Optimization of Spatial Sampling in Satellite–UAV Integrated Remote Sensing: Rationale and Applications in Crop Monitoring
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| Publicado en: | Remote Sensing vol. 17, no. 23 (2025), p. 3895-3914 |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17233895 |2 doi | |
| 035 | |a 3280962396 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Zhao, Zhen |u Digital Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070, China | |
| 245 | 1 | |a Optimization of Spatial Sampling in Satellite–UAV Integrated Remote Sensing: Rationale and Applications in Crop Monitoring | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>Layout configuration of satellite–UAV integrated remote sensing was transformed into a spatial sampling problem. <list-item> An SSO (spatial sampling optimization) model was proposed. </list-item> What is the implication of the main finding? <list list-type="bullet"> <list-item> </list-item>Sampling efficiency requires considering both cost and accuracy. <list-item> The SSO-optimized plan improved efficiency by at least 38.7% over conventional plans. </list-item> Satellite and UAV-based remote sensing have been widely used for agricultural systems monitoring jointly. How to quantitatively optimize the efficiency of integrating these two techniques remains largely understudied. To address this gap, we, for the first time, formulate the configuration of satellite–UAV integrated system as a spatial sampling optimization problem and propose an SSO (spatial sampling optimization) model that jointly optimizes the spatial locations and flight paths of UAV sampling within the satellite monitoring area. The SSO model enables maximizing the accuracy of monitoring under a given cost constraint. We obtained comprehensive data in rapeseed fields and conducted experiments based on the SSO model. We compared the sampling effectiveness of the SSO model with that of simple random sampling, systematic sampling, equal stratified sampling and Neyman stratified sampling. The results showed that the SSO-optimized plan had the highest sampling efficiency, which was at least 38.7% higher than that of the best-performing conventional method (Neyman stratified sampling). Under the same cost constraint, the SSO-optimized sampling scheme can have 11.1% more sampling points than the conventional sampling scheme. The Elite Genetic Algorithm (EGA) performed well in solving the SSO model. The error of the SSO-optimized scheme was reduced by 27.3% and the sampling distance was reduced by 7000 to 8000 m on average. In conclusion, the proposed SSO model helps to optimize the configuration of satellite–UAV integrated remote sensing, thereby improving the cost-effectiveness of agricultural monitoring systems. We call for considering cost constraints and increasing efficiency in agricultural system monitoring and government censuses in the future. | |
| 653 | |a Accuracy | ||
| 653 | |a Rapeseed | ||
| 653 | |a Optimization | ||
| 653 | |a Efficiency | ||
| 653 | |a Remote sensing | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Sampling error | ||
| 653 | |a Monitoring | ||
| 653 | |a Random sampling | ||
| 653 | |a Cost effectiveness | ||
| 653 | |a Configurations | ||
| 653 | |a Farming systems | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Statistical sampling | ||
| 653 | |a Design | ||
| 653 | |a Remote sensing systems | ||
| 653 | |a Satellites | ||
| 653 | |a Algorithms | ||
| 653 | |a Constraints | ||
| 653 | |a Traveling salesman problem | ||
| 653 | |a Parameter estimation | ||
| 700 | 1 | |a Xiong Hang |u Digital Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070, China | |
| 700 | 1 | |a Yu, Yawen |u Digital Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070, China | |
| 700 | 1 | |a Xu Baodong |u Digital Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070, China | |
| 700 | 1 | |a Zhang, Jian |u Digital Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070, China | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 23 (2025), p. 3895-3914 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3280962396/abstract/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3280962396/fulltextwithgraphics/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3280962396/fulltextPDF/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |