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Radar Remote Sensing for Monitoring Agricultural Management
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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: 28 February 2025 | Viewed by 8050

Special Issue Editors

Dr. Dipankar Mandal

E-Mail Website
Guest Editor
School of Agro and Rural Technology, Indian Institute of Technology Guwahati, Assam 781039, India
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
Special Issues, Collections and Topics in MDPI journals
Dr. Lucio Mascolo

E-Mail Website
Guest Editor
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.
Dr. Mehdi Hosseini

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

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.

Themes:

  • 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 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

  • 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

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Published Papers (4 papers)

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Research

21 pages, 6653 KiB  
Article
Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series Data
by Hongzhong Li, Zhengxin Wang, Luyi Sun, Longlong Zhao, Yelong Zhao, Xiaoli Li, Yu Han, Shouzhen Liang and Jinsong Chen
Remote Sens. 2024, 16(15), 2785; https://doi.org/10.3390/rs16152785 - 30 Jul 2024
Viewed by 899
Abstract
The timely and accurate mapping of sugarcane cultivation is significant to ensure the sustainability of the sugarcane industry, including sugarcane production, rural society, sugar futures, and crop insurance. Synthetic aperture radar (SAR), due to its all-weather and all-time imaging capability, plays an important [...] Read more.
The timely and accurate mapping of sugarcane cultivation is significant to ensure the sustainability of the sugarcane industry, including sugarcane production, rural society, sugar futures, and crop insurance. Synthetic aperture radar (SAR), due to its all-weather and all-time imaging capability, plays an important role in mapping sugarcane cultivation in cloudy areas. However, the inherent speckle noise of SAR data worsens the “salt and pepper” effect in the sugarcane map. Therefore, in previous studies, an additional land cover map or optical image was still required. This study proposes a new application paradigm of time series SAR data for sugarcane mapping to tackle this limitation. First, the locally estimated scatterplot smoothing (LOESS) smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multi-temporal SAR images with high importance values. Lastly, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate sugarcane cultivation map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the “salt and pepper” spots and improving the completeness of the sugarcane plots. In particular, the unreasonable non-vegetation spots in the sugarcane map were eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation. Unlike traditional methods that rely on optical remote sensing data, the new paradigm offers a high level of practicality for mapping sugarcane in large regions. This is particularly beneficial in cloudy areas where optical remote sensing data is frequently unavailable. Full article
(This article belongs to the Special Issue Radar Remote Sensing for Monitoring Agricultural Management)
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18 pages, 7018 KiB  
Article
A Comprehensive Evaluation of Dual-Polarimetric Sentinel-1 SAR Data for Monitoring Key Phenological Stages of Winter Wheat
by Mo Wang, Laigang Wang, Yan Guo, Yunpeng Cui, Juan Liu, Li Chen, Ting Wang and Huan Li
Remote Sens. 2024, 16(10), 1659; https://doi.org/10.3390/rs16101659 - 8 May 2024
Cited by 3 | Viewed by 1615
Abstract
Large-scale crop phenology monitoring is critical for agronomic planning and yield prediction applications. Synthetic Aperture Radar (SAR) remote sensing is well-suited for crop growth monitoring due to its nearly all-weather observation capability. Yet, the capability of the dual-polarimetric SAR data for wheat phenology [...] Read more.
