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Special Issue "Radar Remote Sensing for Monitoring Agricultural Management"

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: 15 December 2023 | Viewed by 1913

Special Issue Editors

Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
Interests: microwave and optical remote sensing for crop biophysical parameter retrieval; synthetic aperture radar for crop monitoring; radar vegetation indices; machine learning based inversion algorithms
Institute for Computer Research (IUII), University of Alicante, 03690 Alicante, Spain
Interests: electromagnetic modeling; radar polarimetry; polarimetric SAR data analysis; remote sensing for land applications.
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Interests: physical, statistical and machine learning approaches for modeling of agricultural and environmental
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Special Issue Information

Dear Colleagues,

The growth of the world population is accelerating at a time of increasing climatic uncertainty, leading to effects upon agricultural production. Measuring the current status of our agricultural landscapes, and monitoring how we are managing our agro-ecosystems, is incredibly important. Although there is no single solution, space-based imagery provides science-based data to monitor and respond to risks that threaten agriculture, to manage landscapes, and to quantify crop production. In recent decades, efforts have accelerated to develop methods to exploit space-based synthetic aperture radar (SAR) imagery to monitor agriculture and inventory mapping. Furthermore, it is gaining attention due to the availability of increased SAR satellites and the rapid expansion of the constellations of satellites. With recent developments, SAR imaging modes are more sophisticated, and enable data acquisition not only in single and dual polarizations but also in fully polarimetric (FP) and compact polarimetric (CP) configurations. In addition to these advancements in polarimetry, users of these space-based SAR satellites are able to see the Earth at incredible spatial detail and over large geographical extents. Such advanced sensors offer an extraordinary opportunity to monitor our changing landscapes. These remarkable advancements in SAR engineering have challenged researchers to find ways to exploit the full capability of these advanced SAR modes. Years of research have been convincing. SAR sensors have a vital role to play in monitoring soils and crops, and in quantifying crop production.  In addition, Earth Observation (EO) data analytics and computing framework for agricultural applications has established itself as an independent domain of research over several decades, with numerous renowned organizations, international consortia, and institutions focusing on utilizing and promoting these datasets. Benchmarking such efforts and scientifically developed applications is essential in radar remote sensing for agricultural crop mapping and monitoring for translating research into operation.

This Special Issue aims to present state-of-the-art research in radar remote sensing for monitoring agricultural management including, but not limited to: Tillage operation and harvest;

  • Irrigation management;
  • Crop damage assessment;
  • Crop phenology stage identification;
  • New processing pipelines in cloud computing framework;
  • Geo-biophysical parameter retrieval approaches;
  • Field experiments;
  • Data fusion and assimilation.


  • Analysis of time series dynamics from SAR data to track crop phenological development;
  • Crop characterization using SAR polarimetric features including full, dual and compact polarimetric mode;
  • Multi-frequency SAR data integration;
  • SAR interferometry and coherent change detection;
  • Radar vegetation indices;
  • Cloud computing processing pipelines exploring cropland traits monitoring;
  • Synergies between optical and radar data;
  • Conservation land management practices;
  • Crop classification and crop risk assessment;
  • Geo-biophysical measures of crop productivity and growth.

Article types:

  • Research articles;
  • Review articles;
  • Short communications;
  • Technical notes.

Dr. Dipankar Mandal
Dr. Lucio Mascolo
Dr. Mehdi Hosseini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.


  • tillage operation and harvest
  • soil moisture
  • crop damage assessment
  • crop phenology stage identification
  • new processing pipelines in cloud computing framework
  • geo-biophysical parameter retrieval approaches
  • field experiments
  • data fusion and assimilation

Published Papers (1 paper)

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Bias-Corrected RADARSAT-2 Soil Moisture Dynamics Reveal Discharge Hysteresis at An Agricultural Watershed
Remote Sens. 2023, 15(10), 2677; - 21 May 2023
Cited by 1 | Viewed by 692 | Correction
Satellites are designed to monitor geospatial data over large areas at a catchment scale. However, most of satellite validation works are conducted at local point scales with a lack of spatial representativeness. Although upscaling them with a spatial average of several point data [...] Read more.
Satellites are designed to monitor geospatial data over large areas at a catchment scale. However, most of satellite validation works are conducted at local point scales with a lack of spatial representativeness. Although upscaling them with a spatial average of several point data collected in the field, it is almost impossible to reorganize backscattering responses at pixel scales. Considering the influence of soil storage on watershed streamflow, we thus suggested watershed-scale hydrological validation. In addition, to overcome the limitations of backscattering models that are widely used for C-band Synthetic Aperture Radar (SAR) soil moisture but applied to bare soils only, in this study, RADARSAT-2 soil moisture was stochastically retrieved to correct vegetation effects arising from agricultural lands. Roughness-corrected soil moisture retrievals were assessed at various spatial scales over the Brightwater Creek basin (land cover: crop lands, gross drainage area: 1540 km2) in Saskatchewan, Canada. At the point scale, local station data showed that the Root Mean Square Errors (RMSEs), Unbiased RMSEs (ubRMSEs) and biases of Radarsat-2 were 0.06~0.09 m3/m3, 0.04~0.08 m3/m3 and 0.01~0.05 m3/m3, respectively, while 1 km Soil Moisture Active Passive (SMAP) showed underestimation at RMSEs of 0.1~0.22 m3/m3 and biases of −0.036~−0.2080 m3/m3. Although SMAP soil moisture better distinguished the contributing area at the catchment scale, Radarsat-2 soil moisture showed a better discharge hysteresis. A reliable estimation of the soil storage dynamics is more important for discharge forecasting than a static classification of contributing and noncontributing areas. Full article
(This article belongs to the Special Issue Radar Remote Sensing for Monitoring Agricultural Management)
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