Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Synthetic Aperture Radar (SAR) is a kind of microwave remote sensing equipment, which has the advantage of all-weather and all-day, and can realize large-scale and periodic crop phenological monitoring. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. A stacking ensemble learning algorithm using time series Sentinel-1A SAR images for winter wheat phenology classification was proposed in this paper based on multiple machine learning models, including Random Forest (RF), Support Vector Machine (SVM), K-nearest Neighbor(K-NN), Naive Bayes (NB) and BP Neural Network (BP) models. The experimental results showed that, comparing with each single model, the stacking ensemble learning algorithm proposed in this paper had the optimal performance, with the highest overall recognition accuracy of 81.40%, demonstrating its effectiveness and application potential for winter wheat phenology identification.
KEYWORDS: Data modeling, Yield improvement, Remote sensing, Vegetation, Agriculture, Solar radiation models, Solar radiation, Atmospheric modeling, Process modeling, Meteorology
Accurately and timely grasping agricultural information at the regional scale helps to solve food security issues and formulate agricultural policies. Remote sensing images have the advantages of wide monitoring range and the ability to eliminate human interference. In recent years, they have been increasingly valued in crop yield estimation. The CASA model is used to estimate the net primary productivity (NPP) of crops and then combine it with the harvest index (HI) to estimate crop yields. However, most studies use a fixed HI for yield estimation, which can lead to low accuracy. In this context, the HI is studied and improved for winter wheat yield prediction in this research. The main contents are as follows: Firstly, time-series Sentinel-2 optical data and meteorological data were used as inputs of the CASA model to calculate the NPP of winter wheat. Then, HI was calculated using the fieldwork data and a method was proposed to improve it due to its geographic location-related characteristics. Finally, the calculated NPP and improved HI were used to estimate the winter wheat yield. The experimental results prove that the proposed method has higher accuracy than original methods, with determination coefficient(R2) of 0.595, root mean square error(RMSE) of 793.4 kg/ha, and mean absolute error(MAE) of 659.53 kg/ha.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.