Estimating vegetation properties for hydrological or climate modelling

Owned by Maiwenn Herpe
Last updated: May 31, 2021 by Conny
2 min read

Satellite(s)

 

e.g., MODIS.

Monitoring element

Surface reflectance of multispectral near-infrared and red bands.

Satellite(s)

 

e.g., MODIS.

Monitoring element

Surface reflectance of multispectral near-infrared and red bands.

Description technique

The MCD15A3H V6 level 4, Combined Fraction of Photosynthetically Active Radiation (FPAR), and Leaf Area Index (LAI) product is a 4-day composite data set with 500 meter pixel size. The algorithm chooses the "best" pixel available from all the acquisitions of both MODIS sensors located on NASA's Terra and Aqua satellites from within the 4-day period.

Accuracy / Resolution

500 m.

Case study

Westerhoff et al (2018) used MODIS-based LAI estimates as one of the datasets in their recharge model to incorporate the effect of vegetation transpiration in New Zealand.

Also fits domain

Land

Limitations

Steep learning curve for a non-satellite expert, e.g., using the Google Earth Engine requires some investment in time.

Benefits

Sub-weekly, free.

Applicability for Northland

Yes, applicable as regionwide dynamic coverage of vegetation properties for use in hydrologic modeling.

Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature, and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive.

Publication references

Westerhoff, R., White, P., Rawlinson, Z., 2018. Incorporation of Satellite Data and Uncertainty in a Nationwide Groundwater Recharge Model in New Zealand. Remote Sensing 10, 58.

https://www.mdpi.com/2072-4292/10/1/58/htm


Fang H, Baret F, Plummer S, Schaepman-Strub G. 2019. An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications. Reviews of Geophysics. 57(3):739-799. doi:10.1029/2018rg000608.

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018RG000608

Other comments or information

Datset available from 2002-2021.

https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD15A3H