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Special Issue "Quantitative Remote Sensing for Agricultural Monitoring in the Big Data Era"
Deadline for manuscript submissions: 30 June 2020.
Interests: remote sensing; data assimilation; global change; radiative transfer; inverse problems; gaussian processes; microwave remote sensing, optical remote sensing, thermal remote sensing, fire, vegetation, image processing, signal processing, vegetation modeling, fire modeling, data assimilation; emulation
Special Issues and Collections in MDPI journals
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Interests: data assimilation; deep learning; crop yield prediction; remote sensing monitoring for agri-hazards; crop modeling
Special Issues and Collections in MDPI journals
Within the context of a changing climate, the impact of drought and water depletion, heat stress, soil erosion and combined population growth is predicted to result in challenges to food security and lead to an ever-increasing pressure on the agricultural sector. In addition, global markets, global and regional climate changes and uncertainty in future patterns of drivers of crop production further increase the need for timely monitoring and prediction systems providing information for various levels of government and other actors to achieve sustainable intensification, particularly over large regions.
Enhancing the sustainability of the food-producing system requires frequent monitoring of large areas. This is only possible with Earth Observation (EO) technologies. In this regard, the recent advent of frequent sensors, providing observations over large areas with an unprecedented level of spatial and temporal detail, is very promising. EO data are, however, limited to being indirect observers of the reality on the ground and are not able to measure parameters of interest such as crop yield, pest damage or vegetation stresses. A major research task is how to link these observations to the reality on the ground. To this end, a number of avenues are being actively pursued: from the blending of in situ sensor network data with EO data to the use of historical official statistical data and of mechanistic or statistically derived crop models.
As these techniques have been proven useful for extracting agricultural information, there is an increasing demand to transfer them to large and/or regional scales. A possible solution to this is the use of new ‘big data’ opportunities and cutting-edge research, including, but not limited to, artificial intelligence, cloud computing, data assimilation, and emulation, to provide timely information. Hence, we invite submissions on, but not limited to, the following topics:
- Biophysical parameter retrieval at large scales
- Quantitative remote sensing at regional scales
- Radiative transfer modelling of crop systems
- Data assimilation for agricultural studies
- Multi-sensor combined inferences
- Use of Google Earth Engine, data cube or similar services for agricultural monitoring
- Big Data processing for Analysis Ready Data
- Deep learning for agricultural studies
Dr. Jose Gomez-Dans
Prof. Jianxi Huang
Dr. Qingling Wu
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 2000 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.
- Quantitative remote sensing
- Data assimilation
- Crop modelling
- Deep learning
- Big data for agriculture
- Biophysical parameter retrieval
- Radiative transfer model