Crop yield estimation at different growing stages using a synergy of SAR and optical remote sensing data

Author:

Kalecinski Natacha I.ORCID,Skakun SergiiORCID,Torbick Nathan,Huang Xiaodong,Franch Belen,Roger Jean-ClaudeORCID,Vermote Eric

Funder

NASA Harvest

Publisher

Elsevier BV

Reference37 articles.

1. GEOGLAM (GEO Global Agricultural Monitoring) Crop Assessment Tool. Ag Data Commons;Becker-Reshef,2015

2. The GEOGLAM crop monitor for AMIS: assessing crop conditions in the context of global markets;Becker-Reshef;Global Food Secur.,2019

3. Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield;Betbeder;IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens.,2016

4. Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany;Blickensdörfer;Rem. Sens. Environ.,2022

5. Random forests;Breiman;Mach. Learn.,2001

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