Crop Yield Estimation Based on Crop Models and Remote Sensing Data
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 69
Special Issue Editors
Interests: crop yield modeling; crop dynamic monitoring with remote sensing; smart agriculture
Interests: remote sensing application in agriculture; bio-geophysical properties estimation; crop phenology; data assimilation
Special Issues, Collections and Topics in MDPI journals
Interests: agriculture remote sensing; spatio-temporal data fusion; time series analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Accurate and timely crop yield estimation is critical for addressing global food security, adapting to climate change, and advancing precision agriculture. The application of remote sensing technologies has significantly enhanced spatially explicit monitoring of crop growth and yield predictions across scales, from sub-field to global assessments. By integrating crop growth models with remotely sensed variables derived from multi-platform sensors, researchers can now produce actionable yield maps to support sustainable land management and policy-decisions.
Despite progress, key challenges remain: scaling field-level models to regional/national domains with diverse soils, climates, and practices; quantifying uncertainties in data assimilation from fusion methods, model structure, and inputs; balancing machine-learning accuracy with efficiency; and the development of robust frameworks to assess yield dynamics amid increasing climate extremes like droughts and floods.
This Special Issue invites original research and comprehensive reviews that address any of the above challenges through innovative theory, methodology or real-world applications. Topics of particular interest include, but are not limited to, advanced models in relation to:
- Novel assimilation techniques or hybrid frameworks for integrating remotely sensed variables into crop growth models;
- Uncertainty quantification and scalable solutions: quantifying uncertainties due to techniques, model or remotely sensed variables impacting on yield estimation, and leveraging advanced zone segmentation, cloud computing and high-resolution datasets for sub-field to regional yield mapping;
- Climate extremes assessment: evaluating the impacts of droughts, floods and heatwaves on yield variability and mapping across agroecological zones;
- Crop precision management: early-season yield forecasting and its integration into in-season management.
We welcome submissions that demonstrate multi-source data fusion, methodological innovations and scalable solutions for yield estimation and mapping across diverse cropping systems and environments.
Dr. Xin Du
Dr. Taifeng Dong
Dr. Chunhua Liao
Guest Editors
Manuscript Submission Information
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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. Agriculture 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 2600 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 yield estimation
- remote sensing
- crop models
- data-model fusion
- uncertainty quantification
- climate extremes
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