Economics at your fingertips  

Multisensor fusion of remotely sensed vegetation indices using space‐time dynamic linear models

Margaret C Johnson, Brian J Reich and Josh M Gray

Journal of the Royal Statistical Society Series C, 2021, vol. 70, issue 3, 793-812

Abstract: High spatiotemporal resolution maps of surface vegetation from remote sensing data are desirable for vegetation and disturbance monitoring. However, due to the current limitations of imaging spectrometers, remote sensing datasets of vegetation with high temporal frequency of measurements have lower spatial resolution, and vice versa. In this research, we propose a space‐time dynamic linear model to fuse high temporal frequency data (MODIS) with high spatial resolution data (Landsat) to create high spatiotemporal resolution data products of a vegetation greenness index. The model incorporates the spatial misalignment of the data and models dependence within and across land cover types with a latent multivariate Matérn process. To handle the large size of the data, we introduce a fast estimation procedure and a moving window Kalman smoother to produce a daily, 30‐m resolution data product with associated uncertainty.

Date: 2021
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().

Page updated 2021-06-05
Handle: RePEc:bla:jorssc:v:70:y:2021:i:3:p:793-812