Mixed model estimation of rice yield based on NDVI and GNDVI using a satellite image

Abstract

In Japan, a sustainable supply of rice should be guaranteed since it is the staple food. Therefore it is important to stabilize rice farmers’ incomes by giving them appropriate compensation even at the time of adverse weather, e.g. cold summer. In the case of compensating farmers’ incomes based on a comparison between the usual yield and estimated yield, an accurate and quick estimation of rice yield is significant to help farmers at the time of disaster. However, a manual survey requires a huge amount of effort and time to investigate all the fields where damage has been declared, especially when a largescale cool summer occurs; therefore, we propose a yield estimation system using satellite images and part of the yield data. Our system provides an accurate and quick yield estimation for the vast extent of the fields at a reasonable cost. Many rice yield estimation methodologies utilizing satellite images have been studied over the years. For example, a crop growth model-based method and deep learning based method that utilize many satellite images taken multiple times at different times have been proposed. Although we can get much information about fields from many satellite images, systems using plural images cost much and need exhaustive calibration before the estimation. Therefore, in this study we propose a yield estimation method using a single image that is taken just before the harvest time. First, we extracted the spectral values from the satellite image using field GIS data and then used a mixed model to perform rice yield estimation. Mixed model is expanded linear regression model and able to take the difference between rice varieties, such as Yumepirika and Nanatsuboshi, into accounts. In addition, we introduced two vegetation indexes, normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI), into our model as feature values. Generally, NDVI and GNDVI have a positive correlation with the volume of the plant on the field and are used for yield estimation. Of course, we could use machine learning methods, for example random forest and support vector regression. However, we adopted a mixed model considering the explainability of the results and tha fact that the number of input feature values is small. The area of interest is Asahikawa-City, Hokkaido Province, Japan. We demonstrated our method on two datasets and evaluated the performance of our model based on mean absolute error (MAE) using 10-fold cross-validation. One dataset was damaged field data acquired in 2018 (2170 fields). The other was undamaged field data acquired in 2017 (1358 fields). We used RapidEye and SPOT-6 satellite images in 2017 and 2018, respectively. Our experimental results show that our model reduces the MAE of the estimated yield by over 2.5% percent compared to conventional regression methods in each damaged field and undamaged field case.