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Recent Advances in Synthetic Aperture Radar (SAR) Remote Sensing for...
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Recent Advances in Synthetic Aperture Radar (SAR) Remote Sensing for Agricultural Applications

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 15750

Special Issue Editors

Dr. Heather McNairn

E-Mail Website
Guest Editor
Ottawa Research and Development Centre, Science and Technology Branch, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON K1A0C6, Canada
Interests: synthetic aperture radar; monitoring crops and soils
Special Issues, Collections and Topics in MDPI journals
Dr. Mehdi Hosseini

E-Mail Website
Co-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
Dr. Nathan Torbick

E-Mail Website
Co-Guest Editor
Applied GeoSolutions, Durham, NH 03824, USA
Interests: SAR; crop water management; sustainability; machine learning; MRV tools; data fusion

Special Issue Information

Dear colleagues,

Strategies to feed a growing global population, in an era of increasing climate uncertainty, benefit from high-quality data provided consistently over space and time. Satellite and airborne-based sensors are a source of data to map what crops are growing where, to deliver management and sustainability metrics, and to monitor crop productivity. Although synthetic aperture radars (SARs) have an all-weather advantage, historically the uptake of SAR has been limited as a primary data source for operational monitoring of agriculture.

Engineering advancements with respect to the breadth of SAR satellite capabilities, an increase in the number of SAR missions, and more open data policies, are shifting the role SARs are playing in agriculture applications. These advancements come at a time when researchers are developing novel methods which use SAR technology to quantify agricultural productivity, as well as the health of our soils and crops. These breakthroughs are leading operational agencies to consider SAR-based products within their decision support tools.

This Special Issue is soliciting research papers that document novel and innovative methodologies to use SAR technologies for monitoring croplands including soil health characteristics, conservation land management practices, crop type, irrigation, and biophysical measures of crop productivity and growth. Researchers are encouraged to submit papers that exploit advanced SAR technologies including full and compact polarimetry, multi-frequency SAR integration, interferometry SAR, and coherent change detection. In addition, we welcome manuscripts that focus on scaling the application readiness level of SAR-driven decision support tools and the use of SAR in sustainability metrics.

Dr. Heather McNairn
Dr. Mehdi Hosseini
Dr. Nathan Torbick
Guest Editor

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. Agronomy is an international peer-reviewed open access monthly 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

  • synthetic aperture radar (SAR)
  • agriculture mapping and monitoring
  • crops
  • soils
  • decision support

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Published Papers (4 papers)