Large-scale crop phenology monitoring is critical for agronomic planning and yield prediction applications. Synthetic Aperture Radar (SAR) remote sensing is well-suited for crop growth monitoring due to its nearly all-weather observation capability. Yet, the capability of the dual-polarimetric SAR data for wheat phenology estimation has not been thoroughly investigated. Here, we conducted a comprehensive evaluation of Sentinel-1 SAR polarimetric parameters’ sensibilities on winter wheat’s key phenophases while considering the incidence angle. We extracted 12 polarimetric parameters based on the covariance matrix and a dual-pol-version H-α decomposition. All parameters were evaluated by their temporal profile and feature importance score of Gini impurity with a decremental random forest classification process. A final wheat phenology classification model was built using the best indicator combination. The result shows that the Normalized Shannon Entropy (NSE), Degree of Linear Polarization (DoLP), and Stokes Parameter g2 were the three most important indicators, while the Span, Average Alpha (α2¯), and Backscatter Coefficient σVH0 were the three least important features in discriminating wheat phenology for all three incidence angle groups. The smaller-incidence angle (30–35°) SAR images are better suited for estimating wheat phenology. The combination of NSE, DoLP, and two Stokes Parameters (g2 and g0) constitutes the most effective indicator ensemble. For all eight key phenophases, the average Precision and Recall scores were above 0.8. This study highlighted the potential of dual-polarimetric SAR data for wheat phenology estimation. The feature importance evaluation results provide a reference for future phenology estimation studies using dual-polarimetric SAR data in choosing better-informed indicators. Full article
(This article belongs to the Special Issue Radar Remote Sensing for Monitoring Agricultural Management)
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24 pages, 3929 KiB  
Article
Sentinel-1-Based Soil Freeze–Thaw Detection in Agro-Forested Areas: A Case Study in Southern Québec, Canada
by Shahabeddin Taghipourjavi, Christophe Kinnard and Alexandre Roy
Remote Sens. 2024, 16(7), 1294; https://doi.org/10.3390/rs16071294 - 6 Apr 2024
Cited by 1 | Viewed by 1657
Abstract
Nearly 50 million km2 of global land experiences seasonal transitions from predominantly frozen to thawed conditions, significantly impacting various ecosystems and hydrologic processes. In this study, we assessed the capability to retrieve surface freeze–thaw (FT) conditions using Sentinel-1 synthetic aperture radar (SAR) [...] Read more.
Nearly 50 million km2 of global land experiences seasonal transitions from predominantly frozen to thawed conditions, significantly impacting various ecosystems and hydrologic processes. In this study, we assessed the capability to retrieve surface freeze–thaw (FT) conditions using Sentinel-1 synthetic aperture radar (SAR) data time series at two agro-forested study sites, St-Marthe and St-Maurice, in southern Québec, Canada. In total, 18 plots were instrumented to monitor soil temperature and derive soil freezing probabilities at 2 and 10 cm depths during 2020–21 and 2021–22. Three change detection algorithms were tested: backscatter differences (∆σ) derived from thawed reference (Delta), the freeze–thaw index (FTI), and a newly developed exponential freeze–thaw algorithm (EFTA). Various probabilistic mixed models were compared to identify the model and predictor variables that best predicted soil freezing probability. VH polarization backscatter signals processed with the EFTA and used as predictors in a logistic model led to improved predictions of soil freezing probability at 2 cm (Pseudo-R2 = 0.54) compared to other approaches. The EFTA could effectively address the limitations of the Delta algorithm caused by backscatter fluctuations in the shoulder seasons, resulting in more precise estimates of FT events. Furthermore, the inclusion of crop types as plot-level effects within the probabilistic model also slightly improved the soil freezing probability prediction at each monitored plot, with marginal and conditional R2 values of 0.59 and 0.61, respectively. The model accurately classified observed binary ‘frozen’ or ‘thawed’ states with 85.2% accuracy. Strong cross-level interactions were also observed between crop types and the EFTA derived from VH backscatter, indicating that crop type modulated the backscatter response to soil freezing. This study represents the first application of the EFTA and a probabilistic approach to detect frozen soil conditions in agro-forested areas in southern Quebec, Canada. Full article
(This article belongs to the Special Issue Radar Remote Sensing for Monitoring Agricultural Management)
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15 pages, 3351 KiB  
Article
Bias-Corrected RADARSAT-2 Soil Moisture Dynamics Reveal Discharge Hysteresis at An Agricultural Watershed
by Ju Hyoung Lee and Karl-Erich Lindenschmidt
Remote Sens. 2023, 15(10), 2677; https://doi.org/10.3390/rs15102677 - 21 May 2023
Cited by 2 | Viewed by 1768 | Correction
Abstract
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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