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10 pages, 5621 KiB

Open AccessCommunication

Analysis of Acoustic–Magnetic Fields Induced by Underwater Pressure Wave in a Finite-Depth Ocean

by Yuanguo Zhou, Peng Huang, Guoqing Yang, Shangqing Liang, Qiang Ren and Shiwei Tian

Remote Sens. 2023, 15(5), 1191; https://doi.org/10.3390/rs15051191 - 21 Feb 2023

Cited by 1 | Viewed by 1741

Abstract

As underwater disturbances (natural or artificial) occur in the ocean, moving seawater crossing the geomagnetic fields will produce weak circular currents. These currents can induce measurable magnetic fields, which might be useful for monitoring ocean internal waves using aeromagnetic survey. In this research, [...] Read more.

As underwater disturbances (natural or artificial) occur in the ocean, moving seawater crossing the geomagnetic fields will produce weak circular currents. These currents can induce measurable magnetic fields, which might be useful for monitoring ocean internal waves using aeromagnetic survey. In this research, a spectral-element method (SEM) based on Gauss–Lobatto–Legendre (GLL) polynomials is presented to characterize the magnetic field induced by the underwater pressure waves. A concise mathematical model is established through combining the acoustic wave equations and Maxwell’s equations. Specifically, the acoustic–magnetic coupling simulation adopts the nodal-based SEM for acoustic analysis and edge-based SEM for electromagnetic analysis. The proposed SEM has spectral accuracy, as the error exponentially decreases with the order of the basis functions. Additionally, by adopting an independent modeling and mesh scheme in two solvers, respectively, the waste of computing resources is avoided. The experimental analysis demonstrates that the induced magnetic fields mechanically propagate with the acoustic wave, producing the pseudo-radiation phenomenon. The signals of these magnetic fields may extend for tens of kilometers and exist for hours under certain circumstances, which provide a theoretical basis for underwater target identification via high-sensitivity atomic magnetometer. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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22 pages, 6467 KiB

Open AccessArticle

High-Resolution Imaging of Radiation Brightness Temperature Obtained by Drone-Borne Microwave Radiometer

by Xiangkun Wan, Xiaofeng Li, Tao Jiang, Xingming Zheng, Lei Li and Xigang Wang

Remote Sens. 2023, 15(3), 832; https://doi.org/10.3390/rs15030832 - 1 Feb 2023

Cited by 1 | Viewed by 1891

Abstract

A digital automatic gain compensation (AGC) drone-borne K-band microwave radiometer with continuous high-speed acquisition and fast storage functions is designed and applied to obtain high-resolution radiation brightness temperature (TB) images. In this paper, the composition of the drone-borne passive microwave observation system is [...] Read more.

A digital automatic gain compensation (AGC) drone-borne K-band microwave radiometer with continuous high-speed acquisition and fast storage functions is designed and applied to obtain high-resolution radiation brightness temperature (TB) images. In this paper, the composition of the drone-borne passive microwave observation system is introduced, a data processing method considering the topography and angle correction is proposed, the error analysis of the projection process is carried out, and finally, a high-resolution microwave radiation TB image is obtained by a demonstration area experiment. The characteristics of the radiometer are tested by experiments, and the standard deviation of the TB is 1K. The data processing method proposed is verified using a demonstration case. The corrected data have a good correlation with the theoretical values, of which the R² is 0.87. A high-resolution radiation TB image is obtained, and the results show the TB characteristics of different objects well. The boundary of the ground object is closer to the real value after correction. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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17 pages, 4876 KiB

Open AccessArticle

Assessment of Effective Roughness Parameters for Simulating Sentinel-1A Observation and Retrieving Soil Moisture over Sparsely Vegetated Field

by Xiaojing Wu

Remote Sens. 2022, 14(23), 6020; https://doi.org/10.3390/rs14236020 - 28 Nov 2022

Cited by 4 | Viewed by 1608

Abstract

The variability of surface roughness may lead to relatively large dynamic of backscatter coefficient observed by the synthetic aperture radar (SAR), which complicates the soil moisture (SM) retrieval process based on active remote sensing. The effective roughness parameters are commonly used for parameterizing [...] Read more.

The variability of surface roughness may lead to relatively large dynamic of backscatter coefficient observed by the synthetic aperture radar (SAR), which complicates the soil moisture (SM) retrieval process based on active remote sensing. The effective roughness parameters are commonly used for parameterizing the soil scattering models, the values of which are often assumed to be constant during different study periods for the same site. This paper investigates the reasonableness of this hypothesis from the perspective of backscatter coefficient simulation and SM retrieval using high resolution SAR data. Three years of Sentinel-1A data from 2016 to 2018 were collected over a sparsely vegetated field within the REMEDHUS SM monitoring network. The advanced integral equation model (AIEM) and Dobson dielectric mixing model were combined for optimizing the effective roughness parameters, as well as simulating the backscatter coefficient and retrieving the SM. The effective roughness parameters were optimized at different temporal periods, such as 2016, 2017, 2018, 2016 + 2017, 2017 + 2018, and 2016 + 2017 + 2018, to analyze their temporal dynamics. It was found that: (1) the effective roughness parameters optimized at different temporal periods are very close to each other; (2) the simulated backscatter from AIEM is consistent with Sentinel-1A observation with root mean square errors (RMSEs) between 1.133 and 1.163 dB and correlation coefficient ® value equals to 0.616; (3) the seasonal dynamics ofin situ SM is well-captured by the retrieved SM with R values floating at 0.685 and RMSEs ranging from 0.049 to 0.052 m³/m³; and (4) inverse of the AIEM with the implementation of effective roughness parameters achieves better performance for SM retrieval than the change detection method. These findings demonstrate that the assumption on the constant effective roughness parameters during the study period of at least three years is reasonable. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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21 pages, 7049 KiB

Open AccessArticle

Development of a Pixel-Wise Forest Transmissivity Model at Frequencies of 19 GHz and 37 GHz for Snow Depth Inversion in Northeast China

by Guang-Rui Wang, Xiao-Feng Li, Jian Wang, Yan-Lin Wei, Xing-Ming Zheng, Tao Jiang, Xiu-Xue Chen, Xiang-Kun Wan and Yan Wang

Remote Sens. 2022, 14(21), 5483; https://doi.org/10.3390/rs14215483 - 31 Oct 2022

Cited by 4 | Viewed by 1474

Abstract

Satellite passive microwave remote sensing has been extensively used to estimate snow depth (SD) and snow water equivalent (SWE) across both regional and continental scales. However, the presence of forests causes significant uncertainties in the estimations of snow parameters. Forest transmissivity is one [...] Read more.

Satellite passive microwave remote sensing has been extensively used to estimate snow depth (SD) and snow water equivalent (SWE) across both regional and continental scales. However, the presence of forests causes significant uncertainties in the estimations of snow parameters. Forest transmissivity is one of the most important parameters for describing the microwave radiation and scattering characteristics of forest canopies. Although many researchers have constructed models for the functional relationship between forest transmissivity and forest vegetation parameters (e.g., stand growth and accumulation), such relationships are strongly limited by the inversion accuracy of vegetation parameters, forest distribution types, and scale-transformation effects in terms of regional or global scale applications. In this research, we propose a pixel-wise forest transmissivity estimation model (Pixel-wise γ Model) based on long-term series satellite brightness temperature (TB) data for the satellite remote sensing inversion of snow parameters. The model performance is evaluated and applied in SD inversion. The results show that the SD inversion errors RMSE and Bias are 9.8 cm and −1.5 cm, respectively; the SD inversion results are improved by 41% and 84% after using the Pixel-wise γ Model, compared with the forest transmissivity model applied in the GlobSnow v3.0 product. The proposed forest transmissivity model does not depend on forest cover parameters and other ground measurement parameters, which greatly improves its application scope and simplicity. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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20 pages, 6848 KiB

Open AccessArticle

Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin

by Zhilan Zhou, Lei Fan, Gabrielle De Lannoy, Xiangzhuo Liu, Jian Peng, Xiaojing Bai, Frédéric Frappart, Nicolas Baghdadi, Zanpin Xing, Xiaojun Li, Mingguo Ma, Xin Li, Tao Che, Liying Geng and Jean-Pierre Wigneron

Remote Sens. 2022, 14(21), 5468; https://doi.org/10.3390/rs14215468 - 30 Oct 2022

Cited by 3 | Viewed by 2854

Abstract

Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which [...] Read more.

Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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21 pages, 7673 KiB

Open AccessArticle

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

by Nan Li, Juan M. Lopez-Sanchez, Haiqiang Fu, Jianjun Zhu, Wentao Han, Qinghua Xie, Jun Hu and Yanzhou Xie

Remote Sens. 2022, 14(20), 5109; https://doi.org/10.3390/rs14205109 - 13 Oct 2022

Cited by 8 | Viewed by 2216

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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21 pages, 5942 KiB

Open AccessArticle

A Comprehensive Comparison of Machine Learning and Feature Selection Methods for Maize Biomass Estimation Using Sentinel-1 SAR, Sentinel-2 Vegetation Indices, and Biophysical Variables

by Chi Xu, Yanling Ding, Xingming Zheng, Yeqiao Wang, Rui Zhang, Hongyan Zhang, Zewen Dai and Qiaoyun Xie

Remote Sens. 2022, 14(16), 4083; https://doi.org/10.3390/rs14164083 - 20 Aug 2022

Cited by 16 | Viewed by 3796

Abstract

Rapid and accurate estimation of maize biomass is critical for predicting crop productivity. The launched Sentinel-1 (S-1) synthetic aperture radar (SAR) and Sentinel-2 (S-2) missions offer a new opportunity to map biomass. The selection of appropriate response variables is crucial for improving the [...] Read more.

Rapid and accurate estimation of maize biomass is critical for predicting crop productivity. The launched Sentinel-1 (S-1) synthetic aperture radar (SAR) and Sentinel-2 (S-2) missions offer a new opportunity to map biomass. The selection of appropriate response variables is crucial for improving the accuracy of biomass estimation. We developed models from SAR polarization indices, vegetation indices (VIs), and biophysical variables (BPVs) based on gaussian process regression (GPR) and random forest (RF) with feature optimization to retrieve maize biomass in Changchun, Jilin province, Northeastern China. Three new predictors from each type of remote sensing data were proposed based on the correlations to biomass measured in June, July, and August 2018. The results showed that a predictor combined by vertical-horizontal polarization (VV), vertical-horizontal polarization (VH), and the difference of VH and VV (VH-VV) derived from S-1 images of June, July, and August, respectively, with GPR and RF, provided a more accurate estimation of biomass (R² = 0.81–0.83, RMSE = 0.40–0.41 kg/m²) than the models based on single SAR polarization indices or their combinations, or optimized features (R² = 0.04–0.39, RMSE = 0.84–1.08 kg/m²). Among the S-2 VIs, the GPR model using a combination of ratio vegetation index (RVI) of June, normalized different infrared index (NDII) of July, and normalized difference vegetation index (NDVI) of August achieved a result with R² = 0.83 and RMSE = 0.39 kg/m², much better than single VIs or their combination, or optimized features (R² of 0.31–0.77, RMSE of 0.47–0.87 kg/m²). A BPV predictor, combined with leaf chlorophyll content (CAB) in June, canopy water content (CWC) in July, and fractional vegetation cover (FCOVER) in August, with RF, also yielded the highest accuracy (R² = 0.85, RMSE = 0.38 kg/m²) compared to that of single BPVs or their combinations, or optimized subset. Overall, the three combined predictors were found to be significant contributors to improving the estimation accuracy of biomass with GPR and RF methods. This study clearly sheds new insights on the application of S-1 and S-2 data on maize biomass modeling. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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20 pages, 8892 KiB

Open AccessArticle

Finite-Region Approximation of EM Fields in Layered Biaxial Anisotropic Media

by Zhuangzhuang Kang, Hongnian Wang and Changchun Yin

Remote Sens. 2022, 14(15), 3836; https://doi.org/10.3390/rs14153836 - 8 Aug 2022

Cited by 1 | Viewed by 1763

Abstract

A new algorithm is developed to accurately compute the electromagnetic (EM) fields in the layered biaxial anisotropic media. We enclose the computational region in an infinitely long rectangular region by four vertical truncation planes and establish the corresponding algorithm to approximate the EM [...] Read more.

A new algorithm is developed to accurately compute the electromagnetic (EM) fields in the layered biaxial anisotropic media. We enclose the computational region in an infinitely long rectangular region by four vertical truncation planes and establish the corresponding algorithm to approximate the EM fields in the entire space. The EM fields in this region are expanded as a two-dimensional (2-D) Fourier series of the transverse variables. By using the spectral state variable method, the generalized reflection coefficient matrices and transmission matrices are then derived to determine the Fourier coefficients per layer. Therefore, we can obtain the spatial-domain EM fields by summing the 2-D Fourier series. To enhance the accuracy and efficiency of this algorithm, we apply the method of images to estimate the influence of the artificial boundaries on the EM fields at the observer. We then further develop a quantitative principle to choose the proper size of the region according to the desired error tolerance. With the proper choice, the summation of the series can achieve satisfactory accuracy. This algorithm is finally applied to simulate the responses of the triaxial logging tool in transversely isotropic and biaxial anisotropic media and is verified through comparisons to the other method. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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21 pages, 5942 KiB

Open AccessArticle

Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season

by Bingze Li, Ming Ma, Shengbo Chen, Xiaofeng Li, Si Chen and Xingming Zheng

Remote Sens. 2022, 14(13), 3017; https://doi.org/10.3390/rs14133017 - 23 Jun 2022

Cited by 3 | Viewed by 1671

Abstract

The accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input [...] Read more.

The accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input parameters and the latter has the problem of poor portability. In this study, new semi-empirical geometric and physical parameters of the maize canopy model (GPMCM) crop model adapted to northeast China were proposed based on a time-series field datasets collected from 11 sites in the Nong’an and Changling Counties of Jilin Province, China, during DOY (day of year) 163 to DOY 278 in 2021. The allocation characteristics of and correlations between each maize canopy parameter were investigated for the whole growing season using the 22 algorithms of crop parameters, and the following conclusions were obtained. (1) The high correlation coefficient (

R

mean = 0.79) of LAI with other canopy parameters indicated that it was a good indicator for predicting other parameters. (2) Better performance was achieved by the regression method based on the two-stage simulation. The root-mean-squared error (

R M S E

) of geometric parameters including maize height, stem long radius, and short radius were 12.91 cm, 0.74 mm, and 0.73 mm, respectively, and the

R M S E

of the physical parameters including the FAGB, AGB, VWC, and RWC of the stems and leaves, ranged from 0.05 kg/m² to 4.24 kg/m² (2.0% to 12.9% for mean absolute percentage error (

M A P E

)). (3) The extension of the field-scale GPMCM to the 500 m MODIS-scale still provided a good accuracy (

M A P E

: 11% to 18.5%) and confirmed the feasibility of the large-scale application of the GPMCM. The proposed CPMCM can predict the temporal dynamics of maize geometric and physical parameters, and it is helpful to establish the forward and reverse models of remote sensing and improve the inversion accuracy of crop parameters. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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23 pages, 13293 KiB

Open AccessArticle

Comparison of Machine Learning-Based Snow Depth Estimates and Development of a New Operational Retrieval Algorithm over China

by Jianwei Yang, Lingmei Jiang, Jinmei Pan, Jiancheng Shi, Shengli Wu, Jian Wang and Fangbo Pan

Remote Sens. 2022, 14(12), 2800; https://doi.org/10.3390/rs14122800 - 10 Jun 2022

Cited by 7 | Viewed by 2715

Abstract

Snow depth estimation with passive microwave (PM) remote sensing is challenged by spatial variations in the Earth’s surface, e.g., snow metamorphism, land cover types, and topography. Thus, traditional static snow depth retrieval algorithms cannot capture snow thickness well. In this study, we present [...] Read more.

Snow depth estimation with passive microwave (PM) remote sensing is challenged by spatial variations in the Earth’s surface, e.g., snow metamorphism, land cover types, and topography. Thus, traditional static snow depth retrieval algorithms cannot capture snow thickness well. In this study, we present a new operational retrieval algorithm, hereafter referred to as the pixel-based method (0.25° × 0.25° grid-level), to provide more accurate and nearly real-time snow depth estimates. First, the reference snow depth was retrieved using a previously proposed model in which a microwave snow emission model was coupled with a machine learning (ML) approach. In this process, an effective grain size (effGS) value was optimized by utilizing the snow microwave emission model, and then the nonlinear relationship between snow depth and multiple predictive variables, e.g., effGS, longitude, elevation, and brightness temperature (Tb) gradients, was established with the ML technique to retrieve reference snow depth data. To select a robust and well-performing ML approach, we compared the performance of widely used support vector regression (SVR), artificial neural network (ANN) and random forest (RF) algorithms over China. The results show that the three ML models performed similarly in snow depth estimation, which was attributed to the inclusion of effGS in the training samples. In this study, the RF model was used to retrieve the snow depth reference dataset due to its slightly stronger robustness according to our comparison of results. Second, the pixel-based algorithm was built based on the retrieved reference snow depth dataset and satellite Tb observations (18.7 GHz and 36.5 GHz) from Advanced Microwave Scanning Radiometer 2 (AMSR2) during the 2012–2020 period. For the pixel-based algorithm, the fitting coefficients were achieved dynamically pixel by pixel, making it superior to the traditional static methods. Third, the built pixel-based algorithm was verified using ground-based observations and was compared to the AMSR2, GlobSnow-v3.0, and ERA5-land products during the 2012–2020 period. The pixel-based algorithm exhibited an overall unbiased root mean square error (unRMSE) and R² of 5.8 cm and 0.65, respectively, outperforming GlobSnow-v3.0, with unRMSE and R² values of 9.2 cm and 0.22, AMSR2, with unRMSE and R² values of 18.5 cm and 0.13, and ERA5-land, with unRMSE and R² values of 10.5 cm and 0.33, respectively. However, the pixel-based algorithm estimates were still challenged by the complex terrain, e.g., the unRMSE was up to 17.4 cm near the Tien Shan Mountains. The proposed pixel-based algorithm in this study is a simple and operational method that can retrieve accurate snow depths based solely on spaceborne PM data in comparatively flat areas. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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20 pages, 25407 KiB

Open AccessArticle

Pseudo-Spectral Time-Domain Method for Subsurface Imaging with the Lunar Regolith Penetrating Radar

by Yuxian Zhang, Naixing Feng, Guoda Xie, Lixia Yang and Zhixiang Huang

Remote Sens. 2022, 14(12), 2791; https://doi.org/10.3390/rs14122791 - 10 Jun 2022

Cited by 1 | Viewed by 1858

Abstract

Recently and successfully, the Chang’E-5 (CE-5) lander was launched on a mission to bring 1.731 kg of lunar soil back to Earth. To investigate various compositions of lunar regolith, we apply the Lunar Regolith Penetrating Radar (LRPR) as the same scientific payload installed [...] Read more.

Recently and successfully, the Chang’E-5 (CE-5) lander was launched on a mission to bring 1.731 kg of lunar soil back to Earth. To investigate various compositions of lunar regolith, we apply the Lunar Regolith Penetrating Radar (LRPR) as the same scientific payload installed on the CE-5 lander. Based on the high-accuracy imaging technique, we achieve subsurface imaging to process LRPR-measured data collected from the lunar-like exploration tests in our laboratory. In this paper, we propose the pseudo-spectral time-domain (PSTD) method as the underlying code to implement the reverse-time migration (RTM) method and restore the uncertain subsurface area. With the significant advantage of lower spatial sampling density, the PSTD-RTM method not only saves major computational resources, but also rapidly confirms the object prediction in the effective imaging area. To further analyze the LRPR measured data, we employ the spectrum window to remove high- and low-frequency noise, and thus improve imaging visibility to some extent. The imaging results in this paper can prove the reliability and efficiency of the PSTD-RTM method for subsurface discoveries in planetary exploration. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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21 pages, 11805 KiB

Open AccessArticle

An NDVI Retrieval Method Based on a Double-Attention Recurrent Neural Network for Cloudy Regions

by Ran Jing, Fuzhou Duan, Fengxian Lu, Miao Zhang and Wenji Zhao

Remote Sens. 2022, 14(7), 1632; https://doi.org/10.3390/rs14071632 - 29 Mar 2022

Cited by 3 | Viewed by 2595

Abstract

NDVI is an important parameter for environmental assessment and precision agriculture that well-describes the status of vegetation. Nevertheless, the clouds in optical images often result in the absence of NDVI information at key growth stages. The integration of SAR and optical image features [...] Read more.

NDVI is an important parameter for environmental assessment and precision agriculture that well-describes the status of vegetation. Nevertheless, the clouds in optical images often result in the absence of NDVI information at key growth stages. The integration of SAR and optical image features will likely address this issue. Although the mapping of different data sources is complex, the prosperity of deep learning technology provides an alternative approach. In this study, the double-attention RNN architecture based on the recurrent neural network (RNN) and attention mechanism is proposed to retrieve NDVI data of cloudy regions. Overall, the NDVI is retrieved by the proposed model from two aspects: the temporal domain and the pixel neighbor domain. The performance of the double-attention RNN is validated through different cloud coverage conditions, input ablation, and comparative experiments with various methods. The results conclude that a high retrieval accuracy is guaranteed by the proposed model, even under high cloud coverage conditions (R² = 0.856, RMSE = 0.124). Using SAR images independently results in poor NDVI retrieval results (R² = 0.728, RMSE = 0.141) with considerable artifacts, which need to be addressed with auxiliary data, such as IDM features. Temporal and pixel neighbor features play an important role in improving the accuracy of NDVI retrieval (R² = 0.894, RMSE = 0.096). For the missing values of NDVI data caused by cloud coverage, the double-attention RNN proposed in this study provides a potential solution for information recovery. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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20 pages, 6157 KiB

Open AccessArticle

An Approach to Improve the Spatial Resolution and Accuracy of AMSR2 Passive Microwave Snow Depth Product Using Machine Learning in Northeast China

by Yanlin Wei, Xiaofeng Li, Li Li, Lingjia Gu, Xingming Zheng, Tao Jiang and Xiaojie Li

Remote Sens. 2022, 14(6), 1480; https://doi.org/10.3390/rs14061480 - 18 Mar 2022

Cited by 13 | Viewed by 2983

Abstract

Snow cover plays a highly critical role in the global water cycle and energy exchange. Accurate snow depth (SD) data are important for research on hydrologic processes, climate change, and natural disaster prediction. However, existing passive microwave (PMW) SD products have high uncertainty [...] Read more.

Snow cover plays a highly critical role in the global water cycle and energy exchange. Accurate snow depth (SD) data are important for research on hydrologic processes, climate change, and natural disaster prediction. However, existing passive microwave (PMW) SD products have high uncertainty in Northeast China owing to their coarse spatial resolution. Surface environment parameters should also be considered to reduce errors in existing SD products. Otherwise, it is difficult to accurately capture snow spatiotemporal variations, especially in a complex environment (e.g., mountain or forests areas). To improve the inversion accuracy and spatial resolution of existing SD products in Northeast China, a multifactor SD downscaling model was developed by combining PMW SD data from the AMSR2 sensor, optical snow cover extent data, and surface environmental parameters to produce fine scale (500 m × 500 m) and high precision SD data. Validations at 98 ground meteorological stations show that the developed model greatly improved the spatial resolution and inversion accuracy of the raw AMSR2 SD product; its root-mean-square error (RMSE) reduced from 26.15 cm of the raw product to 7.58 cm, and the correlation coefficient (R) increased from 0.39 to 0.53. For other SD products (WESTDC and FY), the multifactor SD downscaling model still has good applicability, it could further improve the performance of the WESTDC and FY SD products in time and space and achieve better inversion accuracy than raw SD products. Furthermore, the proposed model exhibited good agreement with the observed SD data in a field quadrat (3 km × 2 km) within the fine scale, with an error ranging between −2 and 2 cm. Compared with the existing downscaling methods, the proposed model presented the best performance. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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27 pages, 5436 KiB

Open AccessArticle

Gaussian Process Regression Model for Crop Biophysical Parameter Retrieval from Multi-Polarized C-Band SAR Data

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

Remote Sens. 2022, 14(4), 934; https://doi.org/10.3390/rs14040934 - 15 Feb 2022

Cited by 19 | Viewed by 4461

Abstract

Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), [...] Read more.

Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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12 pages, 3075 KiB

Open AccessTechnical Note

Z-Transform-Based FDTD Implementations of Biaxial Anisotropy for Radar Target Scattering Problems

by Yuxian Zhang, Naixing Feng, Jinfeng Zhu, Guoda Xie, Lixia Yang and Zhixiang Huang

Remote Sens. 2022, 14(10), 2397; https://doi.org/10.3390/rs14102397 - 17 May 2022

Cited by 5 | Viewed by 2130

Abstract

In this article, an efficient Z-transform-based finite-difference time-domain (Z-FDTD) is developed to implement and analyze electromagnetic scatterings in the 3D biaxial anisotropy. In terms of the Z-transform technique, we first discuss the conversion relationship between time- or frequency-domain derivative [...] Read more.

In this article, an efficient Z-transform-based finite-difference time-domain (Z-FDTD) is developed to implement and analyze electromagnetic scatterings in the 3D biaxial anisotropy. In terms of the Z-transform technique, we first discuss the conversion relationship between time- or frequency-domain derivative operators and the corresponding Z-domain operator, then build up the Z-transform-based iteration from the electric flux D converted to the electric field E based on dielectric tensor ε (and from the magnetic flux B converted to the magnetic field H in line with permeability tensor μ) by combining the constitutive formulations about the biaxial anisotropy. As a result, the iterative process about the Z-FDTD implementation can be smoothly carried out by means of combining with the Maxwell’s equations. To our knowledge, it is inevitably necessary for the absorbing boundary condition (ABC) to be considered in the electromagnetic scattering; hence, we utilize the unsplit-field complex-frequency-shifted perfectly matched layer (CFS-PML) to truncate the Z-FDTD’s physical region, and then capture time- and frequency-domain radiation with the electric dipole. In the 3D simulations, we select two different biaxial anisotropic models to validate the proposed formulations by using the popular commercial software COMSOL. Moreover, it is certain that those results are effective and available for electromagnetic scattering problems under the oblique incidence executed by the Z-FDTD method. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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14 pages, 3227 KiB

Open AccessTechnical Note

Numerical Prediction of Duality Principle with Bloch-Floquet Periodic Boundary Condition in Fully Anisotropic FDTD

by Naixing Feng, Yuxian Zhang, Zhixiang Huang, Lixia Yang and Xianliang Wu

Remote Sens. 2022, 14(5), 1135; https://doi.org/10.3390/rs14051135 - 25 Feb 2022

Cited by 1 | Viewed by 2236

Abstract

In this paper, in accordance with fully anisotropic electromagnetic materials, the duality principle is successfully validated by the fully anisotropic finite-difference time-domain (FDTD) with Bloch-Floquet periodic boundary condition (BPBC), which in theory is first effectively applied to the verification of time-domain electromagnetic computation. [...] Read more.

In this paper, in accordance with fully anisotropic electromagnetic materials, the duality principle is successfully validated by the fully anisotropic finite-difference time-domain (FDTD) with Bloch-Floquet periodic boundary condition (BPBC), which in theory is first effectively applied to the verification of time-domain electromagnetic computation. Starting from the conventional duality principle of isotropy, those conditions can be given without any loss term. Without loss of generality, the electromagnetic duality rules involving dielectric and magnetic lossy tensors could be obtained by combining complex extension from original real parameters. In our further research, we introduce the duality principle into the BPBC cases, then execute and validate three different fully anisotropic models by means of the FDTD method under either TE or TM modes. From highly accurate numerical point of view, we apply ourselves to a more effective verification which can forecast the reflection and transmission coefficients and detect the subsurface echoes through the duality principle. Full article

(This article belongs to the Special Issue Microwave Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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