Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield

Xingshuo Peng,Yi Wang,Jianyi Ao,Wenting Han

doi:10.3390/rs13061094

Abstract

In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated.

Highlights

The materials and methods related to this research are presented including the study site and experimental design, the unmanned aerial vehicle (UAV) camera system for multispectral image acquisition, Pix4D software for image preprocessing, and the crop growth model
Four models were tested for leaf area index (LAI) estimation using reflectance divided a training set and aofverification set.reflectance
The linear model, exponential model, logarithmic model and quadratic polynomial model were constructed, and the optimal model was selected as the estimation model of leaf area index

Summary

Introduction

The goal of precision agriculture is to optimize the inputs and outputs of field operations to maximize economic profits while maintaining environmental sustainability [1]. The variation of crop yield in spatial information is very important for precision agriculture. Solving the problem of low-productivity areas in a field can lead directly to an increase in agricultural profits. Remote sensing technology has long been considered as an effective means to support precision agriculture, as it can provide multitemporal information on a large area of crop growth [2,3,4,5]. The spatial and temporal resolution of satellite images mean that they still cannot provide timely detailed information on field changes for business applications [10]

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Conclusion