6533b823fe1ef96bd127f3b0
RESEARCH PRODUCT
Forecasting Wheat Yield Using Remote Sensing: The ARYA Forecasting System
Jean-claude RogerSergii SkakunBelen FranchInbal Becker-reshefBrian BarkerEric VermoteAndrés Santamaría-artigasJosé A. SobrinoChristopher O. JusticeNatacha I. Kalecinskisubject
Land surface temperatureMeteorologyRemote sensing (archaeology)Yield (finance)Winter wheatEnvironmental scienceAtmospheric modelGrowing degree-dayVegetation Indexdescription
In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI) and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied to MODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-07-11 | 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS |