L-band HH Polarization Research Articles

Comparative flood area analysis of C-band VH, VV, and L-band HH polarizations SAR data

ABSTRACTThe role of synthetic aperture radar (SAR)-image-based flood area mapping is proved beyond the doubts. It is also well known that different wavelength, polarization SAR reacts in varying ways over the same land-use/land-cover region. In line to this, this article mainly brings out the significance of comparing and analysing different wavelength, polarization SAR data of the same inundated region against the land-use classes of the study area. The C-band ENVISAT advanced synthetic aperture radar data of vertically transmitted horizontally received (VH), vertically transmitted vertically received (VV) polarizations data, and L-band ALOS-1 PALSAR data of horizontally transmitted horizontally received (HH) polarization data has been obtained as both these satellites captured the same flood event of Andhra Pradesh state of India. Initially, the SAR images are classified with the help of digital elevation model of the disaster region which supports in mapping the fully submerged, partially submerged and non-flooded pixels of disaster region. The fully submerged regions includes the natural waterbodies, adjacent flood plain regions which are completely submerged, as well as not accessible, whereas the partially submerged regions are spatially discontinuous and scattered regions which are inundated due to recent disaster but accessible. In this study, much emphasis has been given in comparing and analysing the fully submerged, partially submerged, and non-flooded regions of classified SAR images against each land use of the disaster region by which the response of individual land-use units of the disaster region at different wavelength, polarization has been brought out. From this comparative assessment, it has been observed that the areal extent of fully submerged regions is considerably more in L-band HH image than in the C-band polarization images. It is also been noticed that C-band VH polarization image is able to map and quantify considerable part of the land-use classes as partially submerged regions than the L-band HH polarization image. In addition to this, the proposed technique is able to rectify in classifying mangrove regions as non-flooded regions due to the land-use/land-cover-based approach.

Estimation of Corn Growth by Radar Scatterometer Data

Ground-based polarimetric scatterometers have been effective tools to monitor the growth of crop with multi-polarization and frequencies and various incident angles. An important advantage of these systems that can be exploited is temporal observation of a specific crop target. Polarimetric backscatter data at L-, C- and X-bands were acquired every 10 minutes. We analyzed the relationships between L-, C- and X-band signatures, biophysical measurements over the whole corn growth period. The Vertical transmit and Vertical receive polarization (VV) backscattering coefficients for all bands were greater than those of the Horizontal transmit and Horizontal receive polarization (HH) until early-July, and then thereafter HH-polarization was greater than VV-polarization or Horizontal transmit and Vertical receive polarization (HV) until the harvesting stage (Day Of Year, DOY 240). The results of correlation analysis between the backscattering coefficients for all bands and corn growth data showed that L-band HH-polarization (L-HH) was the most suited for monitoring the fresh weight (<TEX>$r=0.95^{***}$</TEX>), dry weight (<TEX>$r=0.95^{***}$</TEX>), leaf area index (<TEX>$r=0.86^{**}$</TEX>), and vegetation water content (<TEX>$r=0.93^{***}$</TEX>). Retrieval equations were developed for estimating corn growth parameters using L-HH. The results indicated that L-HH could be used for estimating the vegetation biophysical parameters considered here with high accuracy. Those results can be useful in determining frequency and polarization of satellite Synthetic Aperture Radar stem and in designing a future ground-based microwave system for a long-term monitoring of corn.

Monitoring soybean growth using L-, C-, and X-band scatterometer data

A ground-based fully polarimetric scatterometer operating at multiple frequencies was used to continuously monitor soybean growth over the course of a growing season. Polarimetric backscatter data at L-, C-, and X-bands were acquired every 10 min. We analysed the relationships between L-, C-, and X-band signatures, and biophysical measurements over the entire soybean growth period. Temporal changes in backscattering coefficients for all bands followed the patterns observed in the soybean growth measurements (leaf area index (LAI) and vegetation water content (VWC)). The difference between the backscattering coefficients for horizontally transmitted horizontally received (HH) and vertically transmitted vertically received (VV) polarizations at the L-band was apparent after the R2 stage (DOY 224) due to the double-bounce scattering effect. Results indicated that L-, C-, and X-band radar backscatter data can be used to detect different soybean growth stages. The results of correlation analyses between the backscattering coefficient for specific bands/polarizations and soybean growth data showed that L-band HH-polarization had the highest correlation with the vegetation parameters LAI (r = 0.98) and VWC (r = 0.97). Prediction equations for estimation of soybean growth parameters from the L-HH were developed. The results indicated that L-HH could be used for estimating the vegetation biophysical parameters considered here with high accuracy. These results provide a basis for developing a method to retrieve crop biophysical properties and guidance on the optimum microwave frequency and polarization necessary to monitor crop conditions. The results are directly applicable to systems such as the proposed NASA Soil Moisture Active Passive (SMAP) satellite.

마이크로파 산란계를 이용한 밀 생육 추정

Microwave remote sensing can help monitor the land surface water cycle and crop growth. This type of remote sensing has great potential over conventional remote sensing using the visible and infrared regions due to its all-weather day-and-night imaging capabilities. In this paper, a ground-based multi-frequency (L-, C-, and X-band) polarimetric scatterometer system capable of making observations every 10 min was developed. This system was used to monitor the wheat over an entire growth cycle. The polarimetric scatterometer components were installed inside an air-conditioned shelter to maintain constant temperature and humidity during the data acquisition period. Backscattering coefficients for the crop growing season were compared with biophysical measurements. Backscattering coefficients for all frequencies and polarizations increased until dat of year 137 and then decreased along with fresh weight, dry weight, plant height, and vegetation water content (VWC). The range of backscatter for X-band was lower than for L- and C-band. We examined the relationship between the backscattering coefficients of each band (frequency/polarization) and the various wheat growth parameters. The correlation between the different vegetation parameters and backscatter decreased with increasing frequency. L-band HH-polarization (L-HH) is best suited for the monitoring of fresh weight (r=0.98), dry weight (r=0.96), VWC (r=0.98), and plant height (r=0.96). The correlation coefficients were highest for L-band observations and lowest for X-band. Also, HH-polarization had the highest correlations among the polarization channels (HH, VV and HV). Based on the correlation analysis between backscattering coefficients in each band and wheat growth parameters, we developed prediction equations using the L-HH based on the observed relationships between L-HH and fresh weight, dry weight, VWC and plant height. The results of these analyses will be useful in determining the optimum microwave frequency and polarizations necessary for estimating vegetation parameters in the wheat.

FOREST RESOURCES STUDY IN MONGOLIA USING ADVANCED SPATIAL TECHNOLOGIES

Abstract. The aim of this study is to conduct a forest resources study using optical and synthetic aperture radar (SAR) satellite images. For this purpose, a forest-dominated site around the Lake Khuvsgul located in northern Mongolia is selected. As remote sensing (RS) data sources, panchromatic and multispectral Landsat 7 images as well as ALOS PALSAR L-band HH polarization data are used. To produce a reliable land cover map from the multisensor images, a novel refined maximum likelihood classification based on the spectral and spatial thresholds are applied and for the accuracy assessment an overall accuracy is used. Overall, the research demonstrates that advanced spatial technologies based on optical and microwave RS are reliable tools for different forest studies.

레이더 산란계 후방산란계수를 이용한 토양수분함량 추정

다편파 레이더 산란계 시스템 (L, C, X-밴드 안테나)에서 얻어진 편파별 후방산란계수와 토양수분함량과의 상관성을 분석하고 후방산란계수를 이용 토양수분함량을 추정하고자 하였다. 콩 생육시기에 따른 밴드별 후방산란계수 변화 관측 결과 L-밴드 후방산란계수가 C-, X-밴드후방산란계수보다 높게 나타났고, 모든 안테나 밴드에서 콩 생육초기에는 VV-편파가 HH, HV-편파보다 후방산란계수가 높게 나타났다. HH-편파가 VV-편파보다 후방산란계수가 높게 나타나는 시기는 밴드에 따라 차이를 보였다. L-밴드의 경우 7월 20일 (DOY 200), C, X-밴드는 7월 30일 (DOY 210)부터 HH-편파가 다른 편파들 보다 후방산란계수가 높게 나타났다. 모든 안테나 편파별 후방산란계수가 9월 29일 (DOY 271)에 최대값을 보였고, 그 이후 수확기 (DOY 294) 까지 감소하였다. L-밴드 HH-편파와 VV-편파 간의 차이는 꼬투리가 생성되는 착협기 (R3, DOY 228) 부터 다른 밴드에 비해 크게 나타났고, 반면에 C-밴드 HH-편파와 VV-편파 간의 차이는 착협성기 (R4, DOY 242) 이후 증가폭이 크게 나타났다. 후방산란계수와 토양수분함량과의 변화를 분석한 결과 생육기간동안 토양수분함량 변이가 컸고, 전체 생육기간에서는 모든 밴드별 후방산란계수와 토양수분함량 간에 상관성이 나타나지 않았다. 하지만 엽면적지수가 2 이하 (R2, DOY 224) 일 때 후방산란계수가 증가함에 따라 토양수분함량도 증가하는 경향을 보였다. 밴드별 후방산란계수와 토양수분함량과의 상관관계를 분석하였다. 전체 생육기간에서는 모든 밴드에서 두 변수간의 상관계수가 낮게 나타났다 (<TEX>$r{\leq}0.50$</TEX>). 반면에 엽면적지수 2 이하 일 때 모든 밴드에서 후방산란계수와 토양수분함량과의 상관계수가 전체 생육단계에서 조사한 것 보다 높게 나타났다. L-밴드 후방산란계수가 C-, X-밴드 후방산란계수 보다 토양수분함량과의 상관성이 높게 나타났고 (<TEX>$r{\geq}0.84$</TEX>), L-밴드 HH-편파가 상관계수가 가장 높았다 (r=0.90). X-밴드 후방산란계수는 L-, C-밴드 후방산란계수보다 상관계수가 낮게 나타났다 (<TEX>$r{\leq}0.71$</TEX>). 후방산란계수를 이용하여 토양수분함량 추정 모형식을 작성하였다. L-밴드 HH-편파 후방산란계수와 토양수분함량과의 관계를 비교해 본 결과 결정계수가 높게 나타났다(<TEX>$R^2=0.92$</TEX>). 본 연구를 통해 레이더 산란계 시스템에서 얻어진 후방산란계수를 이용하여 토양수분함량을 추정할 수 있음을 확인하였다. Microwave remote sensing can help monitor the land surface water cycle, crop growth and soil moisture. A ground-based polarimetric scatterometer has an advantage for continuous crop using multi-polarization and multi-frequencies and various incident angles have been used extensively in a frequency range expanding from L-band to Ka-band. In this study, we analyzed the relationships between L-, C- and X-band signatures and soil moisture content over the whole soybean growth period. Polarimetric backscatter data at L-, C- and X-bands were acquired every 10 minutes. L-band backscattering coefficients were higher than those observed using C- or X-band over the period. Backscattering coefficients for all frequencies and polarizations increased until Day Of Year (DOY) 271 and then decreased until harvesting stage (DOY 294). Time serious of soil moisture content was not a corresponding with backscattering over the whole growth stage, although it increased relatively until early August (R2, DOY 224). We conducted the relationship between the backscattering coefficients of each band and soil moisture content. Backscattering coefficients for all frequencies were not correlated with soil moisture content when considered over the entire stage (<TEX>$r{\leq}0.50$</TEX>). However, we found that L-band HH polarization was correlated with soil moisture content (r=0.90) when Leaf Area Index (LAI)<2. Retrieval equations were developed for estimating soil moisture content using L-band HH polarization. Relation between L-HH and soil moisture shows exponential pattern and highly related with soil moisture content (<TEX>$R^2=0.92$</TEX>). Results from this study show that backscattering coefficients of radar scatterometer appear effective to estimate soil moisture content.

Characterizing L-Band Scattering of Paddy Rice in Southeast China With Radiative Transfer Model and Multitemporal ALOS/PALSAR Imagery

Rice is a major food supply in southeast China. With increased population and urbanization, reliable rice mapping is critical in this region. Because of frequent cloud cover and precipitation during the rice-growing season, it is difficult to conduct large-area rice monitoring with optical remote sensing techniques. L-band synthetic aperture radar (SAR), with its all-weather day and night imaging and canopy penetration capabilities, provides a unique alternative. In this study, a first-order radiative transfer model was developed to simulate L-band scattering properties of paddy rice. Three Advanced Land Observing Satellite (ALOS)/Phased-Array-Type L-band Synthetic Aperture Radar (PALSAR) images in dual-polarization mode (HH and HV) acquired in early tillering (June 28, 2007), tillering (August 13, 2007), and heading (September 28, 2007) stages were processed to test the temporal variation of rice backscatter. It was found that plant height and leaf mass amount were the two major structural parameters that contributed to rice backscatter in PALSAR images. The variation of the simulated HH backscatter matched with PALSAR observations in sample fields, although the simulated backscatter coefficients were around 3 dB lower than image-extracted values. Leaf volume scattering and leaf-ground double bounce were found as the two major scattering components in L-band HH polarization and increased with leaf layer height and density. This paper demonstrated that L-band HH backscatter was more sensitive to rice's structural variation than the VV backscatter and may therefore be more useful in rice mapping and modeling studies.

Vegetation mapping using JERS-1 SAR mosaic for northern Australia

The Japanese Earth Resources Satellite (JERS-1) Synthetic Aperture Radar (SAR) L-band HH polarization data over northern Australia were acquired in 1996 as part of the Global Rainforest Mapping (GRFM) experiment by the National Space Development Agency of Japan (NASDA). The data were mosaiced by NASDA and re-sampled to a resolution of 100 m 2 100 m per pixel. As the mosaiced data is a preliminary product, the problem of calibration, including the removal of effects of look angle variations within each scene and the relative calibration among multiple scenes due to different acquisition dates, has not been dealt with sufficiently, which greatly degrades the quality of the product. It is understood that precise calibration in mosaiced data is of necessity in order to retrieve accurate geophysical parameters. Nevertheless, the preliminary mosaiced data is used in the paper to investigate the potential of using SAR data for vegetation mapping in a regional or global scale. Pixel-based mapping using SAR data has shown limited success due to speckle. However, area analysis techniques allow the computation of reliable statistical and textural measures, thus providing a base for pursuing radar classification. In this paper, an image is first segmented using a Gaussian Markov random field model, so that area analysis can be pursued. An unsupervised classification method is developed in which as many as 12 statistical and textural measures are used to group segments with similar textural measures into the same class in a fashion of maximum likelihood. For a pre-given number of 50 as the final number of classes in the unsupervised classification iteration, the average B- distance among these 50 classes is 1.96 when all 12 textural measures are used to compute the distance.

Combined use of optical and microwave remote sensing data for crop growth monitoring

In this study, optical and microwave remote sensing data were used in combination for crop growth monitoring. A simple reflectance model was used for estimating leaf area index (LAI) from optical data, and a simple backscatter model was used for estimating LAI from radar data. Subsequently, the synergistic effect of using both optical and radar data for estimating LAI was analyzed by studying different data acquisition scenarios. Finally, the remote sensing models were inverted to obtain LAI estimates during the growing season for use in calibrating the crop growth model to actual growing conditions. This concept for crop growth monitoring is elucidated and illustrated with examples using ground-based and airborne data obtained during the MAC Europe 1991 campaign. Results showed that simultaneous optical and radar observations did not improve estimates of LAI over optical data alone. However, for operational applications the assumption of nonsimultaneous observations is more realistic. Results for sugar beet indicated that when periodic (about every ten days) optical recordings are available throughout most of the growing season, LAI can be monitored well and a good estimate of sugar beet yield at the end of the season is possible by using a calibrated crop growth model. When only a few recording dates with an optical sensor are available, radar recordings at L-band HH polarization or C-band W polarization gave a slight improvement of the results of crop monitoring and yield estimation compared with the optical data alone. In the absence of optical remote sensing data, radar data yielded a significant improvement in yield estimation compared with the case of no remotely observed information. This confirmed that the main advantage of radar lies in acquiring information on crop growth when other techniques (in particular optical techniques) fail.