Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints

Abstract

Accurate crop type information is of paramount importance for decision makers. This paper focuses on refining rapeseed field detection. This goal is achieved by creating high accuracy rapeseed maps using Sentinel-1 (S1) time series and secondly, by developing different solutions for mapping the rapeseed fields when there are constraints in ground samples collection. Proposed solutions include transferring a model developed over one year to other years with no re-training, and developing models with limited training samples. The research evaluates the performance of Random Forest (RF) and three deep learning (DL) algorithms: Long Short-Term Memory Fully Convolutional Network (LSTM-FCN), InceptionTime, and Multilayer Perceptron (MLP). All four algorithms were used to classify the S1 time series with a large number of ground samples from the same years for training and testing. Smaller sample sizes were then tested for the training phase (100, 300, 500 and 1000 samples in a study site of 800 km2). Model transferability is tested across years. The impact of S1 image count on transfer accuracy is examined. Additionally, the effect of the phenological shift in the rapeseed growth cycle of 15 and 30 days between the training and test years was also investigated. The findings demonstrate strong model performance when training and testing occur in the same year (F1score up to 95%). Within sample sizes of 300 to 1000, RF and InceptionTime stand out with high accuracy (F1-score>90%). When employing different years for training and testing with ample sample sizes, all four algorithms correctly classified rapeseed (F1-score between 85.5% and 92.7%). In cases of a reduced number of images, the performance of InceptionTime and LSTM-FCN decreased (16% decrease in the F1-score), while RF and MLP maintain their performance. Notably, RF outperforms DL algorithms with an F1 score of 89.1%. In the context of a phenological shift, only InceptionTime and LSTM-FCN demonstrated strong performance (F1-score between 87.7% and 92.6%).

Determining Effective Temporal Windows for Rapeseed Detection Using Sentinel-1 Time Series and Machine Learning Algorithms

“…It is noteworthy that although the comparison of S1 backscatter between the three years of this study (Figure 2) indicates a shift of less than one month in the highest peak of S1 backscatter between 2020 and other years, the F1 score, exceeding 90%, indicates that this slight shift in S1 backscatter, resulting from the growth cycle shift, does not significantly impact classification using a short time series. This discovery aligns with the results of our previous study using a complete time series [7]. Therefore, within this range of shift between years, employing the short time series of the main growth cycle enables the creation of a rapeseed map for one year using training data collected during the main growth period from another year.…”

“…For the classification stage, we adopted RF and InceptionTime algorithms; both methods are renowned for their good performance, as noted in a previous rapeseed mapping study by Maleki et al [7]. The RF algorithm is an ensemble learning method that combines the outcomes of multiple decision trees to enhance accuracy and prevent overfitting [22].…”

“…In order to classify the S1 time series images, the widely adopted random forest (RF) classifier and a deep learning-based network based on temporal (one-dimensional) convolutions, namely the InceptionTime method, will be employed. The classifier choice is based on previous related studies [7,20], where the chosen strategies have already demonstrated themselves to be robust and well suited for the crop type mapping task.…”

“…However, the performance of various deep learning and machine learning algorithms appears to be influenced by factors such as the size of the training dataset. Maleki et al [7] conducted an evaluation on the impact of training sample size on the performance of three deep learning and one machine learning algorithm for rapeseed mapping. They demonstrated that using sample sizes ranging from 300 to 1000, random forest (RF) and InceptionTime exhibited higher accuracy (F1 score > 90%) compared to the long short-term memory fully convolutional network (LSTM-FCN) and multi-layer perceptron (MLP).…”