VL  - 23
IS  - 4
SP  - 1449
EP  - 1472
PB  - Springer
CY  - Dordrecht
ER  -