<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The increase in demand for food and the need to predict the impact of a warming climate on vegetation makes it critical that the best tools for assessing crop production are found. Chlorophyll fluorescence (ChlF) has been proposed as a direct indicator of photosynthesis and plant condition. The aim of this paper is to study the feasibility of estimating ChlF from spectral vegetation indices derived from Sentinel-2, in order to monitor crop stress and investigate ChlF changes in response to surface temperatures and meteorological observations. The regressions between thirty three Sentinel-2-derived VIs, and ChlF measured on the ground were evaluated in order to estimate the best predictors of ChlF. The r-Pearson correlation and polynomial linear regression were used. For maize, the highest correlation between ChlF and VIs were found for NDII (r=0.65) and for SIPI (r=−0.68). The weakest relationship between VIs and ChlF were found for sugar beets. Despite this, it should be noted that the highest correlation for sugar beets appeared for EVI (r=0.45) and S2REP (r=0.43). The results of this study indicate the need for a synergy of low and high resolution satellite data that will enable a more detailed analysis for estimating fluorescence and its relation to climatic conditions, environmental aspects, and VIs derived from satellite images.</p>
</abstract>

The increase in demand for food and the need to predict the impact of a warming climate on vegetation makes it critical that the best tools for assessing crop production are found. Chlorophyll fluorescence (ChlF) has been proposed as a direct indicator of photosynthesis and plant condition. The aim of this paper is to study the feasibility of estimating ChlF from spectral vegetation indices derived from Sentinel-2, in order to monitor crop stress and investigate ChlF changes in response to surface temperatures and meteorological observations. The regressions between thirty three Sentinel-2-derived VIs, and ChlF measured on the ground were evaluated in order to estimate the best predictors of ChlF. The r-Pearson correlation and polynomial linear regression were used. For maize, the highest correlation between ChlF and VIs were found for NDII (r=0.65) and for SIPI (r=−0.68). The weakest relationship between VIs and ChlF were found for sugar beets. Despite this, it should be noted that the highest correlation for sugar beets appeared for EVI (r=0.45) and S2REP (r=0.43). The results of this study indicate the need for a synergy of low and high resolution satellite data that will enable a more detailed analysis for estimating fluorescence and its relation to climatic conditions, environmental aspects, and VIs derived from satellite images.

<bold>Remote sensing techniques</bold> to assess chlorophyll fluorescence in support of crop monitoring in Poland

Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies

Miscellanea Geographica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

 to assess chlorophyll fluorescence in support of crop monitoring in Poland

The increase in demand for food and the need to predict the impact of a warming climate on vegetation makes it critical that the best tools for assessing crop...

Remote sensing techniques to assess chlorophyll fluorescence in support of crop monitoring in Poland

Pubblicato online: 26 set 2021

Pagine: 226 - 237

Ricevuto: 28 apr 2020

Accettato: 13 ago 2020

DOI: https://doi.org/10.2478/mgrsd-2020-0029

Parole chiave
Spectral vegetation indices, land surface temperature, JECAM, Sentinel satellites

© 2021 Radosław Gurdak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Remote sensing techniques to assess chlorophyll fluorescence in support of crop monitoring in Poland

Pubblicato online: 26 set 2021

Pagine: 226 - 237

Ricevuto: 28 apr 2020

Accettato: 13 ago 2020

DOI: https://doi.org/10.2478/mgrsd-2020-0029

Parole chiaveSpectral vegetation indices, land surface temperature, JECAM, Sentinel satellites

© 2021 Radosław Gurdak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Spectral vegetation indices, land surface temperature, JECAM, Sentinel satellites