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Recent Advances and Contribution of Synthetic Aperture Radar (SAR) Applications for Agricultural Monitoring

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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 (20 December 2021) | Viewed by 50748

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Special Issue Editors

Dr. Olaniyi Ajadi

E-Mail Website
Guest Editor

Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, P.O. Box 757320, Fairbanks, AK 99775, USA
Interests: SAR timeseries; polarimetric SAR; change detection; interferometry SAR; polarimetric SAR interferometry; agriculture

Dr. David McAlpin

E-Mail Website
Co-Guest Editor

Geophysical Institute, University of Alaska, 2160 Koyukuk Drive, Fairbanks, AK 99775, USA
Interests: application of interferometric SAR to active volcanoes for measurement of surface deformation

Special Issue Information

Dear Colleagues,

In recent years, the synthetic aperture radar (SAR) has gained increasing importance in agricultural applications because of its ability to operate without regard to weather, cloud cover, or daylight. The launch of the C-band Sentinel-1 mission, the X-band TerraSAR-X mission, the commercial-grade Capella’s satellites, and the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission planned in the coming years is steering in a new era for SAR-based agricultural monitoring. This Special Issue aims to present the state-of-the-art research in SAR, PolSAR, and PolInSAR for predictive agricultural monitoring using publicly available and commercial datasets. We solicit contributions from public and private sectors showcasing the contribution of SAR in agriculture spanning a wide range of topics, including but not limited to the following areas:

Crop classification using densely sampled timeseries information of SAR data
Agricultural flood monitoring
Crop management/biophysical parameter retrieval
Soil parameter retrieval
Timeseries analysis for agricultural monitoring using convolutional neural networks
Yield protect/stress detection
Field boundaries
Yield prediction

Dr. Olaniyi Ajadi
Guest Editor

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Keywords

SAR, PolSAR, PolInSAR
Crop management
Vegetation health monitoring
Flood monitoring
Crop productivity
Soil moisture
Precision agriculture

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Published Papers (9 papers)

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Research

19 pages, 49342 KiB

Open AccessArticle

Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data

by Subhadip Dey, Narayanarao Bhogapurapu, Saeid Homayouni, Avik Bhattacharya and Heather McNairn

Remote Sens. 2021, 13(21), 4412; https://doi.org/10.3390/rs13214412 - 2 Nov 2021

Cited by 15 | Viewed by 3873

Abstract

Global crop mapping and monitoring requires high-resolution spatio-temporal information. In this regard, dual polarimetric Synthetic Aperture Radar (SAR) sensors provide high temporal and high spatial resolutions with large swath width. Generally, crop phenological development studies utilized SAR backscatter intensity-based descriptors. However, these descriptors are derived either from the covariance matrix elements or from the eigendecomposition. Therefore, this approach fails to utilize the complete polarization information of the scattered wave. In this study, we propose a target characterization parameter,

θ_{xP}

that utilizes the 2D Barakat degree of polarization and the elements of the covariance matrix. We also propose an unsupervised clustering scheme using

θ_{xP}

and the scattering entropy,

H_{xP}

. We utilize time-series Sentinel-1 data of canola and wheat fields over a Canadian test site to show the sensitivity of

θ_{xP}

to the development of crop morphology at different phenological stages. During the initial growth stages,

θ_{xP}

values are low due to the low vegetation density. In contrast, at advanced phenological stages, we observe decreased values of

θ_{xP}

due to the appearance of complex canopy structure. Similarly, the effectiveness of the unsupervised

H_{xP} / θ_{xP}

clustering plane is also evident from the temporal clustering plots. This innovative clustering framework is beneficial for the operational use of Sentinel-1 SAR data for agricultural applications. Full article

(This article belongs to the Special Issue Recent Advances and Contribution of Synthetic Aperture Radar (SAR) Applications for Agricultural Monitoring)

