Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems?

  • Amy Ferguson
  • , Catherine Murray
  • , Yared Mesfin Tessema
  • , Peter C. McKeown
  • , Louis Reymondin
  • , Ana Maria Loboguerrero
  • , Tiffany Talsma
  • , Brendan Allen
  • , Andy Jarvis
  • , Aaron Golden
  • , Charles Spillane

Research output: Contribution to journalReview articlepeer-review

Abstract

Systematic tools and approaches for measuring climate change adaptation at multiple scales of spatial resolution are lacking, limiting measurement of progress toward the adaptation goals of the Paris Agreement. In particular, there is a lack of adaptation measurement or tracking systems that are coherent (measuring adaptation itself), comparable (allowing comparisons across geographies and systems), and comprehensive (are supported by the necessary data). In addition, most adaptation measurement efforts lack an appropriate counterfactual baseline to assess the effectiveness of adaptation-related interventions. To address this, we are developing a “Biomass Climate Adaptation Index” (Biomass CAI) for agricultural systems, where climate adaptation progress across multiple scales can be measured by satellite remote sensing. The Biomass CAI can be used at global, national, landscape and farm-level to remotely monitor agri-biomass productivity associated with adaptation interventions, and to facilitate more tailored “precision adaptation”. The Biomass CAI places focus on decision-support for end-users to ensure that the most effective climate change adaptation investments and interventions can be made in agricultural and food systems.

Original languageEnglish
Article number938975
JournalFrontiers in Climate
Volume4
DOIs
Publication statusPublished - 30 Nov 2022
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • adaptation
  • agriculture
  • artificial intelligence
  • climate change
  • machine learning
  • remote sensing
  • resilience

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