Informations et ressources scientifiques
sur le développement des zones arides et semi-arides

United States Department of Agriculture (USDA) National Institute of Food and Agriculture

Titre : INTEGRATING FIELD MEASUREMENTS AND MODELS TO EVALUATE SOLAR INDUCED FLUORESCENCE AS A PREDICTOR OF DRYLAND CROP PRODUCTIVITY

Identification : 0441919

Pays : Etats Unis

Durée : START : 01 APR 2022 TERM : 30 JUN 2024

Présentation
OBJECTIVES : Will develop the capacity to use remote sensing based solar induced fluorescence (SIF) for determining crop productivity and yield under stressed conditions in semi-arid regions.

APPROACH : Data will be collected from two ARS research farms, the Limited Irrigation Research Farm (LIRF) in Greeley, CO, and Central Great Plains Research Station in Akron, CO. Ground-based PhotoSpec SIF instruments, a spectrometer system designed by a UC Davis collaborator, will be used to collect continuous SIF measurements that can be used to simulate remotely-sensed measurements. The PhotoSpec will measure diurnal SIF in the red (670â¿¿732 nm) and far-red (729â¿¿784 nm) wavelength range. An unmanned aerial vehicle (UAV) imaging system will be used to routinely acquire multispectral and thermal imagery so SIF measurements can be compared against established methodologies. Vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the photochemical reflectance index (PRI), and crop water stress index (CWSI) will be derived from UAV imagery for these comparisons. Existing Eddy covariance (EC) systems installed at both farms will acquire carbon flux data to derive half-hourly net ecosystem exchange (NEE) and GPP for further ground-truthing. Finally, we will determine the relationship of SIF with productivity and yield in crops under stressed conditions by coupling field-level measurements of plant physiology, growth, and canopy architecture with a 3-dimensional biophysical model of radiation transfer, and drought impacts on GPP, and transpiration. This modeling will corroborate sub-daily trends in GPP and SIF with the timing of satellite fly-over. The modeling will use measurements of transpiration from sap flow sensors, continuous soil water content from soil moisture sensors, and 15-minute meteorological data from on-site weather stations, as well as ground measurements of leaf area index, crop grow stage, biomass, and yield collected throughout the growing season. The relationships between GPP with its proxies (SIF and VIs) will be established and compared. The factors cause the deviate of the GPP-SIF relationship from linearity under stress conditions will be explored. The accumulated annual effect of water stress on crop productivity and yield will be evaluated.

Performing Institution : COLORADO STATE UNIVERSITY
Investigator : ZHANG H ; BARNARD D M ; SCHUH A ; COMAS L H

Présentation : USDA (NIFA)