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Remote Sensing
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Remote Sensing and Modeling of Primary Productivity - New Insights
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Remote Sensing and Modeling of Primary Productivity - New Insights

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: closed (31 May 2023) | Viewed by 2049

Special Issue Editors

Dr. Roshanak Darvishzadeh

E-Mail Website
Guest Editor
Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
Interests: remote sensing; vegetation physiological properties; imaging spectroscopy; radiative transfer models; smart agriculture; biodiversity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lammert Kooistra

E-Mail Website
Guest Editor
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
Interests: image spectroscopy; unmanned aerial vehicle; agronomy; sensor integration; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vegetation productivity is an eminent indicator of vegetation functioning and health. Primary productivity is either directly or indirectly linked to several plant ecophysiological traits such as canopy chlorophyll content, absorbed photosynthetic active radiation, leaf area index and land use/cover change which are critical to understanding plant functioning. While in situ estimation of primary productivity through calculation of ecosystem carbon exchange or ecosystem biomass variations has been limited to small-scale studies, its estimation has been among the most important applications attempted by satellite remote sensing. In recent years, the advancement in the field of remote sensing and sensor technology has further allowed for the assessment of primary productivity using high-resolution data from airborne and UAV platforms.

A large number of relationships has been realized between remote sensing data obtained from various sensors (at field, airborne, or satellite levels), utilizing different empirical, semi-empirical or process-based models. However, regardless of remote sensing data type and models, the wide array of canopy geometry and life-cycle dynamics at large scales makes the estimation of primary production from remote sensing data challenging and needs further studies.

This Special Issue, entitled " Remote Sensing and Modeling of Primary Productivity - New Insights ", is calling for papers that demonstrate original research that can overcome or address the challenges, gaps and corresponding solutions in the estimation of vegetation primary productivity, in particular using recent advances in the remote sensing domain.

Dr. Roshanak Darvishzadeh
Dr. Lammert Kooistra
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 submissions that pass pre-check are 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 2700 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

  • net primary productivity
  • gross primary productivity
  • time series
  • vegetation indices
  • multi-scale analysis
  • empirical, vegetation modelling

Published Papers (1 paper)

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Review

25 pages, 1728 KiB  
Review
A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems
by Luleka Dlamini, Olivier Crespo, Jos van Dam and Lammert Kooistra
Remote Sens. 2023, 15(16), 4066; https://doi.org/10.3390/rs15164066 - 17 Aug 2023
Cited by 1 | Viewed by 1610
Abstract
There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming [...] Read more.
There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments. Full article
(This article belongs to the Special Issue Remote Sensing and Modeling of Primary Productivity - New Insights)
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