High-Throughput Prediction of Whole Season Green Area Index in Winter Wheat With an Airborne Multispectral Sensor

Abstract

Introduction: In recent decades, the interest has grown to quantify the green area index as one of the key characteristics of crop canopies (e.g. for modelling transpiration, light interception, growth). The approach of estimating green area index based on multispectral reflection data from unmanned airborne vehicles with lightweight sensors might have the potential to deliver data with sufficient accuracy and high throughput during the whole season. Materials and Methods: We therefore examined the applicability of a recently launched drone-based multispectral system (Sequoia, Parrot) for the prediction of whole season green area index in winter wheat, with data from field trials in Northern Germany (2017, 2018 and 2019). The explanatory power of different modeling approaches to predict green area index based on multispectral data was tested: linear and non-linear regression models, multivariate techniques, and machine learning algorithms. Further, different predictors were implemented in these models: multispectral data as raw bands and as ratios. Additionally, a new approach for the evaluation of green area index predictions during senescence is introduced. It is shown that a robust calibration during growth phase is applicable during senescence as well. Results and Discussion: A linear model which includes all four wavebands provided by the sensor in three ratios (VIQUO) and a Support Vector Machine (SVM) algorithm allow a reliable and sufficiently accurate whole season prediction. The VIQUO-model is recommended as the best model, as it is precise but still relatively simple, thus easier to communicate and to apply than the SVM. The integrated values of predicted green area indices in an independent trial are highly correlated with their final biomass (R 2 : VIQUO = 0.84, SVM = 0.85) which represents the process of radiation interception, one of the determining factors of growths. This is an indicator for both, a robust model calibration and a high potential of the tested multispectral system for agricultural research and crop management.

Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR

“…Ultimately, UAS provide more flexibility, with compact sizes and accessibility that can provide increased temporal resolution. Being independent of cloud cover, UAS is sometimes the only means of obtaining the appropriate temporal resolution desired for a growing season [39].…”

Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment

“…The GAI-calibrations applied to the UAV data are based on extensive destructive sampling in 11 different cultivars, 7 nitrogen levels and 6 sowing densities, described in more detail by Bukowiecki et al [ 16 ]. During three years, the manually determined GAI on the 0.25 m 2 -sampling spots was correlated with UAV-based spectral data with a spatial resolution within the centimeter range.…”

“…The UAV-based GAI was calculated using the empirical VI models of Bukowiecki et al [ 16 ] and further used as reference data for the satellite-derived estimates. For calibration and evaluation purposes, UAV and satellite data were assigned to each other if their acquisition dates differed not more than five days as the plant development during this period can be neglected.…”

“…GAI can be assessed by multispectral measurements, whether by simple linear regression between GAI and different vegetation indices (VI) or by multivariate methods [ 10 , 11 , 12 ]. Even though different effects impeding GAI-calibration are discussed in the literature, e.g., [ 13 , 14 , 15 , 16 ], quite stable GAI-calibrations for cultivars of the same crop over the whole season are achievable [ 17 , 18 ]. However, the parameters of the empirical model equations may vary with crop, background effects, spatial resolution and VI [ 19 , 20 , 21 ].…”

“…Further refined, it should additionally enable the contextualization and comparison of UAV- and satellite-based spectral data within the application of yield prediction. The main starting points of this study are to transfer the approach of Revill et al [ 27 ] to a larger data volume, including multiple years and many acquisition dates, and to make use of well-calibrated UAV-based multispectral VI algorithms [ 16 ].…”