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Radar Remote Sensing for Agriculture

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (28 June 2019) | Viewed by 45782

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Prof. Dr. Susan Steele-Dunne

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Department of Water Management, Delft University of Technology, Stevinweg 1, CN Delft 2628, The Netherlands

Dr. Francesco Mattia

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CNR-ISSIA, via Amendola 122/D, 70126 Bari, Italy

Dr. Heather McNairn

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Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
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Dr. Thuy Le Toan

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Co-Chair of BIOMASS Mission Advisory Group, Centre d'Etudes Spatiales de la BIOsphère (CESBIO) (CNES/CNRS/UPS/IRD), 18, avenue Edouard Belin, 31401 Toulouse, CEDEX 9, France

Prof. Dr. Juanma Lopez Sanchez

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DFISTS – IUII, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain
Interests: radar polarimetry; interferometry; polarimetric SAR interferometry; agriculture; geophysics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The launch of ESA’s Sentinel-1 mission heralded a new era in radar remote sensing for agriculture. The availability of open, global, SAR data at unprecedented spatial and temporal resolution offers new opportunities for agricultural applications.

The suitability of radar data for crop classification, crop monitoring and soil moisture estimation has been demonstrated using spaceborne, airborne and ground-based sensors. The availability of data from new missions, such as Sentinel-1, TerraSAR-X, Radarsat2, SAOCOM, and COSMO SkyMed, has stimulated a transition from research to operational use.

This Special Issue aims to present state-of-the-art research in radar remote sensing for agricultural applications. Contributions are invited from across the spectrum of radar remote sensing for agriculture including but not limited to new sensors, new processing techniques, scattering theory, retrieval approaches, field experiments and airborne campaigns, data fusion and assimilation. New insights from the fields of scatterometry, SAR, PolSAR, PolInSAR and radar tomography are welcome.

Contributions relating to operational use of radar observations for decision-making, and the generation of services provided to farmers, food producers and other stakeholders in agricultural production are particularly welcome.

Prof. Dr. Susan Steele-Dunne
Dr. Francesco Mattia
Dr. Heather McNairn
Dr. Thuy Le Toan
Prof. Dr. Juan Manuel Lopez Sanchez
Guest Editors

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Published Papers (6 papers)

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Research

21 pages, 6302 KiB

Open AccessArticle

Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images

by Frédéric Baup, Maël Ameline, Rémy Fieuzal, Frédéric Frappart, Samuel Corgne and Jean-François Berthoumieu

Remote Sens. 2019, 11(17), 1978; https://doi.org/10.3390/rs11171978 - 22 Aug 2019

Cited by 15 | Viewed by 3596

Abstract

This work aims to provide daily estimates of the evolution of popcorn dry masses at the field scale using an agro-meteorological model, named the simple algorithm for yield model combined with a water balance model (SAFY-WB), controlled by the Green Area Index (GAI), derived from satellite images acquired in the microwave and optical domains. Synthetic aperture radar (SAR) satellite information (σ°_VH/VV) was provided by the Sentinel-1A (S1-A) mission through two orbits (30 and 132), with a repetitiveness of six days. The optical data were obtained from the Landsat-8 mission. SAR and optical data were acquired over one complete agricultural season, in 2016, over a test site located in the southwest of France. The results show that the total dry masses of corn can be estimated accurately (R² = 0.92) at daily time steps due to a combination of satellite and model data. The SAR data are more suitable for characterizing the first period of crop development (until the end of flowering), whereas the optical data can be used throughout the crop cycle. Moreover, the model offers good performances in plant (R² = 0.90) and ear (R² = 0.93) mass retrieval, irrespective of the phenological stage. The results also reveal that four phenological stages (four to five leaves, flowering, ripening, and harvest) can be accurately predicted by the proposed approach (R² = 0.98; root-mean-square error (RMSE) = seven days). Nevertheless, some important points must be taken into account before assimilation, namely the SAR signal must be corrected with respect to thermal noise before being assimilated, and the relationship estimated between the GAI and SAR signal must be performed over fields cultivated without intercrops. These results are unique in the literature and provide a new way to better monitor corn production over time. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Agriculture)

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24 pages, 9901 KiB

Open AccessEditor’s ChoiceArticle

Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands

by Saeed Khabbazan, Paul Vermunt, Susan Steele-Dunne, Lexy Ratering Arntz, Caterina Marinetti, Dirk van der Valk, Lorenzo Iannini, Ramses Molijn, Kees Westerdijk and Corné van der Sande

Remote Sens. 2019, 11(16), 1887; https://doi.org/10.3390/rs11161887 - 13 Aug 2019

Cited by 136 | Viewed by 15104

Abstract

Agriculture is of huge economic significance in The Netherlands where the provision of real-time, reliable information on crop development is essential to support the transition towards precision agriculture. Optical imagery can provide invaluable insights into crop growth and development but is severely hampered by cloud cover. This case study in the Flevopolder illustrates the potential value of Sentinel-1 for monitoring five key crops in The Netherlands, namely sugar beet, potato, maize, wheat and English rye grass. Time series of radar backscatter from the European Space Agency’s Sentinel-1 Mission are analyzed and compared to ground measurements including phenological stage and height. Temporal variations in backscatter data reflect changes in water content and structure associated with phenological development. Emergence and closure dates are estimated from the backscatter time series and validated against a photo archive. Coherence data are compared to Normalized Difference Vegetation Index (NDVI) and ground data, illustrating that the sudden increase in coherence is a useful indicator of harvest. The results presented here demonstrate that Sentinel-1 data have significant potential value to monitor growth and development of key Dutch crops. Furthermore, the guaranteed availability of Sentinel-1 imagery in clouded conditions ensures the reliability of data to meet the monitoring needs of farmers, food producers and regulatory bodies. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Agriculture)

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30 pages, 35763 KiB

Open AccessArticle

Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data

by Katharina Harfenmeister, Daniel Spengler and Cornelia Weltzien

Remote Sens. 2019, 11(13), 1569; https://doi.org/10.3390/rs11131569 - 2 Jul 2019

Cited by 71 | Viewed by 10134

Abstract

The knowledge about heterogeneity on agricultural fields is essential for a sustainable and effective field management. This study investigates the performance of Synthetic Aperture Radar (SAR) data of the Sentinel-1 satellites to detect variability between and within agricultural fields in two test sites in Germany. For this purpose, the temporal profiles of the SAR backscatter in VH and VV polarization as well as their ratio VH/VV of multiple wheat and barley fields are illustrated and interpreted considering differences between acquisition settings, years, crop types and fields. Within-field variability is examined by comparing the SAR backscatter with several crop parameters measured at multiple points in 2017 and 2018. Structural changes, particularly before and after heading, as well as moisture and crop cover differences are expressed in the backscatter development. Furthermore, the crop parameters wet and dry biomass, absolute and relative vegetation water content, leaf area index (LAI) and plant height are related to SAR backscatter parameters using linear and exponential as well as multiple regression. The regression performance is evaluated using the coefficient of determination (R

^{2}

) and the root mean square error (RMSE) and is strongly dependent on the phenological growth stage. Wheat shows R

^{2}

values around 0.7 for VV backscatter and multiple regression and most crop parameters before heading. Single fields even reach R

^{2}

values above 0.9 for VV backscatter and for multiple regression related to plant height with RMSE values around 10 cm. The formulation of clear rules remains challenging, as there are multiple influencing factors and uncertainties and a lack of conformity. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Agriculture)

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20 pages, 3704 KiB

Open AccessArticle

Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping

by Rubén Valcarce-Diñeiro, Benjamín Arias-Pérez, Juan M. Lopez-Sanchez and Nilda Sánchez

Remote Sens. 2019, 11(13), 1518; https://doi.org/10.3390/rs11131518 - 27 Jun 2019

Cited by 25 | Viewed by 4828

Abstract

Land-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR) can contribute considerably to this monitoring effort. The first objective of this research is to extend the use of time series of polarimetric data for land-cover classification using a decision tree classification algorithm. With this aim, RADARSAT-2 (quad-pol) and Sentinel-1 (dual-pol) data were acquired over an area of 600 km² in central Spain. Ten polarimetric observables were derived from both datasets and seven scenarios were created with different sets of observables to evaluate a multitemporal parcel-based approach for classifying eleven land-cover types, most of which were agricultural crops. The study demonstrates that good overall accuracies, greater than 83%, were achieved for all of the different proposed scenarios and the scenario with all RADARSAT-2 polarimetric observables was the best option (89.1%). Very high accuracies were also obtained when dual-pol data from RADARSAT-2 or Sentinel-1 were used to classify the data, with overall accuracies of 87.1% and 86%, respectively. In terms of individual crop accuracy, rapeseed achieved at least 95% of a producer’s accuracy for all scenarios and that was followed by the spring cereals (wheat and barley), which achieved high producer’s accuracies (79.9%-95.3%) and user’s accuracies (85.5% and 93.7%). Full article

(This article belongs to the Special Issue Radar Remote Sensing for Agriculture)

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16 pages, 3666 KiB

Open AccessArticle

Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

by Rei Sonobe

Remote Sens. 2019, 11(10), 1148; https://doi.org/10.3390/rs11101148 - 14 May 2019

Cited by 34 | Viewed by 5199

Abstract

Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Agriculture)

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20 pages, 4436 KiB

Open AccessArticle

On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

by Qinghua Xie, Jinfei Wang, Chunhua Liao, Jiali Shang, Juan M. Lopez-Sanchez, Haiqiang Fu and Xiuguo Liu

Remote Sens. 2019, 11(7), 776; https://doi.org/10.3390/rs11070776 - 31 Mar 2019

Cited by 32 | Viewed by 5209

Abstract

In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition. Full article

(This article belongs to the Special Issue Radar Remote Sensing for Agriculture)

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