Special Issue "Utilizing Satellite Observations for Improved Crop Model Implementations at Regional Scales"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Dr. Vikalp Mishra
Website
Guest Editor
NASA Marshall Space Flight Center, SERVIR/SPoRT, Huntsville, AL 35805, USA
Interests: Remote sensing in hydrology; Agriculture and Food security; crop modeling
Dr. Christopher R. Hain
Website
Guest Editor
NASA, 320 Sparkman Drive, Huntsville, AL 35805, USA
Interests: surface energy balance modeling; soil moisture retrieval; hydrologic data assimilation and drought monitoring
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Agricultural simulation models can be a key component in addressing issues of global food security that includes monitoring and prediction of agricultural drought and its impacts; yields (production); precision agriculture; and agriculture water resources. Crop models typically depend on accurate estimates of numerous inputs, which for many areas of the world are typically not available. Sparse meteorological inputs (e.g., temperature precipitation), in combination with inconsistent management options, tend to increase uncertainties within crop model results. However, some of these uncertainties may be mitigated by utilizing remotely sensed data, such as soil moisture; optical vegetation indices; leaf area index; reference and actual evapotranspiration; land surface temperature; etc. directly or indirectly. In this Special Issue, we seek research that puts forward the use of earth observation data into crop modeling directly (forcing/assimilation) or indirectly (coupled with other land surface models) for improved crop model performance, particularly in data-limited regions of the world at regional scales.

Dr. Vikalp Mishra
Dr. Christopher Hain
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Crop modeling
  • Data assimilation
  • Soil moisture
  • Leaf area index

Published Papers (1 paper)

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Research

Open AccessArticle
How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?
Remote Sens. 2020, 12(6), 925; https://doi.org/10.3390/rs12060925 - 13 Mar 2020
Cited by 1
Abstract
The combination of Sentinel-2 derived information about sub-field heterogeneity of crop canopy leaf area index (LAI) and SoilGrids-derived information about local soil properties might help to improve the prediction accuracy of crop simulation models at sub-field level without prior knowledge of detailed site [...] Read more.
The combination of Sentinel-2 derived information about sub-field heterogeneity of crop canopy leaf area index (LAI) and SoilGrids-derived information about local soil properties might help to improve the prediction accuracy of crop simulation models at sub-field level without prior knowledge of detailed site characteristics. In this study, we ran a crop model using either soil texture derived from samples that were taken spatially distributed across a field and analyzed in the lab (AS) or SoilGrids-derived soil texture (SG) as model input in combination with different levels of LAI assimilation. We relied on the LINTUL5 model implemented in the SIMPLACE modeling framework to simulate winter wheat biomass development in 40 to 60 points in each field with detailed measured soil information available, for 14 fields across France, Germany, and the Netherlands during two growing seasons. Water stress was the only growth-limiting factor considered in the model. The model performance was evaluated against total aboveground biomass measurements at harvest with regard to the average per-field prediction and the simulated spatial variability within the field. Our findings showed that a) per-field average biomass predictions of SG-based modeling approaches were not inferior to those using AS-texture as input, but came with a greater prediction uncertainty, b) relying on the generation of an ensemble without LAI assimilation might produce results as accurate as simulations where LAI is assimilated, and c) sub-field heterogeneity was not reproduced well in any of the fields, predominantly because of an inaccurate simulation of water stress in the model. We conclude that research should be devoted to the testing of different approaches to simulate soil moisture dynamics and to the testing in other sites, potentially using LAI products derived from other remotely sensed imagery. Full article
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