The variation in crop growth provides critical insights for yield estimation, crop health diagnosis, precision field management, and variable-rate fertilization. This study constructs key monitoring indicators (KMIs) for corn growth based on satellite remote sensing data, along with inversion models for these growth indicators. Initially, the leaf area index (LAI) and plant height were integrated into the KMI by calculating their respective weights using the entropy weight method. Eight vegetation indices derived from Sentinel-2A satellite remote sensing data were then selected: the Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Soil-Adjusted Vegetation Index (SAVI), Red-Edge Inflection Point (REIP), Inverted Red-Edge Chlorophyll Index (IRECI), Pigment Specific Simple Ratio (PSSRa), Terrestrial Chlorophyll Index (MTCI), and Modified Chlorophyll Absorption Ratio Index (MCARI). A comparative analysis was conducted to assess the correlation of these indices in estimating corn plant height and LAI. Through recursive feature elimination, the most highly correlated indices, REIP and IRECI, were selected as the optimal dual red-edge vegetation indices. A deep neural network (DNN) model was established for estimating corn plant height, achieving optimal performance with an R² of 0.978 and a root mean square error (RMSE) of 2.709. For LAI estimation, a DNN model optimized using particle swarm optimization (PSO) was developed, yielding an R² of 0.931 and an RMSE of 0.130. KMI enables farmers and agronomists to monitor crop growth more accurately and in real-time. Finally, this study calculated the KMI by integrating the inversion results for plant height and LAI, providing an effective framework for crop growth assessment using satellite remote sensing data. This successfully enables remote sensing-based growth monitoring for the 2023 experimental field in Haicheng, making the precise monitoring and management of crop growth possible.

High-resolution Earth observation data is indispensable for remote sensing applications. However, achieving imagery with both high spatial (10m) and temporal (every 2 days) resolution presents significant challenges due to satellite, technological, and budgetary constraints. This paper introduces a novel approach by integrating MODIS and Sentinel data through an enhanced deep learning framework. The existing Enhanced Deep Convolutional Spatiotemporal Fusion Network (EDCSTFN) is augmented by incorporating multi-scale convolutions within the encoder to more effectively capture Landsat features. Additionally, standard convolutions are supplanted by a residual encoder to facilitate more comprehensive feature extraction.A Squeeze-and-Excitation (SE) channel attention module is integrated to enhance nonlinear processing capabilities and optimize channel interrelationships. These modifications have led to measurable improvements in the model's performance, including an increase in the structural similarity index by 0.2%-0.3% and a reduction in normalized global error by 0.3%-0.4%. The newly developed Modified Integrated Enhanced Deep Convolutional Spatiotemporal Fusion Network (MIEDCSTFN) utilizes the processed Landsat data from EDCSTFN as input for the DSTFN model, generating 10m resolution data.Rigorous comparative analyses and validation against multiple models confirm the enhanced accuracy and robustness of MIEDCSTFN. The resultant imagery closely approximates actual observations, making significant contributions to remote sensing time-series analysis applications such as crop monitoring, environmental change detection, and resource management. This breakthrough not only holds substantial social value but also offers extensive application potential across diverse fields.