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Research

17 pages, 2481 KiB  
Article
Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease
by Izrahayu Che Hashim, Abdul Rashid Mohamed Shariff, Siti Khairunniza Bejo, Farrah Melissa Muharam and Khairulmazmi Ahmad
Agronomy 2021, 11(3), 532; https://doi.org/10.3390/agronomy11030532 - 12 Mar 2021
Cited by 18 | Viewed by 3274
Abstract
Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is [...] Read more.
Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is to prolong the life of oil palm trees since there has been no effective treatment for the BSR disease. This project used an ALOS PALSAR-2 image with dual polarization, Horizontal transmit and Horizontal receive (HH) and Horizontal transmit and Vertical receive (HV). The aims of this study were to (1) identify the potential backscatter variables; and (2) examine the performance of machine learning (ML) classifiers (Multilayer Perceptron (MLP) and Random Forest (RF) to classify oil palm trees that are non-infected and infected by G. boninense. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approach (Synthetic Minority Over-Sampling Technique (SMOTE) in these classifications due to the differing sample sizes. The result showed backscatter variable HV had a higher correct classification for the G. boninense non-infected and infected oil palm trees for both classifiers; the MLP classifier model had a robust success rate, which correctly classified 100% for non-infected and 91.30% for infected G. boninense, and RF had a robust success rate, which correctly classified 94.11% for non-infected and 91.30% for infected G. boninense. In terms of model performance using the most significant variables, HV, the MLP model had a balanced accuracy (BCR) of 95.65% compared to 92.70% for the RF model. Comparison between the MLP model and RF model for the receiver operating characteristics (ROC) curve region, (AUC) gave a value of 0.92 and 0.95, respectively, for the MLP and RF models. Therefore, it can be concluded by using only the HV polarization, that both the MLP and RF can be used to predict BSR disease with a relatively high accuracy. Full article
(This article belongs to the Special Issue Recent Advances in Synthetic Aperture Radar (SAR) Remote Sensing for Agricultural Applications)
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20 pages, 6457 KiB  
Article
Comparison between Dense L-Band and C-Band Synthetic Aperture Radar (SAR) Time Series for Crop Area Mapping over a NISAR Calibration-Validation Site
by Simon Kraatz, Nathan Torbick, Xianfeng Jiao, Xiaodong Huang, Laura Dingle Robertson, Andrew Davidson, Heather McNairn, Michael H. Cosh and Paul Siqueira
Agronomy 2021, 11(2), 273; https://doi.org/10.3390/agronomy11020273 - 1 Feb 2021
Cited by 14 | Viewed by 3993
Abstract
Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter [...] Read more.
Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CVthr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values. Full article
(This article belongs to the Special Issue Recent Advances in Synthetic Aperture Radar (SAR) Remote Sensing for Agricultural Applications)
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25 pages, 6222 KiB  
Article
Decomposition-Based Soil Moisture Estimation Using UAVSAR Fully Polarimetric Images
by Zeinab Akhavan, Mahdi Hasanlou, Mehdi Hosseini and Heather McNairn
Agronomy 2021, 11(1), 145; https://doi.org/10.3390/agronomy11010145 - 14 Jan 2021
Cited by 11 | Viewed by 3938
Abstract
Polarimetric decomposition extracts scattering features that are indicative of the physical characteristics of the target. In this study, three polarimetric decomposition methods were tested for soil moisture estimation over agricultural fields using machine learning algorithms. Features extracted from model-based Freeman–Durden, Eigenvalue and Eigenvector [...] Read more.
Polarimetric decomposition extracts scattering features that are indicative of the physical characteristics of the target. In this study, three polarimetric decomposition methods were tested for soil moisture estimation over agricultural fields using machine learning algorithms. Features extracted from model-based Freeman–Durden, Eigenvalue and Eigenvector based H/A/α, and Van Zyl decompositions were used as inputs in random forest and neural network regression algorithms. These algorithms were applied to retrieve soil moisture over soybean, wheat, and corn fields. A time series of polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the Soil Moisture Active Passive Experiment 2012 (SMAPVEX12) field campaign was used for the training and validation of the algorithms. Three feature selection methods were tested to determine the best input features for the machine learning algorithms. The most accurate soil moisture estimates were derived from the random forest regression algorithm for soybeans, with a correlation of determination (R2) of 0.86, root mean square error (RMSE) of 0.041 m3 m−3 and mean absolute error (MAE) of 0.030 m3 m−3. Feature selection also impacted results. Some features like anisotropy, Horizontal transmit and Horizontal receive (HH), and surface roughness parameters (correlation length and RMS-H) had a direct effect on all algorithm performance enhancement as these parameters have a direct impact on the backscattered signal. Full article
(This article belongs to the Special Issue Recent Advances in Synthetic Aperture Radar (SAR) Remote Sensing for Agricultural Applications)
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13 pages, 8358 KiB  
Article
Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning
by Xiaodong Huang, Beth Ziniti, Michael H. Cosh, Michele Reba, Jinfei Wang and Nathan Torbick
Agronomy 2021, 11(1), 35; https://doi.org/10.3390/agronomy11010035 - 26 Dec 2020
Cited by 8 | Viewed by 3597
Abstract
Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the [...] Read more.
Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60. Full article
(This article belongs to the Special Issue Recent Advances in Synthetic Aperture Radar (SAR) Remote Sensing for Agricultural Applications)
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