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29 pages, 3800 KiB

Open AccessArticle

Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring

by Emilie Beriaux, Alban Jago, Cozmin Lucau-Danila, Viviane Planchon and Pierre Defourny

Remote Sens. 2021, 13(14), 2785; https://doi.org/10.3390/rs13142785 - 15 Jul 2021

Cited by 23 | Viewed by 4428

Abstract

In this upcoming Common Agricultural Policy (CAP) reform, the use of satellite imagery is taking an increasing role for improving the Integrated Administration and Control System (IACS). Considering the operational aspect of the CAP monitoring process, the use of Sentinel-1 SAR (Synthetic Aperture Radar) images is highly relevant, especially in regions with a frequent cloud cover, such as Belgium. Indeed, SAR imagery does not depend on sunlight and is barely affected by the presence of clouds. Moreover, the SAR signal is particularly sensitive to the geometry and the water content of the target. Crop identification is often a pre-requisite to monitor agriculture at parcel level (ploughing, harvest, grassland mowing, intercropping, etc.) The main goal of this study is to assess the performances and constraints of a SAR-based crop classification in an operational large-scale application. The Random Forest object-oriented classification model is built on Sentinel-1 time series from January to August 2020 only. It can identify crops in the Walloon Region (south part of Belgium) with high performance: 93.4% of well-classified area, representing 88.4% of the parcels. Among the 48 crop groups, the six most represented ones get a F1-score higher or equal to 84%. Additionally, this research documents how the classification performance is affected by different parameters: the SAR orbit, the size of the training dataset, the use of different internal buffers on parcel polygons before signal extraction, the set of explanatory variables, and the period of the time series. In an operational context, this allows to choose the right balance between classification accuracy and model complexity. A key result is that using a training dataset containing only 3.2% of the total number of parcels allows to correctly classify 91.7% of the agricultural area. The impact of rain and snow is also discussed. Finally, this research analyses how the classification accuracy depends on some characteristics of the parcels like their shape or size. This allows to assess the relevance of the classification depending on those characteristics, as well as to identify a subset of parcels for which the global accuracy is higher. Full article

(This article belongs to the Special Issue Recent Advances and Contribution of Synthetic Aperture Radar (SAR) Applications for Agricultural Monitoring)

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28 pages, 22825 KiB

Open AccessArticle

Agricultural Monitoring Using Polarimetric Decomposition Parameters of Sentinel-1 Data

by Katharina Harfenmeister, Sibylle Itzerott, Cornelia Weltzien and Daniel Spengler

Remote Sens. 2021, 13(4), 575; https://doi.org/10.3390/rs13040575 - 6 Feb 2021

Cited by 35 | Viewed by 5444

Abstract

The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters of agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines the potentials of entropy, anisotropy, and alpha angle derived from a dual-polarimetric decomposition of Sentinel-1 data to monitor crop development. The temporal profiles of these parameters are analysed for wheat and barley in the vegetation periods 2017 and 2018 for 13 fields in two test sites in Northeast Germany. The relation between polarimetric parameters and biophysical parameters observed in the field is investigated using linear and exponential regression models that are evaluated using the coefficient of determination (R²) and the root mean square error (RMSE). The performance of single regression models is furthermore compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types. The regression models perform best for data from the phenological growth stages tillering to booting. The highest R² values of the single regression models are reached for the plant height of wheat related to entropy and anisotropy with R² values of 0.64 and 0.61, respectively. The multiple regression models of VH, VV, entropy, and alpha outperform single regression models in most cases. R² values of multiple regression models of plant height (0.76), wet biomass (0.7), dry biomass (0.7), and vegetation water content (0.69) improve those of single regression models slightly by up to 0.05. Additionally, the RMSE values of the multiple regression models are around 10% lower compared to those of single regression models. The results indicate the capability of dual-polarimetric decomposition parameters in serving as meaningful input parameters for multiple regression models to improve the prediction of biophysical parameters. Additionally, their temporal profiles indicate phenological development dependent on meteorological conditions. Knowledge about biophysical parameter development and phenology is important for farmers to monitor crop growth variability during the vegetation period to adapt and to optimize field management. Full article

