Rice Crop Height Inversion from TanDEM-X PolInSAR Data Using the RVoG Model Combined with the Logistic Growth Equation

The random volume over ground (RVoG) model has been widely used in the field of vegetation height retrieval based on polarimetric interferometric synthetic aperture radar (PolInSAR) data. However, to date, its application in a time-series framework has not been considered. In this study, the logistic growth equation was introduced into the PolInSAR method for the first time to assist in estimating crop height, and an improved inversion scheme for the corresponding RVoG model parameters combined with the logistic growth equation was proposed. This retrieval scheme was tested using a time series of single-pass HH-VV bistatic TanDEM-X data and reference data obtained over rice fields. The effectiveness of the time-series RVoG model based on the logistic growth equation and the convenience of using equation parameters to evaluate vegetation growth status were analyzed at three test plots. The results show that the improved method can effectively monitor the height variation of crops throughout the whole growth cycle and the rice height estimation achieved an accuracy better than when single dates were considered. This proved that the proposed method can reduce the dependence on interferometric sensitivity and can achieve the goal of monitoring the whole process of rice height evolution with only a few PolInSAR observations.This research was funded in part by the National Natural Science Foundation of China (grant nos. 41820104005, 42030112, 41904004) and in part by the and the Spanish Ministry of Science and Innovation (grant no. PID2020-117303GB-C22)

A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation

This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10°.This work was supported in part by National Nature Science Foundation of China under Grant 41531068, 41371335, 41671356, and 41274010, the Spanish Ministry of Economy and Competitiveness and EU FEDER under Project TIN2014-55413-C2-2-P, China Scholarship Council under Grant 201406370079, and Hunan Provincial Department of Education Science Research Key Project 15A074. The BioSAR2008 campaign data is provided by European Space Agency under the ESA EO Project 14751

Estimation of Forest Height Using Spaceborne Repeat-Pass L-Band InSAR Correlation Magnitude over the US State of Maine

This paper describes a novel, simple and efficient approach to estimate forest height over a wide region utilizing spaceborne repeat-pass InSAR correlation magnitude data at L-band. We start from a semi-empirical modification of the RVoG model that characterizes repeat-pass InSAR correlation with large temporal baselines (e.g., 46 days for ALOS) by taking account of the temporal change effect of dielectric fluctuation and random motion of scatterers. By assuming (1) the temporal change parameters and forest backscatter profile/extinction coefficient follow some mean behavior across each inteferogram; (2) there is minimal ground scattering contribution for HV-polarization; and (3) the vertical wavenumber is small, a simplified inversion approach is developed to link the observed HV-polarized InSAR correlation magnitude to forest height and validated using ALOS/PALSAR repeat-pass observations against LVIS lidar heights over the Howland Research Forest in central Maine, US (with RMSE \u3c 4 m at a resolution of 32 hectares). The model parameters derived from this supervised regression are used as the basis for propagating the estimates of forest height to available interferometric pairs for the entire state of Maine, thus creating a state-mosaic map of forest height. The present approach described here serves as an alternative and complementary tool for other PolInSAR inversion techniques when full-polarization data may not be available. This work is also meant to be an observational prototype for NASA’s DESDynI-R (now called NISAR) and JAXA’s ALOS-2 satellite missions

Electromagnetic Scattering Models for InSAR Correlation Measurements of Vegetation and Snow

Interferometric Synthetic Aperture Radar (InSAR) has proved successful and efficient in measuring the vertical structure of the distributed targets such as vegetation and snow, which are dominated by volume scattering. In particular, the InSAR correlation measurement has been utilized to retrieve the target vertical structural information. One existing and well-known electromagnetic scattering model of the InSAR correlation was first brought forward focusing on the single-pass InSAR observation of a sparse random medium like vegetation. However, the lack of the adaption of this InSAR scattering model for repeat-pass InSAR observation of vegetation as well as for single-pass InSAR observation of snow by considering its dense medium characteristics, essentially constrain fully exploiting InSAR\u27s capability of measuring sparse and dense medium characteristics. In this work, the well-known InSAR scattering model will be adapted to accommodate the two scenarios: 1) repeat-pass InSAR observation of vegetation and 2) single-pass InSAR observation of snow and considering its dense medium characteristics. Theoretical model derivations as well as parameter retrieval approaches are demonstrated for both of the applications, respectively. Both of the simulated and ground validation results are also presented. The InSAR scattering models along with the parameter retrieval analysis described in this work will expand InSAR\u27s capability as well as the range of vegetation and snow characteristics that can be retrieved by single-pass and/or repeat-pass InSAR systems

A Simple RVoG Test for PolInSAR Data

In this paper, we present a simple algorithm for assessing the validity of the RVoG model for PolInSAR-based inversion techniques. This approach makes use of two important features characterizing a homogeneous random volume over a ground surface, i.e., the independence on polarization states of wave propagation through the volume and the structure of the polarimetric interferometric coherency matrix. These two features have led to two different methods proposed in the literature for retrieving the topographic phase within natural covers, i.e., the well-known line fitting procedure and the observation of the (1, 2) element of the polarimetric interferometric coherency matrix. We show that differences between outputs from both approaches can be interpreted in terms of the PolInSAR modeling based on the Freeman-Durden concept, and this leads to the definition of a RVoG/non-RVoG test. The algorithm is tested with both indoor and airborne data over agricultural and tropical forest areas.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Project TEC2011-28201-C02-02

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

This article evaluates the effect of the double-bounce (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 ground-to-volume ratios. At steep incidences, this decorrelation term does not affect the estimation of vegetation height and ground-to-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.This work was supported in part by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI), and in part by the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P. The work of Noelia Romero-Puig was supported in part by the Generalitat Valenciana and in part by the European Social Fund (ESF) under Grant ACIF/2018/204

Retrieval of vegetation height in rice fields using polarimetric SAR interferometry with TanDEM-X data

This work presents for the first time a demonstration with satellite data of polarimetric SAR interferometry (PolInSAR) applied to the retrieval of vegetation height in rice fields. Three series of dual-pol interferometric SAR data acquired with large baselines (2–3 km) by the TanDEM-X system during its science phase (April–September 2015) are exploited. A novel inversion algorithm especially suited for rice fields cultivated in flooded soil is proposed and evaluated. The validation is carried out over three test sites located in geographically different areas: Sevilla (SW Spain), Valencia (E Spain), and Ipsala (W Turkey), in which different rice types are present. Results are obtained during the whole growth cycle and demonstrate that PolInSAR is useful to produce accurate height estimates (RMSE 10–20 cm) when plants are tall enough (taller than 25–40 cm), without relying on external reference information.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under project TIN2014-55413-C2-2-P. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement 606983, and the Land-SAF (the EUMETSAT Network of Satellite Application Facilities) project. The in-situ measurements in the Ipsala site were conducted with the funding of The Scientific and Technological Research Council of Turkey (TUBITAK, Project No.: 113Y446)

Application of the Trace Coherence to HH-VV PolInSAR TanDEM-X Data for Vegetation Height Estimation

This article investigates, for the first time, the inclusion of the operator Trace Coherence (TrCoh) in polarimetric and interferometric synthetic aperture radar (SAR) methodologies for the estimation of biophysical parameters of vegetation. A modified inversion algorithm based on the well-known Random Volume over Ground (RVoG) model, which employs the TrCoh, is described and evaluated. In this regard, a different set of coherence extrema is used as input for the retrieval stage. In addition, the proposed methodology improves the inversion algorithm by employing analytical solutions rather than approximations. Validation is carried out exploiting single-pass HH-VV bistatic TanDEM-X data, together with reference data acquired over a paddy rice area in Spain. The added value of the TrCoh and the convenience of the use of analytical solutions are assessed by comparing with the conventional polarimetric SAR interferometry (PolInSAR) algorithm. Results demonstrate that the modified proposed methodology is computationally more effective than current methods on this dataset. For the same scene, the steps required for inversion are computed in 6 min with the conventional method, while it only takes 6 s with the proposed approach. Moreover, vegetation height estimates exhibit a higher accuracy with the proposed method in all fields under evaluation. The root-mean-squared error reached with the modified method improves by 7 cm with respect to the conventional algorithm

Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version