Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought
文献类型：期刊论文
| 作者 | Sobejano-Paz, Veronica1,2,3; Mikkelsen, Teis Norgaard1; Baum, Andreas4; Mo, Xingguo2,3; Liu, Suxia2,3; Koppl, Christian Josef1; Johnson, Mark S.5; Gulyas, Lorant4; Garcia, Monica1,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020-10-01 |
| 卷号 | 12期号:19页码:32 |
| 关键词 | crop phenotyping hydraulic traits leaf conductance phenology photosynthetic CO2 assimilation rate PLSR shortwave radiation transpiration rate temperature water stress |
| DOI | 10.3390/rs12193182 |
| 通讯作者 | Sobejano-Paz, Veronica(vepa@env.dtu.dk) |
| 英文摘要 | During water stress, crops undertake adjustments in functional, structural, and biochemical traits. Hyperspectral data and machine learning techniques (PLS-R) can be used to assess water stress responses in plant physiology. In this study, we investigated the potential of hyperspectral optical (VNIR) measurements supplemented with thermal remote sensing and canopy height (h(c)) to detect changes in leaf physiology of soybean (C-3) and maize (C-4) plants under three levels of soil moisture in controlled environmental conditions. We measured canopy evapotranspiration (ET), leaf transpiration (T-r), leaf stomatal conductance (g(s)), leaf photosynthesis (A), leaf chlorophyll content and morphological properties (h(c) and LAI), as well as vegetation cover reflectance and radiometric temperature (T-L,T-Rad). Our results showed that water stress caused significant ET decreases in both crops. This reduction was linked to tighter stomatal control for soybean plants, whereas LAI changes were the primary control on maize ET. Spectral vegetation indices (VIs) and T-L,T-Rad were able to track these different responses to drought, but only after controlling for confounding changes in phenology. PLS-R modeling of g(s), T-r, and A using hyperspectral data was more accurate when pooling data from both crops together rather than individually. Nonetheless, separated PLS-R crop models are useful to identify the most relevant variables in each crop such as T-L,T-Rad for soybean and h(c) for maize under our experimental conditions. Interestingly, the most important spectral bands sensitive to drought, derived from PLS-R analysis, were not exactly centered at the same wavelengths of the studied VIs sensitive to drought, highlighting the benefit of having contiguous narrow spectral bands to predict leaf physiology and suggesting different wavelength combinations based on crop type. Our results are only a first but a promising step towards larger scale remote sensing applications (e.g., airborne and satellite). PLS-R estimates of leaf physiology could help to parameterize canopy level GPP or ET models and to identify different photosynthetic paths or the degree of stomatal closure in response to drought. |
| WOS关键词 | WATER-STRESS DETECTION ; NARROW-BAND INDEXES ; VEGETATION INDEXES ; CHLOROPHYLL CONTENT ; RED EDGE ; REFLECTANCE ; LEAF ; TEMPERATURE ; TRAITS ; MODEL |
| 资助项目 | Sino-Danish Center (SDC) ; EU ; Innovation Fund Denmark (IFD) ; National Key R&D program of China[2018YEE0106500] ; IFD China WaterSense project |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000587189600001 |
| 资助机构 | Sino-Danish Center (SDC) ; EU ; Innovation Fund Denmark (IFD) ; National Key R&D program of China ; IFD China WaterSense project |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/156491]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Sobejano-Paz, Veronica |
| 作者单位 | 1.Tech Univ Denmark, Dept Environm Engn, DK-2800 Lyngby, Denmark 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Environm & Resources, Sino Danish Ctr, Beijing 101408, Peoples R China 4.Tech Univ Denmark, Dept Appl Math & Comp Sci, DK-2800 Lyngby, Denmark 5.Univ British Columbia, Inst Resources Environm & Sustainabil, Vancouver, BC V6T 1Z4, Canada |
| 推荐引用方式 GB/T 7714 | Sobejano-Paz, Veronica,Mikkelsen, Teis Norgaard,Baum, Andreas,et al. Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought[J]. REMOTE SENSING,2020,12(19):32. |
| APA | Sobejano-Paz, Veronica.,Mikkelsen, Teis Norgaard.,Baum, Andreas.,Mo, Xingguo.,Liu, Suxia.,...&Garcia, Monica.(2020).Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought.REMOTE SENSING,12(19),32. |
| MLA | Sobejano-Paz, Veronica,et al."Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought".REMOTE SENSING 12.19(2020):32. |
入库方式： OAI收割
来源：地理科学与资源研究所
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