English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The Normalized Difference Vegetation Index (NDVI) is widely used for Leaf Area Index (LAI) estimation. It is well documented\udthat the NDVI is extremely subject to the saturation problem when LAI reaches a high value. A new multi-angular vegetation index,\udthe Hotspot-darkspot Difference Vegetation Index (HDVI) is proposed to estimate the high density LAI. The HDVI, defined as the\uddifference between the hot and dark spot NDVI, relative to the dark spot NDVI, was proposed based on the Analytical two-layer\udCanopy Reflectance Model (ACRM) model outputs. This index is validated using both in situ experimental data in wheat and data\udfrom the multi-angular optical Compact High-Resolution Imaging Spectrometer (CHRIS) satellite. Both indices, the\udHotspot-Darkspot Index (HDS) and the NDVI were also selected to analyze the relationship with LAI, and were compared with new\udindex HDVI. The results show that HDVI is an appropriate proxy of LAI with higher determination coefficients (R2) for both the\uddata from the in situ experiment (R2=0.7342, RMSE=0.0205) and the CHRIS data (R2=0.7749, RMSE=0.1013). Our results\uddemonstrate that HDVI can make better the occurrence of saturation limits with the information of multi-angular observation, and is\udmore appropriate for estimating LAI than either HDS or NDVI at high LAI values. Although the new index needs further evaluation,\udit also has the potential under the condition of dense canopies. It provides the effective improvement to the NDVI and other\udvegetation indices that are based on the red and NIR spectral bands

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Estimation of leaf area index using an angular vegetation index based on in situ measurements and CHRIS/PROBA data