(This article belongs to the Special Issue Recent Advances and Contribution of Synthetic Aperture Radar (SAR) Applications for Agricultural Monitoring)

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19 pages, 9005 KiB

Open AccessArticle

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

by Chong Luo, Beisong Qi, Huanjun Liu, Dong Guo, Lvping Lu, Qiang Fu and Yiqun Shao

Remote Sens. 2021, 13(4), 561; https://doi.org/10.3390/rs13040561 - 4 Feb 2021

Cited by 62 | Viewed by 7072

Abstract

The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). In this study, two study areas (Keshan farm and Tongnan town) with different average plot sizes in Heilongjiang Province, China, were selected. The research time was two consecutive years (2018 and 2019), which were used to verify the robustness of the method. Sentinel-1 images of the crop growth period (May to September) in each study area were composited with three time intervals (10 d, 15 d and 30 d). Then, the composite images were segmented by simple noniterative clustering (SNIC) according to different sizes and finally, the training samples and processed images were input into a random forest classifier for crop classification. The results showed the following: (1) the overall accuracy of using the object-oriented classification method combined composite Sentinel-1 image represented a great improvement compared with the pixel-based classification method in areas with large average plots (increase by 10%), the applicable scope of the method depends on the plot size of the study area; (2) the shorter time interval of the composite Sentinel-1 image was, the higher the crop classification accuracy was; (3) the features with high importance of composite Sentinel-1 images with different time intervals were mainly distributed in July, August and September, which was mainly due to the large differences in crop growth in these months; and (4) the optimal segmentation size of crop classification was closely related to image resolution and plot size. Previous studies usually emphasize the advantages of object-oriented classification. Our research not only emphasizes the advantages of object-oriented classification but also analyzes the constraints of using object-oriented classification, which is very important for the follow-up research of crop classification using object-oriented and synthetic aperture radar (SAR). Full article

(This article belongs to the Special Issue Recent Advances and Contribution of Synthetic Aperture Radar (SAR) Applications for Agricultural Monitoring)

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19 pages, 4156 KiB

Open AccessArticle

Crop Height Estimation of Corn from Multi-Year RADARSAT-2 Polarimetric Observables Using Machine Learning

by Qinghua Xie, Jinfei Wang, Juan M. Lopez-Sanchez, Xing Peng, Chunhua Liao, Jiali Shang, Jianjun Zhu, Haiqiang Fu and J. David Ballester-Berman

Remote Sens. 2021, 13(3), 392; https://doi.org/10.3390/rs13030392 - 23 Jan 2021

Cited by 24 | Viewed by 4390

Abstract

This study presents a demonstration of the applicability of machine learning techniques for the retrieval of crop height in corn fields using space-borne PolSAR (Polarimetric Synthetic Aperture Radar) data. Multi-year RADARSAT-2 C-band data acquired over agricultural areas in Canada, covering the whole corn growing period, are exploited. Two popular machine learning regression methods, i.e., Random Forest Regression (RFR) and Support Vector Regression (SVR) are adopted and evaluated. A set of 27 representative polarimetric parameters are extracted from the PolSAR data and used as input features in the regression models for height estimation. Furthermore, based on the unique capability of the RFR method to determine variable importance contributing to the regression, a smaller number of polarimetric features (6 out of 27 in our study) are selected in the final regression models. Results of our study demonstrate that PolSAR observables can produce corn height estimates with root mean square error (RMSE) around 40–50 cm throughout the growth cycle. The RFR approach shows better overall accuracy in corn height estimation than the SVR method in all tests. The six selected polarimetric features by variable importance ranking can generate better results. This study provides a new perspective on the use of PolSAR data in retrieving agricultural crop height from space. Full article

