ArticleSensors (Basel, Switzerland)2019
Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Reconciling canopy light interception and photosynthetic efficiency to optimize source-sink coordination and yield formation in winter wheat.BMC plant biology · 2026Article
- A camera calibration method driven by unmanned aerial vehicles and high-precision estimation of maize SPAD values under adverse conditions.Frontiers in plant science · 2026Article
- Estimating soil profile salinity under vegetation cover based on UAV multi-source remote sensing.Scientific reports · 2025Article
- Estimation of regional-scale maize plant nitrogen content based on multi-source remote sensing data.Frontiers in plant science · 2025Article
- Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage.Sensors (Basel, Switzerland) · 2024Article
- Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.Frontiers in plant science · 2024Article
- Prediction of wheat SPAD using integrated multispectral and support vector machines.Frontiers in plant science · 2024Article
- Maize Production under Drought Stress: Nutrient Supply, Yield Prediction.Plants (Basel, Switzerland) · 2023Article
- Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images.Frontiers in plant science · 2023Article
- Effects of Basal Selenium Fertilizer Application on Agronomic Traits, Yield, Quality, and Se Content of Dryland Maize.Plants (Basel, Switzerland) · 2022Article
- Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery.Sensors (Basel, Switzerland) · 2022Article
- Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery.Plant methods · 2022Article
- Prediction of Chlorophyll Content in Multi-Temporal Winter Wheat Based on Multispectral and Machine Learning.Frontiers in plant science · 2022Article
- Evaluation of the soil profile quality of subsided land in a coal mining area backfilled with river sediment based on monitoring wheat growth biomass with UAV systems.Environmental monitoring and assessment · 2021Article
- Integration of Radiometric Ground-Based Data and High-Resolution QuickBird Imagery with Multivariate Modeling to Estimate Maize Traits in the Nile Delta of Egypt.Sensors (Basel, Switzerland) · 2021Article
- Inversion of Winter Wheat Growth Parameters and Yield Under Different Water Treatments Based on UAV Multispectral Remote Sensing.Frontiers in plant science · 2021Article
- Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta.Sensors (Basel, Switzerland) · 2020Article
- Rapid monitoring of reclaimed farmland effects in coal mining subsidence area using a multi-spectral UAV platform.Environmental monitoring and assessment · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hekou District were selected as the core test area, and 140 ground sampling points were selected. Based on the measured SPAD values and UAV multispectral images, UAV-based SPAD inversion models were constructed, and the most accurate model was selected. Second, by comparing satellite and UAV imagery, a reflectance correction for satellite imagery was performed. Finally, based on the UAV-based inversion model and satellite imagery after reflectance correction, the inversion results for SPAD values in multi-scale were obtained. The results showed that green, red, red-edge and near-infrared bands were significantly correlated with SPAD values. The modeling precisions of the best inversion model are R² = 0.926, Root Mean Squared Error (RMSE) = 0.63 and Mean Absolute Error (MAE) = 0.92, and the verification precisions are R² = 0.934, RMSE = 0.78 and MAE = 0.87. The Sentinel-2A imagery after the reflectance correction has a pronounced inversion effect; the SPAD values in the study area were concentrated between 40 and 60, showing an increasing trend from the eastern coast to the southwest and west, with obvious spatial differences. This study synthesizes the advantages of satellite, UAV and ground methods, and the proposed satellite-UAV-ground integrated inversion method has important implications for real-time, rapid and precision SPAD values collected on multiple scales.
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