Estimation of Crop Biomass and Leaf Area Index from Multitemporal and Multispectral Imagery Using Machine Learning Approaches. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Reisi Gahrouei, Omid; McNairn, Heather; Hosseini, Mehdi; Homayouni, Saeid
• Is Part Of: Canadian journal of remote sensing. Volume 46:Issue 1(2020)
• Page Start: 84
• Page End: 99
• Publication Date: 2020-01-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2020.1740584
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: Accurate estimation of biomass and Leaf Area Index (LAI) requires appropriate models and predictor variables. These biophysical parameters are indicative of crop productivity, and thus, are of interest in applications such as crop yield forecasting and precision farming. This study evaluated the potential of leveraging vegetation indices derived from multi-temporal RapidEye data using a machine learning approach to estimate crop biomass and LAI. Both near-infrared and red-edge based indices were considered in this study. In-situ measurements of these two parameters for three main cash crops, including canola, corn, and soybeans, were collected during a field campaign and used for model calibration and validation. Crops models were developed using the artificial neural network (ANN) and support vectors regression (SVR). Results showed that, for each crop, the SVR modeled LAI and biomass more accurately than ANN. For biomass, the SVR's Root Mean Square Errors (RMSEs) were reported as 25.22 g/m 2 for canola, 88.13 g/m 2 for corn, 5.91 g/m 2 for soybean, and 56.14 g/m 2 for all crops pooled. Similarly, for the LAI, SVR provided the best model with RMSE = 0.59 m 2 /m 2 for canola, RMSE = 0.27 m 2 /m 2 for corn, RMSE = 0.21 m 2 /m 2 for soybean, and RMSE = 0.51 m 2 /m 2 for all crops together.