(This article belongs to the Special Issue Recent Advances and Contribution of Synthetic Aperture Radar (SAR) Applications for Agricultural Monitoring)

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25 pages, 75008 KiB

Open AccessArticle

Rice-Field Mapping with Sentinel-1A SAR Time-Series Data

by Lena Chang, Yi-Ting Chen, Jung-Hua Wang and Yang-Lang Chang

Remote Sens. 2021, 13(1), 103; https://doi.org/10.3390/rs13010103 - 30 Dec 2020

Cited by 27 | Viewed by 5766

Abstract

This study proposed a feature-based decision method for the mapping of rice cultivation by using the time-series C-band synthetic aperture radar (SAR) data provided by Sentinel-1A. In this study, a model related to crop growth was first established. The model was developed based on a cubic polynomial function which was fitted by the complete time-series SAR backscatters during the rice growing season. From the developed model, five rice growth-related features were introduced, including backscatter difference (BD), time interval (TI) between vegetative growth and maturity stages, backscatter variation rate (BVR), average normalized backscatter (ANB) and maximum backscatter (MB). Then, a decision method based on the combination of the five extracted features was proposed to improve the rice detection accuracy. In order to verify the detection performance of the proposed method, the test data set of this study consisted of 50,000 rice and non-rice fields which were randomly sampled from a research area in Taiwan for simulation verification. From the experimental results, the proposed method can improve overall accuracy in rice detection by 6% compared with the method using feature BD. Furthermore, the rice detection efficiency of the proposed method was compared with other four classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN) and quadratic discriminant analysis (QDA). The experimental results show that the proposed method has better rice detection accuracy than the other four classifiers, with an overall accuracy of 91.9%. This accuracy is 3% higher than fine SVM, which performs best among the other four classifiers. In addition, the consistency and effectiveness of the proposed method in rice detection have been verified for different years and studied regions. Full article

(This article belongs to the Special Issue Recent Advances and Contribution of Synthetic Aperture Radar (SAR) Applications for Agricultural Monitoring)

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22 pages, 5848 KiB

Open AccessArticle

Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series

by Nikolaos-Christos Vavlas, Toby W. Waine, Jeroen Meersmans, Paul J. Burgess, Giacomo Fontanelli and Goetz M. Richter

Remote Sens. 2020, 12(15), 2385; https://doi.org/10.3390/rs12152385 - 24 Jul 2020

Cited by 12 | Viewed by 5864

Abstract

High-frequency Earth observation (EO) data have been shown to be effective in identifying crops and monitoring their development. The purpose of this paper is to derive quantitative indicators of crop productivity using synthetic aperture radar (SAR). This study shows that the field-specific SAR time series can be used to characterise growth and maturation periods and to estimate the performance of cereals. Winter wheat fields on the Rothamsted Research farm in Harpenden (UK) were selected for the analysis during three cropping seasons (2017 to 2019). Average SAR backscatter from Sentinel-1 satellites was extracted for each field and temporal analysis was applied to the backscatter cross-polarisation ratio (VH/VV). The calculation of the different curve parameters during the growing period involves (i) fitting of two logistic curves to the dynamics of the SAR time series, which describe timing and intensity of growth and maturation, respectively; (ii) plotting the associated first and second derivative in order to assist the determination of key stages in the crop development; and (iii) exploring the correlation matrix for the derived indicators and their predictive power for yield. The results show that the day of the year of the maximum VH/VV value was negatively correlated with yield (r = −0.56), and the duration of “full” vegetation was positively correlated with yield (r = 0.61). Significant seasonal variation in the timing of peak vegetation (p = 0.042), the midpoint of growth (p = 0.037), the duration of the growing season (p = 0.039) and yield (p = 0.016) were observed and were consistent with observations of crop phenology. Further research is required to obtain a more detailed picture of the uncertainty of the presented novel methodology, as well as its validity across a wider range of agroecosystems. Full article

