Assessment of Paddy Rice Height: Sequential Inversion of Coherent and Incoherent Models

Yüzügüllü, Onur; Erten, Esra; Hajnsek, Irena

doi:10.1109/jstars.2018.2844798

Cited by 13 publications

(13 citation statements)

References 34 publications

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“…Crop height is an important agronomic descriptor related to crop type, biomass estimation, phenological stage, potential yield, detection of growth anomalies (e.g., diseases, pests, weather disasters, and cereal lodging), and precision fertilization [1][2][3]. Traditional methods to monitor crop height by visual inspection require a huge workforce over large areas [4]. Synthetic Aperture Radar (SAR), with its capability of imaging in day and night and all weather conditions and its sensitivity to the geometric and physical properties of the target, has shown to be an effective remote sensing technique in crop biophysical parameter retrieval at regional and global scales.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the complexity of the physical scattering model, crop height estimation may require computationally expensive Monte Carlo simulations to relate the SAR measurements to parameters describing the entire canopy's physical characteristics [1]. Moreover, the inversion process of model parameters often leads to ill-posed problems due to a high-dimensional parameter space [1,4,13,14]. Although the merging of a metamodel (e.g., the polynomial chaos expansion (PCE)) with the backscattering model enables a significant reduction of the computational cost and the complexity involved in the inversion scheme, the growth stage needs to be identified in advance to narrow the solution space [4,13,14].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…The estimation of rice height using Pol-InSAR inversion was tested over test sites in Spain and Turkey with different rice varieties and over the whole cultivation campaign, from sowing to plant maturity. In [30], dual-pol inversion results were combined with stochastic inversion algorithms for small-scaled morphological changes for the complete growth cycle of rice plant. However, in order to obtain dual-pol data in the TDX antenna system, two polarizations are required by toggling the polarization from pulse to pulse, resulting in lower spatial resolution or narrower swath, compared to single-pol TDX data [31].…”

Section: Introductionmentioning

confidence: 99%

Vegetation Height Estimate in Rice Fields Using Single Polarization TanDEM-X Science Phase Data

2018

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This study presents the retrieval of rice paddy height using single polarization (single-pol) interferometric SAR (InSAR) data by means of model-based height inversion without an external DEM. A total of eight TanDEM-X (TDX) scenes were used and the TDX images were; acquired using a large cross-track baseline configuration during the TDX Science Phase (June–August 2015). A single-pol inversion approach for a flooded rice field is proposed and evaluated over the Buan test site in South Korea. A novel approach is adopted for the estimation of the ground (i.e., water level) interferometric phase within a flooded rice paddy from TDX data acquired during a period of early rice growth. It is consequently possible to apply the model-based inversion algorithm to the single-pol InSAR data. Rice height maps during the rice growth cycle presented and validated by field measurements. The results demonstrated the high performance of the inversion with a correlation coefficient of 0.78 and an RMSE of 0.10 m. The proposed methodology will be useful to monitor rice plants and to predict a gross rice yield, along with dual and fully polarimetric interferometric SAR data.

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“…In the literature, one can find different strategies to retrieve the physical parameters of the scene employed in the RVoG model (see [8], [11], [14], [16]). Taking as reference [6] and [15], the methodology employed here particularizes the inversion procedure for the dual-pol TanDEM-X bistatic data, adapted to the specific properties of a scene in which the DB contribution dominates.…”

Section: A Inversion Algorithmmentioning

confidence: 99%

“…Unfortunately, until the operation of TanDEM-X, this formulation could not be tested on real data. However, all the experiments illustrated in the literature using PolInSAR with TanDEM-X data for forests [6]- [9] and most of the crop cases [14], [16], [17] have ignored the mentioned decorrelation term, and the vegetation heights obtained were accurate enough. Therefore, to date, the real extent of the influence of this term on the retrieval of the scene parameters still remains unclear.…”

mentioning

confidence: 99%

Estimation of RVoG Scene Parameters by Means of PolInSAR With TanDEM-X Data: Effect of the Double-Bounce Contribution

Romero-Puig

López-Sánchez

Ballester-Berman

2020

IEEE Trans. Geosci. Remote Sensing

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This article evaluates the effect of the doublebounce (DB) decorrelation term that appears in single-pass bistatic acquisitions, as in the TanDEM-X system, on the inversion of scene parameters by means of polarimetric SAR interferometry (PolInSAR). The retrieval of all scene parameters involved in the Random Volume over Ground (RVoG) model (i.e., ground topography, vegetation height, extinction, and ground-to-volume ratios) is affected by this term when the radar response from the ground is dominated by the DB. The estimation error in all these parameters is analyzed by means of simulations over a wide range of system configurations and scene variables for both agricultural crops and forest scenarios. Simulations demonstrate that the inclusion of the DB term, which complicates the inversion algorithm, is necessary for the angles of incidence shallower than 30 • to achieve an estimation error below 10% in vegetation height and to avoid a significant underestimation in the groundto-volume ratios. At steep incidences, this decorrelation term does not affect the estimation of vegetation height and groundto-volume ratios. Regarding the extinction, this parameter is intrinsically not well estimated, since most retrieved values are close to the initial guesses employed for the optimization algorithm, regardless of the use or not of the DB decorrelation term. Finally, these findings are compared with the experimental results from the TanDEM-X data acquired over the rice fields in Spain for the available system parameters (baseline and incidence angle) of the acquired data set.

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Assessment of Paddy Rice Height: Sequential Inversion of Coherent and Incoherent Models

Cited by 13 publications

References 34 publications

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

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

Vegetation Height Estimate in Rice Fields Using Single Polarization TanDEM-X Science Phase Data

Estimation of RVoG Scene Parameters by Means of PolInSAR With TanDEM-X Data: Effect of the Double-Bounce Contribution

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