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

Accurate monitoring of heavy metal stress in crops is of great importance to assure agricultural productivity and food security, and remote sensing is an effective tool to address this problem. However, given that Earth observation instruments provide data at multiple scales, the choice of scale for use in such monitoring is challenging. This study focused on identifying the characteristic scale for effectively monitoring heavy metal stress in rice using the dry weight of roots (WRT) as the representative characteristic, which was obtained by assimilation of GF-1 data with the World Food Studies (WOFOST) model. We explored and quantified the effect of the important state variable LAI (leaf area index) at various spatial scales on the simulated rice WRT to find the critical scale for heavy metal stress monitoring using the statistical characteristics. Furthermore, a ratio analysis based on the varied heavy metal stress levels was conducted to identify the characteristic scale. Results indicated that the critical threshold for investigating the rice WRT in monitoring studies of heavy metal stress was larger than 64 m but smaller than 256 m. This finding represents a useful guideline for choosing the most appropriate imagery.

Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model