Articles | Volume 27, issue 5
Research article
16 Mar 2023
Research article |  | 16 Mar 2023

A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest

Marissa Kivi, Noemi Vergopolan, and Hamze Dokoohaki


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-342', Warrick Dawes, 03 Nov 2022
    • AC1: 'Reply on RC1', Hamze Dokoohaki, 09 Jan 2023
  • RC2: 'Comment on hess-2022-342', Svitlana Kokhan, 29 Nov 2022
    • AC2: 'Reply on RC2', Hamze Dokoohaki, 09 Jan 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (further review by editor) (24 Jan 2023) by Alexander Gruber
AR by Hamze Dokoohaki on behalf of the Authors (31 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
EF by Polina Shvedko (01 Feb 2023)
ED: Publish as is (06 Feb 2023) by Alexander Gruber
AR by Hamze Dokoohaki on behalf of the Authors (11 Feb 2023)  Manuscript 
Short summary
This study attempts to provide a framework for direct integration of soil moisture observations collected from soil sensors and satellite imagery into process-based crop models for improving the representation of agricultural systems. The performance of this framework was evaluated across 19 sites times years for crop yield, normalized difference vegetation index (NDVI), soil moisture, tile flow drainage, and nitrate leaching.