UAV as a Bridge: Mapping Key Rice Growth Stage with Sentinel-2 Imagery and Novel Vegetation Indices

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 13; p. 2180
• Main Authors: Zhang, Jianping, Zhang, Rundong, Meng, Qi, Chen, Yanying, Deng, Jie, Chen, Bingtai
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2025
• Subjects: Accuracy; Aerial surveys; Agricultural management; Agricultural production; Algorithms; Band spectra; Classification; Corn; Data collection; Data processing; Datasets; Fertilizers; Food security; Food supply; Ground truth; Growth; Growth stage; Labels; machine learning; Mapping; Methods; Monitoring; Phenology; Redundancy; Remote sensing; remote sensing collaboration; Rice; rice growth monitoring; Satellite imagery; Satellites; Security management; Sentinel-2; Spectral bands; Surveys; UAV; Unmanned aerial vehicles; Vegetation; Vegetation index; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17132180

Rice is one of the three primary staple crops worldwide. The accurate monitoring of its key growth stages is crucial for agricultural management, disaster early warning, and ensuring food security. The effective collection of ground reference data is a critical step for monitoring rice growth stages using satellite imagery, traditionally achieved through labor-intensive field surveys. Here, we propose utilizing UAVs as an alternative means to collect spatially continuous ground reference data across larger areas, thereby enhancing the efficiency and scalability of training and validation processes for rice growth stage mapping products. The UAV data collection involved the Nanchuan, Yongchuan, Tongnan, and Kaizhou districts of Chongqing City, encompassing a total area of 377.5 hectares. After visual interpretation, centimeter-level high-resolution labels of the key rice growth stages were constructed. These labels were then mapped to Sentinel-2 imagery through spatiotemporal matching and scale conversion, resulting in a reference dataset of Sentinel 2 data that covered growth stages such as jointing and heading. Furthermore, we employed 30 vegetation index calculation methods to explore 48,600 spectral band combinations derived from 10 Sentinel-2 spectral bands, thereby constructing a series of novel vegetation indices. Based on the maximum relevance minimum redundancy (mRMR) algorithm, we identified an optimal subset of features that were both highly correlated with rice growth stages and mutually complementary. The results demonstrate that multi-feature modeling significantly enhanced classification performance. The optimal model, incorporating 300 features, achieved an F1 score of 0.864, representing a 2.5% improvement over models based on original spectral bands and a 38.8% improvement over models using a single feature. Notably, a model utilizing only 12 features maintained a high classification accuracy (F1 = 0.855) while substantially reducing computational costs. Compared with existing methods, this study constructed a large-scale ground-truth reference dataset for satellite imagery based on UAV observations, demonstrating its potential as an effective technical framework and providing an effective technical framework for the large-scale mapping of rice growth stages using satellite data.