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ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 42231

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Special Issue Editors

Dr. Takeo Tadono

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Guest Editor

Japan Aerospace Exploration Agency (JAXA), Earth Observation Research Center (EORC), 2-1-1 Sengen, Tsukuba, Ibaraki 305-8505, Japan
Interests: SAR; optical instrument; calibration; validation; hydrology; disaster; forest and ecosystem; photogrammetry
Special Issues, Collections and Topics in MDPI journals

Dr. Masato Ohki

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Guest Editor

Japan Aerospace Exploration Agency (JAXA), Earth Observation Research Center (EORC), 2-1-1 Sengen, Tsukuba, Ibaraki 305-8505, Japan
Interests: SAR; calibration; disaster; classification; polarimetry; interferometry

Special Issue Information

Dear Colleagues,

The Advanced Land Observing Satellite-2 (ALOS-2, nicknamed "DAICHI-2") was launched on May 24th, 2014, which is a follow-on mission of L-band Synthetic Aperture Radars (SARs) by the ALOS "DAICHI" from 2006 to 2011, and the Japanese Earth Resources Satellite-1 (JERS-1, “FUYO-1”) from 1992 to 1998. Thus, global coverage and almost three decades of L-band SAR data are currently available. In addition, ALOS-4 is under development and will be launched in the Japanese Fiscal Year 2022 as a successor to Japanese L-band SAR missions.

The mission objectives of ALOS-2 that were defined to fulfill social needs include the following: 1) disaster monitoring of damaged areas, both in considerable detail and when these areas may be large, 2) continuous updating of data archives related to national land and infrastructure information, 3) effective monitoring of cultivated areas, and 4) global monitoring of tropical rain forests to identify carbon sinks. The Phased Array-type L-band SAR-2 (PALSAR-2) mounted on ALOS-2 has capabilities of high-resolution, wide-swath width and both right and left looking observations, and is now operating globally to achieve these objectives. The Japan Aerospace Exploration Agency (JAXA) is continuously conducting research announcement (RA) programs that provide opportunities to use PALSAR-2 and other satellite data to engage and enhance science and application activities worldwide.

This Special Issue solicits original manuscripts on calibration, validation, science, and applications based on PALSAR-2 data. The potential topics of this Special Issue include, but are not limited to, the following:

Calibration related issues and achievements of PALSAR-2
Polarimetry and interferometry related processing and analysis
Scientific and application analysis in various fields
Data fusion technics and new products, e.g., analysis-ready data

We also invite submissions from principal investigators (PIs) of the ALOS-2 RA programs.

Dr. Takeo Tadono
Dr. Masato Ohki
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

ALOS-2 and PALSAR-2
L-band SAR
Calibration
PolSAR, InSAR, PolInSAR, and coherence analysis
Disasters monitoring
Forest, vegetation, and ecosystem
Hydrology
Agriculture
Snow and ice
Ocean applications
Time-series analysis
Data fusion analysis

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Published Papers (12 papers)

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13 pages, 444 KiB

Open AccessCommunication

Calibration and Validation of Polarimetric ALOS2-PALSAR2

by Ridha Touzi, Masanobu Shimada and Takeshi Motohka

Remote Sens. 2022, 14(10), 2452; https://doi.org/10.3390/rs14102452 - 20 May 2022

Cited by 3 | Viewed by 1963

Abstract

PALSAR2 polarimetric distortion matrix is measured using corner reflectors deployed in the Amazonian forest. The Amazonian forest near the geomagnetic equator provides ideal sites for the assessment of L-band PALSAR2 antenna parameters, at free Faraday rotation. Corner reflectors (CRs) deployed at free Faraday rotation provide accurate estimation of antenna cross-talks in contrast to the biased measurements obtained with CRs deployed at significant Faraday rotation. The extended Freeman–Van Zyl calibration method introduced and validated for ALOS-PALSAR calibration is used for the assessment of PALSAR-2 calibration parameters. Six datasets collected over the Amazonian rainforests (with CRs) are used to assess PALSAR-2 distortion matrix for five beams (FP6-3 to FP6-7) with incidence angle varying from 25° to 40°. It is shown that the PALSAR2 antenna is highly isolated with very low cross-talks (lower than −40 dB). Finally, the impact of a significant Faraday rotation on antenna cross-talk measurements using CR is discussed. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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25 pages, 11216 KiB

Open AccessArticle

Performance Study of Landslide Detection Using Multi-Temporal SAR Images

by Yunung Nina Lin, Yi-Ching Chen, Yu-Ting Kuo and Wei-An Chao

Remote Sens. 2022, 14(10), 2444; https://doi.org/10.3390/rs14102444 - 19 May 2022

Cited by 6 | Viewed by 2803

Abstract

This study addresses one of the most commonly-asked questions in synthetic aperture radar (SAR)-based landslide detection: How the choice of datatypes affects the detection performance. In two examples, the 2018 Hokkaido landslides in Japan and the 2017 Putanpunas landslide in Taiwan, we utilize the Growing Split-Based Approach to obtain Bayesian probability maps for such a performance evaluation. Our result shows that the high-resolution, full-polarimetric data offers superior detection capability for landslides in forest areas, followed by single-polarimetric datasets of high spatial resolutions at various radar wavelengths. The medium-resolution single-polarimetric data have comparable performance if the landslide occupies a large area and occurs on bare surfaces, but the detection capability decays significantly for small landslides in forest areas. Our result also indicates that large local incidence angles may not necessarily hinder landslide detection, while areas of small local incidence angles may coincide with layover zones, making the data unusable for detection. The best area under curve value among all datatypes is 0.77, suggesting that the performance of SAR-based landslide detection is limited. The limitation may result from radar wave’s sensitivity to multiple physical factors, including changes in land cover types, local topography, surface roughness and soil moistures. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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19 pages, 13123 KiB

Open AccessArticle

Comparative Study on Potential Landslide Identification with ALOS-2 and Sentinel-1A Data in Heavy Forest Reach, Upstream of the Jinsha River

by Chen Cao, Kuanxing Zhu, Tianhao Song, Ji Bai, Wen Zhang, Jianping Chen and Shengyuan Song

Remote Sens. 2022, 14(9), 1962; https://doi.org/10.3390/rs14091962 - 19 Apr 2022

Cited by 12 | Viewed by 2528

Abstract

Many SAR satellites such as the ALOS-2 satellite and Sentinel-1A satellite can be used in Interferometric Synthetic Aperture Radar (InSAR) to identify landslides. As their wavelengths are different, they can perform differently in the same area. In this study, we selected the alpine canyon heavy forest area of the Baishugong–Shangjiangxiang section of the Jinsha River with a strong uplift of faults and folds as the study area. The Small Baseline Subset (SBAS)–InSAR was used for landslide identification to compare the reliability and applicability of L-band ALOS-2 data and C-band Sentinel-1A data. In total, 13 potential landslides were identified, of which 12 potential landslides were identified by ALOS-2 data, two landslides were identified by Sentinel-1A data, and the Kongzhigong (KZG) landslide was identified by both datasets. Then, the field investigation was used to verify the identification results and analyze the genetic mechanism of four typical landslides. Both the Duila (DL) and KZG landslides are bedding slip, while the Jirenhe (JRH) and Maopo (MP) landslides are creep–pull failure. Then, the difference between ALOS-2 and Sentinel-1A data on KZG landslide was compared. A total of 35,961 deformation points on the KZG landslide were obtained using ALOS-2 data, which are relatively dense. Meanwhile, a total of 7715 deformation points were obtained by Sentinel-1A data, which are relatively scattered and seriously lacking, especially in areas with dense vegetation coverage. Comparing the advantages of ALOS-2 and Sentinel-1A data and the identification results of potential landslides, the reliability and applicability of ALOS-2 data in the identification of potential landslides in areas with dense vegetation cover and complex geological conditions were confirmed from the aspects of vegetation cover, topography, field investigation, and comparative analysis of typical landslides. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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18 pages, 9423 KiB

Open AccessArticle

Interferometric SAR Observation of Permafrost Status in the Northern Qinghai-Tibet Plateau by ALOS, ALOS-2 and Sentinel-1 between 2007 and 2021

by Lichuan Zou, Chao Wang, Yixian Tang, Bo Zhang, Hong Zhang and Longkai Dong

Remote Sens. 2022, 14(8), 1870; https://doi.org/10.3390/rs14081870 - 13 Apr 2022

Cited by 10 | Viewed by 2631

Abstract

With global warming, permafrost is undergoing degradation, which may cause thawing subsidence, collapse, and emission of greenhouse gases preserved in previously frozen permafrost, change the local hydrology and ecology system, and threaten infrastructure and indigenous communities. The Qinghai-Tibet Plateau (QTP) is the world’s largest permafrost region in the middle and low latitudes. Permafrost status monitoring in the QTP is of great significance to global change and local economic development. In this study, we used 66 scenes of ALOS data (2007–2009), 73 scenes of ALOS-2 data (2015–2020) and 284 scenes of Sentinel-1 data (2017–2021) to evaluate the spatial and temporal permafrost deformation over the 83,000 km² in the northern QTP, passing through the Tuotuohe, Beiluhe, Wudaoliang and Xidatan regions. We use the SBAS-InSAR method and present a coherence weighted least squares estimator without any hypothetical model to calculate long-term deformation velocity (LTDV) and maximum seasonal deformation (MSD) without any prior knowledge. Analysis of the ALOS results shows that the LTDV ranged from −20 to +20 mm/year during 2007–2009. For the ALOS-2 and Sentinel-1 results, the LTDV ranged from −30 to 30 mm/year during 2015–2021. Further study shows that the expansion areas of permafrost subsidence are concentrated on braided stream plains and thermokarst lakes. In these areas, due to glacial erosion, surface runoff and river alluvium, the contents of water and ground ice are sufficient, which could accelerate permafrost subsidence. In addition, by analyzing LTDV and MSD for the different periods, we found that the L-band ALOS-2 is more sensitive to the thermal collapse of permafrost than the C-band sensor and the detected collapse areas (LTDV < −10 mm/year) are consistent with the GF-1/2 thermal collapse dataset. This research indicates that the InSAR technique could be crucial for monitoring the evolution of permafrost and freeze-thaw disasters. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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21 pages, 4848 KiB

Open AccessArticle

Estimation of Forest Aboveground Biomass of Two Major Conifers in Ibaraki Prefecture, Japan, from PALSAR-2 and Sentinel-2 Data

by Hantao Li, Tomomichi Kato, Masato Hayashi and Lan Wu

Remote Sens. 2022, 14(3), 468; https://doi.org/10.3390/rs14030468 - 19 Jan 2022

Cited by 21 | Viewed by 4793

Abstract

Forest biomass is a crucial component of the global carbon budget in climate change studies. Therefore, it is essential to develop a credible way to estimate forest biomass as carbon stock. Our study used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which we trained with airborne lidar data. We used the model to estimate forest aboveground biomass (AGB) of two significant coniferous trees, Japanese cedar and Japanese cypress, in Ibaraki Prefecture, Japan. We used 48 variables derived from the two remote sensing datasets to predict forest AGB under the Random Forest algorithm, and found that the model that combined the two datasets performed better than models based on only one dataset, with R² = 0.31, root-mean-square error (RMSE) = 54.38 Mg ha⁻¹, mean absolute error (MAE) = 40.98 Mg ha⁻¹, and relative RMSE (rRMSE) of 0.35 for Japanese cedar, and R² = 0.37, RMSE = 98.63 Mg ha⁻¹, MAE = 76.97 Mg ha⁻¹, and rRMSE of 0.33 for Japanese cypress, over the whole AGB range. In the satellite AGB map, the total AGB of Japanese cedar in 17 targeted cities in Ibaraki Prefecture was 5.27 Pg, with a mean of 146.50 Mg ha⁻¹ and a standard deviation of 44.37 Mg ha⁻¹. The total AGB of Japanese cypress was 3.56 Pg, with a mean of 293.12 Mg ha⁻¹ and a standard deviation of 78.48 Mg ha⁻¹. We also found a strong linear relationship with between the model estimates and Japanese government data, with R² = 0.99 for both species and found the government information underestimates the AGB for cypress but overestimates it for cedar. Our results reveal that combining information from multiple sensors can predict forest AGB with increased accuracy and robustness. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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17 pages, 1786 KiB

Open AccessArticle

Comparison of Field and SAR-Derived Descriptors in the Retrieval of Soil Moisture from Oil Palm Crops Using PALSAR-2

by Veena Shashikant, Abdul Rashid Mohamed Shariff, Aimrun Wayayok, Md Rowshon Kamal, Yang Ping Lee and Wataru Takeuchi

Remote Sens. 2021, 13(23), 4729; https://doi.org/10.3390/rs13234729 - 23 Nov 2021

