File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Estimating Fractional Vegetation Cover from Landsat-7 ETM+ Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model

TitleEstimating Fractional Vegetation Cover from Landsat-7 ETM+ Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model
Authors
KeywordsCrop growth model
dimidiate pixel model
finer spatial resolution satellite data
fractional vegetation cover
Global LAnd Surface Satellite (GLASS) fractional vegetation cover (FVC) product
radiative transfer model
Issue Date2017
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2017, v. 55, n. 10, p. 5539-5546 How to Cite?
AbstractFractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (∼1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data (R2 = 0.6942, RMSE =0.0884), compared with the widely used dimidiate pixel model (R2 = 0.7034, RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.
Persistent Identifierhttp://hdl.handle.net/10722/321741
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Xiaoxia-
dc.contributor.authorJia, Kun-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorLi, Qiangzi-
dc.contributor.authorWei, Xiangqin-
dc.contributor.authorYao, Yunjun-
dc.contributor.authorZhang, Xiaotong-
dc.contributor.authorTu, Yixuan-
dc.date.accessioned2022-11-03T02:21:08Z-
dc.date.available2022-11-03T02:21:08Z-
dc.date.issued2017-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2017, v. 55, n. 10, p. 5539-5546-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/321741-
dc.description.abstractFractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (∼1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data (R2 = 0.6942, RMSE =0.0884), compared with the widely used dimidiate pixel model (R2 = 0.7034, RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectCrop growth model-
dc.subjectdimidiate pixel model-
dc.subjectfiner spatial resolution satellite data-
dc.subjectfractional vegetation cover-
dc.subjectGlobal LAnd Surface Satellite (GLASS) fractional vegetation cover (FVC) product-
dc.subjectradiative transfer model-
dc.titleEstimating Fractional Vegetation Cover from Landsat-7 ETM+ Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2017.2709803-
dc.identifier.scopuseid_2-s2.0-85022069521-
dc.identifier.volume55-
dc.identifier.issue10-
dc.identifier.spage5539-
dc.identifier.epage5546-
dc.identifier.isiWOS:000413653200010-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats