Journal
CANADIAN JOURNAL OF REMOTE SENSING
Volume 46, Issue 1, Pages 84-99Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/07038992.2020.1740584
Keywords
-
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available