Cited by 5 | Viewed by 2752

Abstract

Synthetic-aperture radar’s (SAR’s) capacity to resolve the cloud cover concerns encountered while gathering optical data has tremendous potential for soil moisture data retrieval using SAR data. It is possible to use SAR data to recover soil moisture because the backscatter coefficient is sensitive to both soil and vegetation by penetrating through the vegetation layer. This study investigated the feasibility of employing a SAR-derived radar vegetation index (RVI), the ratios of the backscatter coefficients using polarizations of HH/HV (R_HH/HV) and HV/HH (R_HH/HV) to an oil palm crops as vegetation indicators in the water cloud model (WCM) using phased-array L-band SAR-2 (PALSAR-2). These data were compared to the manual leaf area index (LAI) and a physical soil sampling method for computing soil moisture. The field data included the LAI input parameters and, more importantly, physical soil samples from which to calculate the soil moisture. The fieldwork was carried out in Chuping District, Perlis State, Malaysia. Corresponding PALSAR-2 data were collected on three observation dates in 2019: 17 January, 16 April, and 9 July. The results showed that the WCM modeled using the LAI under HV polarization demonstrated promising accuracy, with the root mean square error recorded as 0.033 m³/m³. This was comparable to the RVI and R_HH/HV under HV polarization, which had accuracies of 0.031 and 0.049 m³/m³, respectively. The findings of this study suggest that SAR-based indicators, R_HH/HV and RVI using PALSAR-2, can be used to reduce field-related input in the retrieval of soil moisture data using the WCM for oil palm crop. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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16 pages, 6771 KiB

Open AccessArticle

SAR-Based Flood Monitoring for Flatland with Frequently Fluctuating Water Surfaces: Proposal for the Normalized Backscatter Amplitude Difference Index (NoBADI)

by Hiroto Nagai, Takahiro Abe and Masato Ohki

Remote Sens. 2021, 13(20), 4136; https://doi.org/10.3390/rs13204136 - 15 Oct 2021

Cited by 6 | Viewed by 2960

Abstract

Space-based synthetic aperture radar (SAR) is a powerful tool for monitoring flood conditions over large areas without the influence of clouds and daylight. Permanent water surfaces can be excluded by comparing SAR images with pre-flood images, but fluctuating water surfaces, such as those found in flat wetlands, introduce uncertainty into flood mapping results. In order to reduce this uncertainty, a simple method called Normalized Backscatter Amplitude Difference Index (NoBADI) is proposed in this study. The NoBADI is calculated from a post-flood SAR image of backscatter amplitude and multiple images on non-flooding conditions. Preliminary analysis conducted in the US state of Florida, which was affected by Hurricane Irma in September 2017, shows that surfaces frequently covered by water (more than 20% of available data) have been successfully excluded by means of C-/L-band SAR (HH, HV, VV, and VH polarizations). Although a simple comparison of pre-flood and post-flood images is greatly affected by the spatial distribution of the water surface in the pre-flood image, the NoBADI method reduces the uncertainty of the reference water surface. This advantage will contribute in making quicker decisions during crisis management. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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15 pages, 1870 KiB

Open AccessArticle

Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

by Veena Shashikant, Abdul Rashid Mohamed Shariff, Aimrun Wayayok, Md Rowshon Kamal, Yang Ping Lee and Wataru Takeuchi

Remote Sens. 2021, 13(20), 4023; https://doi.org/10.3390/rs13204023 - 9 Oct 2021

Cited by 8 | Viewed by 2699

Abstract

In oil palm crop, soil fertility is less important than the physical soil characteristics. It is important to have a balance and sufficient soil moisture to sustain high yields in oil palm plantations. However, conventional methods of soil moisture determination are laborious and time-consuming with limited coverage and accuracy. In this research, we evaluated synthetic aperture radar (SAR) and in-situ observations at an oil palm plantation to determine SAR signal sensitivity to oil palm crop by means of water cloud model (WCM) inversion for retrieving soil moisture from L-band HH and HV polarized data. The effects of vegetation on backscattering coefficients were evaluated by comparing Leaf Area Index (LAI), Leaf Water Area Index (LWAI) and Normalized Plant Water Content (NPWC). The results showed that HV polarization effectively simulated backscatter coefficient as compared to HH polarization where the best fit was obtained by taking the LAI as a vegetation descriptor. The HV polarization with the LAI indicator was able to retrieve soil moisture content with an accuracy of at least 80%. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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20 pages, 7723 KiB

Open AccessArticle

Soil Moisture Retrieval over a Vegetation-Covered Area Using ALOS-2 L-Band Synthetic Aperture Radar Data

by Ya Gao, Maofang Gao, Liguo Wang and Offer Rozenstein

Remote Sens. 2021, 13(19), 3894; https://doi.org/10.3390/rs13193894 - 29 Sep 2021

Cited by 7 | Viewed by 3368

Abstract

Soil moisture (SM) plays a significant part in regional hydrological and meteorological systems throughout Earth. It is considered an indispensable state variable in earth science. The high sensitivity of microwave remote sensing to soil moisture, and its ability to function under all weather conditions at all hours of the day, has led to its wide application in SM retrieval. The aim of this study is to evaluate the ability of ALOS-2 data to estimate SM in areas with high vegetation coverage. Through the water cloud model (WCM), the article simulates the scene coupling between active microwave images and optical data. Subsequently, we use a genetic algorithm to optimize back propagation (GA-BP) neural network technology to retrieve SM. The vegetation descriptors of the WCM, derived from optical images, were the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized multi-band drought index (NMDI). In the vegetation-covered area, 240 field soil samples were collected simultaneously with the ALOS-2 SAR overpass. Soil samples at two depths (0–10 cm, 20–30 cm) were collected at each sampling site. The backscattering of the ALOS-2 with the copolarization was found to be more sensitive to SM than the crosspolarization. In addition, the sensitivity of the soil backscattering coefficient to SM at a depth of 0–10 cm was higher than at a depth of 20–30 cm. At a 0–10 cm depth, the best results were the mean square error (MAE) of 2.248 vol%, the root mean square error (RMSE) of 3.146 vol%, and the mean absolute percentage error (MAPE) of 0.056 vol%, when the vegetation is described as by the NDVI. At a 20–30 cm depth, the best results were an MAE of 2.333 vol%, an RMSE of 2.882 vol%, a MAPE of 0.067 vol%, with the NMDI as the vegetation description. The use of the GA-BP NNs method for SM inversion presented in this paper is novel. Moreover, the results revealed that ALOS-2 data is a valuable source for SM estimation, and ALOS-2 L-band data was sensitive to SM even under vegetation cover. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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21 pages, 7510 KiB

Open AccessArticle

Combination of Sentinel-2 and PALSAR-2 for Local Climate Zone Classification: A Case Study of Nanchang, China

by Chaomin Chen, Hasi Bagan, Xuan Xie, Yune La and Yoshiki Yamagata

Remote Sens. 2021, 13(10), 1902; https://doi.org/10.3390/rs13101902 - 13 May 2021

Cited by 18 | Viewed by 3300

Abstract

Local climate zone (LCZ) maps have been used widely to study urban structures and urban heat islands. Because remote sensing data enable automated LCZ mapping on a large scale, there is a need to evaluate how well remote sensing resources can produce fine LCZ maps to assess urban thermal environments. In this study, we combined Sentinel-2 multispectral imagery and dual-polarized (HH + HV) PALSAR-2 data to generate LCZ maps of Nanchang, China using a random forest classifier and a grid-cell-based method. We then used the classifier to evaluate the importance scores of different input features (Sentinel-2 bands, PALSAR-2 channels, and textural features) for the classification model and their contribution to each LCZ class. Finally, we investigated the relationship between LCZs and land surface temperatures (LSTs) derived from summer nighttime ASTER thermal imagery by spatial statistical analysis. The highest classification accuracy was 89.96% when all features were used, which highlighted the potential of Sentinel-2 and dual-polarized PALSAR-2 data. The most important input feature was the short-wave infrared-2 band of Sentinel-2. The spectral reflectance was more important than polarimetric and textural features in LCZ classification. PALSAR-2 data were beneficial for several land cover LCZ types when Sentinel-2 and PALSAR-2 were combined. Summer nighttime LSTs in most LCZs differed significantly from each other. Results also demonstrated that grid-cell processing provided more homogeneous LCZ maps than the usual resampling methods. This study provided a promising reference to further improve LCZ classification and quantitative analysis of local climate. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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23 pages, 11653 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Accuracy of ALOS/PALSAR-2 and Sentinel-1 Radar Images in Estimating the Land Subsidence of Coastal Areas: A Case Study in Alexandria City, Egypt