(This article belongs to the Special Issue Recent Advances and Contribution of Synthetic Aperture Radar (SAR) Applications for Agricultural Monitoring)

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24 pages, 11478 KiB

Open AccessArticle

Monitoring Agricultural Fields Using Sentinel-1 and Temperature Data in Peru: Case Study of Asparagus (Asparagus officinalis L.)

by Cristian Silva-Perez, Armando Marino and Iain Cameron

Remote Sens. 2020, 12(12), 1993; https://doi.org/10.3390/rs12121993 - 21 Jun 2020

Cited by 3 | Viewed by 4928

Abstract

This paper presents the analysis and a methodology for monitoring asparagus crops from remote sensing observations in a tropical region, where the local climatological conditions allow farmers to grow two production cycles per year. We used the freely available dual-polarisation GRD data provided by the Sentinel-1 satellite, temperature from a ground station and ground truth from January to August of 2019 to perform the analysis. We showed how particularly the VH polarisation can be used for monitoring the canopy formation, density and the growth rate, revealing connections with temperature. We also present a multi-output machine learning regression algorithm trained on a rich spatio-temporal dataset in which each output estimates the number of asparagus stems that are present in each of the pre-defined crop phenological stages. We tested several scenarios that evaluated the importance of each input data source and feature, with results that showed that the methodology was able to retrieve the number of asparagus stems in each crop stage when using information about starting date and temperature as predictors with coefficients of determination

(R^{2})

between 0.84 and 0.86 and root mean squared error (RMSE) between 2.9 and 2.7. For the multitemporal SAR scenario, results showed a maximum

R^{2}

of 0.87 when using up to 5 images as input and an RMSE that maintains approximately the same values as the number of images increased. This suggests that for the conditions evaluated in this paper, the use of multitemporal SAR data only improved mildly the retrieval when the season start date and accumulated temperature are used to complement the backscatter. Full article

(This article belongs to the Special Issue Recent Advances and Contribution of Synthetic Aperture Radar (SAR) Applications for Agricultural Monitoring)

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23 pages, 5587 KiB

Open AccessArticle

Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

by Abdul Qadir and Pinki Mondal

Remote Sens. 2020, 12(3), 522; https://doi.org/10.3390/rs12030522 - 5 Feb 2020

Cited by 28 | Viewed by 5825

Abstract

Monsoon crops play a critical role in Indian agriculture, hence, monitoring these crops is vital for supporting economic growth and food security for the country. However, monitoring these crops is challenging due to limited availability of optical satellite data due to cloud cover during crop growth stages, landscape heterogeneity, and small field sizes. In this paper, our objective is to develop a robust methodology for high-resolution (10 m) monsoon cropland mapping appropriate for different agro-ecological regions (AER) in India. We adapted a synergistic approach of combining Sentinel-1 Synthetic Aperture Radar (SAR) data with Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical data using the Google Earth Engine platform. We developed a new technique, Radar Optical cross Masking (ROM), for separating cropland from non-cropland by masking out forest, plantation, and other non-dynamic features. The methodology was tested for five different AERs in India, representing a wide diversity in agriculture, soil, and climatic variations. Our findings indicate that the overall accuracy obtained by using the SAR-only approach is 90%, whereas that of the combined approach is 93%. Our proposed methodology is particularly effective in regions with cropland mixed with tree plantation/mixed forest, typical of smallholder dominated tropical countries. The proposed agriculture mask, ROM, has high potential to support the global agriculture monitoring missions of Geo Global Agriculture Monitoring (GEOGLAM) and Sentinel-2 for Agriculture (S2Agri) project for constructing a dynamic monsoon cropland mask. Full article

(This article belongs to the Special Issue Recent Advances and Contribution of Synthetic Aperture Radar (SAR) Applications for Agricultural Monitoring)

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