Combining Sentinel-1 and -3 Imagery for Retrievals of Regional Multitemporal Biophysical Parameters Under a Deep Learning Framework

Abstract

Regions with excessive cloud cover lead to limited feasibility of applying optical images to monitor crop growth. In this study, we built an upsampling moving window network for regional crop growth monitoring (UMRCGM) model to estimate the two key biophysical parameters (BPs), leaf area index (LAI) and canopy chlorophyll content (CCC) during the main growth period of winter wheat by using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-3 optical images. Sentinel-1 imagery is unaffected by cloudy weather and Sentinel-3 imagery has a wide width and short revisit period, the organic combination of the two will greatly improve the ability to monitor crop growth at a regional scale. The impact of two different types of SAR information (intensity and polarization) on the estimation of the two BPs was further analyzed. The UMRCGM model optimized the correspondence between inputs and outputs, it had more accurate LAI and CCC estimates compared with the three classical machine learning models, and had the highest accuracy at the green-up stage of winter wheat, followed by the jointing stage and the heading-filling stage, and the lowest accuracy was found at the milk maturity stage. The estimation accuracies of CCC were slightly higher than that of LAI for the first three growth stages of winter wheat, while lower than that of LAI for the milk maturity stage. This study proposes a new method for regional BPs (especially for CCC) estimation by combining SAR and optical imagery with large differences in spatial resolution under a deep learning framework.

Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing

“…To improve the accuracy and stability of estimation, machine learning algorithms, especially deep learning methods, have been widely applied in the study of remote-sensing-based estimation of crop growth parameters. Deep learning algorithms, by constructing deep neural network models, can automatically learn high-level feature representations from remote sensing data and optimize parameters through large-scale training samples, thereby enhancing estimation accuracy and generalization ability [18,19]. The advantages of deep learning lie in its ability to model nonlinear relationships in remote sensing data and perform feature extraction and combination through multilayer network structures, overcoming the limitations of traditional methods in feature extraction [20,21].…”

“…Deep learning algorithms typically require a large amount of training samples and high computational resources [23,24]. Additionally, the selection and tuning of hyperparameters can be complex [18]. In order to further improve the performance and robustness of estimation, stacking ensemble learning methods have been introduced into remote sensing estimation research.…”