by Noura Darwish, Mona Kaiser, Magaly Koch and Ahmed Gaber

Remote Sens. 2021, 13(9), 1838; https://doi.org/10.3390/rs13091838 - 9 May 2021

Cited by 18 | Viewed by 5746

Abstract

Recently, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique is widely used for quantifying the land surface deformation, which is very important to assess the potential impact on social and economic activities. Radar satellites operate in different wavelengths and each provides different levels of vertical displacement accuracy. In this study, the accuracies of Sentinel-1 (C-band) and ALOS/PALSAR-2 (L-band) were investigated in terms of estimating the land subsidence rate along the study area of Alexandria City, Egypt. A total of nine Sentinel-1 and 11 ALOS/PALSAR-2 scenes were used for such assessment. The small baseline subset (SBAS) processing scheme, which detects the land deformation with a high spatial and temporal coverage, was performed. The results show that the threshold coherence values of the generated interferograms from ALOS-2 data are highly concentrated between 0.2 and 0.3, while a higher threshold value of 0.4 shows no coherent pixels for about 80% of Alexandria’s urban area. However, the coherence values of Sentinel-1 interferograms ranged between 0.3 and 1, with most of the urban area in Alexandria showing coherent pixels at a 0.4 value. In addition, both data types produced different residual topography values of almost 0 m with a standard deviation of 13.5 m for Sentinel-1 and −20.5 m with a standard deviation of 33.24 m for ALOS-2 using the same digital elevation model (DEM) and wavelet number. Consequently, the final deformation was estimated using high coherent pixels with a threshold of 0.4 for Sentinel-1, which is comparable to a threshold of about 0.8 when using ALOS-2 data. The cumulative vertical displacement along the study area from 2017 to 2020 reached −60 mm with an average of −12.5 mm and mean displacement rate of −1.73 mm/year. Accordingly, the Alexandrian coastal plain and city center are found to be relatively stable, with land subsidence rates ranging from 0 to −5 mm/year. The maximum subsidence rate reached −20 mm/year and was found along the boundary of Mariout Lakes and former Abu Qir Lagoon. Finally, the affected buildings recorded during the field survey were plotted on the final land subsidence maps and show high consistency with the DInSAR results. For future developmental urban plans in Alexandria City, it is recommended to expand towards the western desert fringes instead of the south where the present-day ground lies on top of the former wetland areas. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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14 pages, 497 KiB

Open AccessTechnical Note

L-Band SAR Co-Polarized Phase Difference Modeling for Corn Fields

by Matías Ernesto Barber, David Sebastián Rava and Carlos López-Martínez

Remote Sens. 2021, 13(22), 4593; https://doi.org/10.3390/rs13224593 - 15 Nov 2021

Cited by 4 | Viewed by 2507

Abstract

This research aims at modeling the microwave backscatter of corn fields by coupling an incoherent, interaction-based scattering model with a semi-empirical bulk vegetation dielectric model. The scattering model is fitted to co-polarized phase difference measurements over several corn fields imaged with fully polarimetric synthetic aperture radar (SAR) images with incidence angles ranging from 20° to 60°. The dataset comprised two field campaigns, one over Canada with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR, 1.258 GHz) and the other one over Argentina with Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) (ALOS-2/PALSAR-2, 1.236 GHz), totaling 60 data measurements over 28 grown corn fields at peak biomass with stalk gravimetric moisture larger than 0.8 g/g. Co-polarized phase differences were computed using a maximum likelihood estimation technique from each field’s measured speckled sample histograms. After minimizing the difference between the model and data measurements for varying incidence angles by a nonlinear least-squares fitting, well agreement was found with a root mean squared error of 24.3° for co-polarized phase difference measurements in the range of −170.3° to −19.13°. Model parameterization by stalk gravimetric moisture instead of its complex dielectric constant is also addressed. Further validation was undertaken for the UAVSAR dataset on earlier corn stages, where overall sensitivity to stalk height, stalk gravimetric moisture, and stalk area density agreed with ground data, with the sensitivity to stalk diameter being the weakest. This study provides a new perspective on the use of co-polarized phase differences in retrieving corn stalk features through inverse modeling techniques from space. Full article

(This article belongs to the Special Issue ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications)

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