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It is very time consuming to rely on a manual field inspection of cultivated land to archive farm crops. But with the help of satellite monitoring data on the earth\u2019s surface, it is a new vision to classify farmland based on deep learning. This article has studied the Sentinel 2 (S2) data, which are top-of-atmosphere (TOA) reflectance values at the processing level-1C (L1C) observed from some areas of Germany and France. Aiming at the problem that the interference of atmosphere and cloud coverage weakens the recognition accuracy of subsequent algorithms, a method of combining feature expansion and feature importance analysis is proposed to optimize the raw S2 data. Specifically, the new 13 spectral features are expanded based on the linear and nonlinear combination of the raw 13 spectral bands of S2. The random forest (RF) algorithm is used to score the importance of features, and the important features of each time series are selected to form a new dataset. Then, an end-to-end deep learning model has been used for training. The structure of the model is a two-layer unidirectional recurrent neural network with long short-term memory (LSTM) as the backbone. And two linear layers as the output, which form two decision-making heads, respectively, representing output classification probability and the stop decision. The results show that adding features and selecting features is beneficial for the model to improve classification accuracy and predict the classification without all of the input data. This end-to-end classification pattern with early prediction would support intelligent monitoring of farm crops with a great advantage to the implementation of various agricultural policies.<\/jats:p>","DOI":"10.3390\/rs15215203","type":"journal-article","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T11:51:43Z","timestamp":1698839503000},"page":"5203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Feature Importance Ranking of Random Forest-Based End-to-End Learning Algorithm"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7895-1899","authenticated-orcid":false,"given":"Xiaoguang","family":"Yuan","sequence":"first","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Remote Sensing, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Shiruo","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Remote Sensing, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Wei","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Remote Sensing, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi\u2019an 710071, China"},{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0677-6702","authenticated-orcid":false,"given":"Gabriel","family":"Dauphin","sequence":"additional","affiliation":[{"name":"Laboraory of Information Processing and Transmission, L2TI, Institut Galil\u00e9e, University Paris XIII, 93430 Villetaneuse, France"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113195","DOI":"10.1016\/j.rse.2022.113195","article-title":"Fifty years of Landsat science and impacts","volume":"228","author":"Wulder","year":"2022","journal-title":"Remote Sens. 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