Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia

Abstract

<jats:p>Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.</jats:p>

Abiotic Stress Response of Near-Isogenic Spring Durum Wheat Lines under Different Sowing Densities

“…The chlorophyll content of the flag leaf in phenophases Z77 and Z83 was in significant correlation with the grain number and grain weight of the main spike, the thousand-kernel weight and the plant height, confirming the likelihood that correlations between vegetation indices, yield and yield components should be sought in phenophases after flowering. In the course of breeding, the physiological parameters that are in positive correlation with the yield during drought stress (the chlorophyll content of the flag leaf, remote sensing-based indices) could be suitable for the identification of genotypes with stay-green traits, which could make them more tolerant of drought [ 46 , 47 ].…”

Estimating Farm Wheat Yields from NDVI and Meteorological Data

“…Currently, crop yield statistics are the main source of crop yield data [4]. However, the spatial and temporal scale of these crop yield statistics are not adequate, since these data are typically only available at regional or country levels and are assessed yearly [5].…”

“…Crop yield data derived from remote-sensed vegetation indices (VIs) could offer crop yield data at a higher spatial and temporal resolution. The spatial resolution of VIs can be as high as a few meters, and some VIs are available at a daily scale [4]. VIs monitor particular properties of crops that can be related to final crop yield.…”

“…A VI that is often used to monitor crop yield is the normalized difference vegetation index (NDVI), an indicator of the photosynthetic active biomass [6]. Several studies have shown that information on the photosynthetic active biomass (i.e., NDVI) during the growing season or at particular stages of the crop growing season is related to crop yield [4,[6][7][8].…”

“…Monitoring crop yield from space using VIs sounds promising, and several studies have already shown the potential of VI-derived crop yield estimates [2,[4][5][6][9][10][11][12]. Empirical or mechanistic modeling strategies are used to derive crop yield from VIs [13].…”