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| DOI | 10.1016/j.rse.2020.111960 |
| Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages | |
| Diao C. | |
| 发表日期 | 2020 |
| ISSN | 00344257 |
| 卷号 | 248 |
| 英文摘要 | The phenological dynamics of crops reflect the response and feedback of agricultural systems to climate and environmental constraints, and have significant controls on carbon and nutrient cycling across the globe. Remote monitoring of crop phenological dynamics in a consistent and systematic manner is vitally crucial for optimizing the farm management activities and evaluating the agricultural resilience to extreme weather conditions and future climate change. Yet our ability to retrieve crop growing stages with satellite time series is limited. The remotely sensed phenological transition dates may not be characteristic of crop physiological growing stages. The objective of this study is to develop a remote sensing phenological monitoring framework that can reconcile satellite-based phenological measures with ground-based crop growing observations, with corn and soybean in Illinois as a case study. The framework comprises three key components: time series phenological pre-processing, time series phenological modeling, and time series phenological characterization. As an exploratory prototype, the framework retrieved a total of 56 phenological transition dates that were subsequently evaluated with the district-level ground phenological observations. The results indicated that the devised framework can adequately retrieve a wide range of physiological growing stages for corn and soybean in Illinois, with R square greater than 0.6 and RMSE less than 1 week for most stages. The devised framework largely extends the limited satellite phenological measures to a range of phenological transition dates that are characteristic of essential crop growing stages. It paves the way for formulating standard crop phenological monitoring protocols via remote sensing. The wealth of retrieved phenological characteristics open up unique opportunities to enhance our understanding of the complex mechanisms underlying the crop growth in response to varying environmental stresses, and to make more adaptive farm management strategies towards sustained agricultural development. © 2020 Elsevier Inc. |
| 英文关键词 | Agriculture; Crop progress; MODIS; Phenology; Time series analysis |
| 语种 | 英语 |
| scopus关键词 | Agricultural robots; Climate change; Crops; Cultivation; Farms; Physiology; Satellites; Time series; Agricultural development; Agricultural system; Crop growing stages; Environmental constraints; Environmental stress; Extreme weather conditions; Monitoring frameworks; Phenological observations; Remote sensing; climate change; data processing; environmental conditions; extreme event; farming system; growth rate; maize; management practice; nutrient cycling; phenology; physiological response; remote sensing; satellite data; soybean; time series analysis; Illinois; United States; Glycine max; Zea mays |
| 来源期刊 | Remote Sensing of Environment
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/179213 |
| 作者单位 | Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States |
| 推荐引用方式 GB/T 7714 | Diao C.. Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages[J],2020,248. |
| APA | Diao C..(2020).Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages.Remote Sensing of Environment,248. |
| MLA | Diao C.."Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages".Remote Sensing of Environment 248(2020). |
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