Estimation of Rice Crop Height From X- and C-Band PolSAR by Metamodel-Based Optimization

Yüzügüllü, Onur; Erten, Esra; Hajnsek, Irena

doi:10.1109/jstars.2016.2575362

Cited by 29 publications

(22 citation statements)

References 26 publications

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“…In order to consider the spatial heterogeneity of the field, the mean (µ σ qp ) and standard deviation (ν σ qp ) of the measured backscattering intensities are calculated for each data cluster of polarimetric channels. Each O S qp is then bounded according to the intensity constraints given byEquation (10).…”

Section: Parameter Space Search Algorithmmentioning

confidence: 99%

Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion

et al. 2017

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Abstract:Rice crops are important in the global food economy, and new techniques are being implemented for their effective management. These techniques rely mainly on the changes in the phenological cycle, which can be investigated by remote sensing systems. High frequency and high spatial resolution Synthetic Aperture Radar (SAR) sensors have great potential in all-weather conditions for detecting temporal phenological changes. This study focuses on a novel approach for growth stage determination of rice fields from SAR data using a parameter space search algorithm. The method employs an inversion scheme for a morphology-based electromagnetic backscattering model. Since such a morphology-based model is complicated and computationally expensive, a surrogate metamodel-based inversion algorithm is proposed for the growth stage estimation. The approach is designed to provide estimates of crop morphology and corresponding growth stage from a continuous growth scale. The accuracy of the proposed method is tested with ground measurements from Turkey and Spain using the images acquired by the TerraSAR-X (TSX) sensor during a full growth cycle of rice crops. The analysis shows good agreement for both datasets. The results of the proposed method emphasize the effectiveness of X-band PolSAR data for morphology-based growth stage determination of rice crops.

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Section: Parameter Space Search Algorithmmentioning

confidence: 99%

Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion

et al. 2017

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“…In current precision agriculture practices, two of the most commonly used data sources are multi-spectral optical (MS) [1] and synthetic aperture radar (SAR) [12] data. The imagery from MS systems provides reflectance information in the visible and infrared part of the light spectrum, which usually ranges between 300 to 2500 nm.…”

Section: Introductionmentioning

confidence: 99%

Understanding Fields by Remote Sensing: Soil Zoning and Property Mapping

Yüzügüllü

Lorenz²,

Fröhlich³

et al. 2020

Remote Sensing

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Precision agriculture aims to optimize field management to increase agronomic yield, reduce environmental impact, and potentially foster soil carbon sequestration. In 2015, the Copernicus mission, with Sentinel-1 and -2, opened a new era by providing freely available high spatial and temporal resolution satellite data. Since then, many studies have been conducted to understand, monitor and improve agricultural systems. This paper presents results from the SolumScire project, focusing on the prediction of the spatial distribution of soil zones and topsoil properties, such as pH, soil organic matter (SOM) and clay content in agricultural fields through random forest algorithms. For this purpose, samples from 120 fields were investigated. The zoning and soil property prediction has an accuracy greater than 90%. This is supported by a high agreement of the derived zones with farmer’s observations. The trained models revealed a prediction accuracy of 94%, 89% and 96% for pH, SOM and clay content, respectively. The obtained models for soil properties can support precision field management, the improvement of soil sampling and fertilization strategies, and eventually the management of soil properties such as SOM.

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“…Many CGSM are point-based and scaling this up to estimate yields across larger areas is an active research field [8][9][10]. Remote sensing can be used to estimate LAI, plant height and phenology, and provides spatial and temporal information on other crop growth parameters [11,12] which can reduce spatial variation and uncertainty [9] resulting in more accurate yield estimates across large areas [8]. This makes the study of the relationship between remote sensing information and LAI a focus for many remote sensing studies [5,13].…”

Section: Introductionmentioning

confidence: 99%

Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases

et al. 2019

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The objective of this study is to provide complete information on the dynamic relationship between X-band (3.11 cm) backscattering intensity (σ • ) and rice crop's leaf area index (LAI) at all growth phases. Though the relationship between X-band σ • and LAI has been previously explored, details on the relationship at the reproductive phase remain unstudied. LAI at the reproductive phase is important particularly at the heading stage where LAI reaches its maximum as it is closely related to grain yield, and at flowering stage where the total leaf area affects the amount of photosynthates. Therefore, this study examined the relationship of increasing LAI (vegetative to reproductive phase) and decreasing LAI (ripening phase) with TerraSAR-X (TSX) ScanSAR (3.11 cm) σ • at HH polarisation and 45 • incidence angle. The results showed a statistically significant (R 2 = 0.51, p value < 0.001) non-linear relationship of LAI with σ • at the vegetative to reproductive phase while no significant linear relationship was found at the ripening phase. This study completes the response curve of X-band σ • to LAI by filling in the information on the reproductive phase which more accurately characterises the dynamic relationship between the rice crop's LAI and X-band's σ • . This contributes to improved knowledge on the use of X-band data for estimating LAI for the whole crop cycle which is essential for the modelling of crop growth and estimation of yield.

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Estimation of Rice Crop Height From X- and C-Band PolSAR by Metamodel-Based Optimization

Cited by 29 publications

References 26 publications

Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion

Determining Rice Growth Stage with X-Band SAR: A Metamodel Based Inversion

Understanding Fields by Remote Sensing: Soil Zoning and Property Mapping

Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases

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