Photosynthetically Active Radiation and Foliage Clumping Improve Satellite-Based NIRv Estimates of Gross Primary Production
文献类型：期刊论文
| 作者 | Filella, Iolanda1,2; Descals, Adria1,2; Balzarolo, Manuela3; Yin, Gaofei4; Verger, Aleixandre1,5; Fang, Hongliang6,7; Penuelas, Josep1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 15期号:8页码:12 |
| 关键词 | GPP clumping index NIRv photosynthetically active radiation evergreen needleleaf forest vegetation cover type |
| DOI | 10.3390/rs15082207 |
| 通讯作者 | Filella, Iolanda(i.filella@creaf.uab.cat) |
| 英文摘要 | Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon flux density (PPFD) information to NIRv would improve estimates of GPP and that (ii) a further improvement would be obtained by incorporating the estimates of radiation distribution in the canopy provided by the foliar clumping index (CI). Thus, we used GPP data from FLUXNET sites to test these possible improvements by comparing the performance of a model based solely on NIRv with two other models, one combining NIRv and PPFD and the other combining NIRv, PPFD and the CI of each vegetation cover type. We tested the performance of these models for different types of vegetation cover, at various latitudes and over the different seasons. Our results demonstrate that the addition of daily radiation information and the clumping index for each vegetation cover type to the NIRv improves its ability to estimate GPP. The improvement was related to foliage organization, given that the foliar distribution in the canopy (CI) affects radiation distribution and use and that radiation drives productivity. Evergreen needleleaf forests are the vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information, likely as a result of their greater radiation constraints. Vegetation type was more determinant of the sensitivity to PPFD changes than latitude or seasonality. We advocate for the incorporation of PPFD and CI into NIRv algorithms and GPP models to improve GPP estimates. |
| WOS关键词 | LEAF-AREA INDEX ; CENTRAL GRASSLAND REGION ; NET PRIMARY PRODUCTION ; TERRESTRIAL ; FOREST ; CO2 ; REFLECTANCE ; TEMPERATURE ; REDUCTION ; RETRIEVAL |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000977222600001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/197049]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Filella, Iolanda |
| 作者单位 | 1.CREAF, Barcelona 08193, Spain 2.UAB, CSIC, Global Ecol Unit, CREAF, Barcelona 08193, Spain 3.Univ Antwerp, Dept Biol, PLECO, B-2610 Antwerp, Belgium 4.Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Peoples R China 5.UV GV, CSIC, CIDE, Valencia 46113, Spain 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, LREIS, Beijing 100101, Peoples R China 7.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Filella, Iolanda,Descals, Adria,Balzarolo, Manuela,et al. Photosynthetically Active Radiation and Foliage Clumping Improve Satellite-Based NIRv Estimates of Gross Primary Production[J]. REMOTE SENSING,2023,15(8):12. |
| APA | Filella, Iolanda.,Descals, Adria.,Balzarolo, Manuela.,Yin, Gaofei.,Verger, Aleixandre.,...&Penuelas, Josep.(2023).Photosynthetically Active Radiation and Foliage Clumping Improve Satellite-Based NIRv Estimates of Gross Primary Production.REMOTE SENSING,15(8),12. |
| MLA | Filella, Iolanda,et al."Photosynthetically Active Radiation and Foliage Clumping Improve Satellite-Based NIRv Estimates of Gross Primary Production".REMOTE SENSING 15.8(2023):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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