Winter Wheat Growth Monitoring Using Multi-temporal TerraSAR-X Dual-polarimetric Data

Sonobe, Rei; Tani, Hiroshi; Wang, Xiufeng; Kobayashi, Nobuyuki; Shimamura, Hideki

doi:10.6090/jarq.48.471

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…In contrast, for Sentinel-1 C-SAR data, all J-M distance values were <1.0. Vertical polarized microwaves have a less penetration than horizontal polarized waves over mature wheat fields [14], and their intensity is decreased via absorption by the vertical structure of dense, narrow stems [25,63]. The main scattering pattern was surface scattering and the intensity was low in August, with the result that the differences in sigma naught were small.…”

Section: Separability Assessmentsmentioning

confidence: 99%

“…However, the quality of optical remote sensing data depends on atmospheric influences and weather conditions. Substantial information about soil and vegetation parameters has been obtained through microwave remote sensing, and this type of technique is increasingly being used to manage land and water resources for agricultural applications [11][12][13][14]. Synthetic aperture radar (SAR) systems offer a large amount of information about soil moisture, crop height, and crop cover rate, which are useful for monitoring plant phenology [12,15].…”

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confidence: 99%

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Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Sonobe

2019

Remote Sensing

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The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 ± 1.8% were achieved using SVM.

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Section: Separability Assessmentsmentioning

confidence: 99%

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confidence: 99%

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Sonobe

2019

Remote Sensing

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“…There are examples of uses for crop growth monitoring of beets (Vyas et al 2003), maize (Beriaux et al 2013;Blaes et al 2006), and wheat (Fontanelli et al 2013;Lievens and Verhoest 2011;Mattia et al 2003;Sonobe et al 2014c). Furthermore, SAR data have been used to identify specific crop types, such as paddy fields (Choudhury and Chakraborty 2006;Kuenzer and Knauer 2013).…”

Section: Introductionmentioning

confidence: 99%

Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-X

Sonobe

Tani

Wang

et al. 2014

International Journal of Remote Sensing

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This article describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X data. In the study area, beans, beets, grasslands, maize, potatoes, and winter wheat were cultivated. Although classification maps are required for both management and estimation of agricultural disaster compensation, those techniques have yet to be established. Some supervised learning models may allow accurate classification. Therefore, comparisons among the classification and regression tree (CART), the support vector machine (SVM), and random forests (RF) were performed. SVM was the optimum algorithm in this study, achieving an overall accuracy of 89.1% for the same-year classification, which is the classification using the training data in 2009 to classify the test data in 2009, and 78.0% for the cross-year classification, which is the classification using the training data in 2009 to classify the data in 2012

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“…These parameters are primarily crop height, crop LAI and crop biomass. Sonobe et al (2014) measured the height, moisture content and dry matter of the crops during nearly the same interval time as the collection of the TerraSAR-X data in the same area and studied the relationships between the measured parameters and the SAR data, including the sigma naught and coherence. The results confirmed that the X-band SAR data possessed great potential for the development of an operational system for monitoring wheat growth (Sonobe et al 2014).…”

Section: Crop Parameter Estimationmentioning

confidence: 99%

Research advances of SAR remote sensing for agriculture applications: A review

Liu

Chen

Shao

et al. 2019

Journal of Integrative Agriculture

155

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Synthetic aperture radar (SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.

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Winter Wheat Growth Monitoring Using Multi-temporal TerraSAR-X Dual-polarimetric Data

Cited by 6 publications

References 15 publications

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-X

Research advances of SAR remote sensing for agriculture applications: A review

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