scholarly journals Preliminary Study on the Radar Vegetation Index (RVI) Application to Actual Paddy Fields by ALOS/PALSAR Full-polarimetry SAR Data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 129-131 ◽  
Author(s):  
Y. Yamada
Keyword(s):  
Vegetation Index ◽  
Wave Length ◽  
Paddy Fields ◽  
Bragg Scattering ◽  
Growth Data ◽  
Alos Palsar ◽  
Rice Plants ◽  
L Band ◽  
Sar Data ◽  
Vegetation Water

Kim and van Zyl (2001) proposed a kind of radar vegetation index (RVI). RVI = 4*min(λ1, λ2, λ3) / (λ1 + λ2 + λ3) They modified the equation as follows. (2009) RVI = 8 * σ<sup>0</sup>hv / (σ<sup>0</sup>hh + σ<sup>0</sup>vv +σ<sup>0</sup>hv ) by L-band full-polarimetric SAR data. They applied it into rice crop and soybean. (Y.Kim, T.Jackson et al., 2012) They compared RVI for L-, C- and X-bands to crop growth data, LAI and NDVI. They found L-band RVI was well correlated with Vegetation Water Content, LAI and NDVI. But the field data were collected by the multifrequency polarimetric scatterometer. The platform height was 4.16 meters from the ground. The author tried to apply the method to actual paddy fields near Tsukuba science city in Japan using ALOS/PALSAR, full-polarimetry L-band SAR data. The staple crop in Eastern Asia is rice and paddy fields are dominant land use. A rice-planting machine comes into wide use in this areas. The young rice plants were bedded regularly ridged line in the paddy fields by the machine. The space between two ridges of rice plants is about 30 cm and the wave length of PALSAR sensor is about 23 cm. Hence the Bragg scattering will appear depending upon the direction of the ridges of paddy fields. Once the Bragg scattering occurs, the backscattering values from the pixels should be very high comparing the surrounding region. Therefore the radar vegetation index (RVI) would be saturated. The RVI did not follow the increasing of vegetation anymore. Japan has launched ALOS-2 satellite and it has PALSAR-2, L-band SAR. Therefore RVI application product by PALSAR-2 will be watched with deep interest.

scholarly journals CROP SPECIES RECOGNITION AND DISCRIMINATION PADDY-RICE-GROWINGFIELDS FROM REAPED-FIELDS BY THE RADAR VEGETATION INDEX (RVI) OF ALOS-2/PALSAR2

2016 ◽  
Vol XLI-B8 ◽  
pp. 1083-1087
Author(s):  
Y. Yamada
Keyword(s):  
Vegetation Index ◽  
Species Recognition ◽  
Wave Length ◽  
Radar Data ◽  
Paddy Rice ◽  
Summer Season ◽  
Crop Species ◽  
Main Island ◽  
Main Crop ◽  
L Band

The Japanese ALOS-2 satellite was launched on May 24&lt;sup&gt;th&lt;/sup&gt;, 2014. It has the L-band SAR, PALSAR-2. Kim,Y. and van Zyl, J.J. proposed a kind of Radar Vegetation Index (RVI) as RVI = 8 * σ&lt;sup&gt;0&lt;/sup&gt;&lt;sub&gt;hv&lt;/sub&gt; / (σ&lt;sup&gt;0&lt;/sup&gt;&lt;sub&gt;hh&lt;/sub&gt; + σ&lt;sup&gt;0&lt;/sup&gt;&lt;sub&gt;vv&lt;/sub&gt; + 2* σ&lt;sup&gt;0&lt;/sup&gt;&lt;sub&gt;hv&lt;/sub&gt;) by L-band full-polarimetric radar data. Kim, Y. and Jackson, T.J., et al. applied the equation into rice and soybean by multi-frequency polarimetric scatterometer above 4.16 meters from the ground. Their report showed the L-band was the most promising wave length for estimating LAI and NDVI from RVI. The author tried to apply the analysis to the actual paddy field areas, both Inashiki region and Miyagi region in the eastern main island, “Honshu”, areas of Japan by ALOS-2/PALSAR-2 full-polarimetry data in the summer season, the main crop growing time, of 2015. Judging from conventional methods, it will be possible to discriminate paddy rice growing fields from reaped fields or the other crops growing fields by the PALSAR-2 data. But the RVI value is vaguely related to such land use or biomass at the present preliminary experiment. The continuous research by the additional PALSAR-2 full-polarimetry data should be desired.

scholarly journals CROP SPECIES RECOGNITION AND DISCRIMINATION PADDY-RICE-GROWINGFIELDS FROM REAPED-FIELDS BY THE RADAR VEGETATION INDEX (RVI) OF ALOS-2/PALSAR2

2016 ◽  
Vol XLI-B8 ◽  
pp. 1083-1087
Author(s):  
Y. Yamada
Keyword(s):  
Vegetation Index ◽  
Species Recognition ◽  
Wave Length ◽  
Radar Data ◽  
Paddy Rice ◽  
Summer Season ◽  
Crop Species ◽  
Main Island ◽  
Main Crop ◽  
L Band

The Japanese ALOS-2 satellite was launched on May 24<sup>th</sup>, 2014. It has the L-band SAR, PALSAR-2. Kim,Y. and van Zyl, J.J. proposed a kind of Radar Vegetation Index (RVI) as RVI = 8 * σ<sup>0</sup><sub>hv</sub> / (σ<sup>0</sup><sub>hh</sub> + σ<sup>0</sup><sub>vv</sub> + 2* σ<sup>0</sup><sub>hv</sub>) by L-band full-polarimetric radar data. Kim, Y. and Jackson, T.J., et al. applied the equation into rice and soybean by multi-frequency polarimetric scatterometer above 4.16 meters from the ground. Their report showed the L-band was the most promising wave length for estimating LAI and NDVI from RVI. The author tried to apply the analysis to the actual paddy field areas, both Inashiki region and Miyagi region in the eastern main island, “Honshu”, areas of Japan by ALOS-2/PALSAR-2 full-polarimetry data in the summer season, the main crop growing time, of 2015. Judging from conventional methods, it will be possible to discriminate paddy rice growing fields from reaped fields or the other crops growing fields by the PALSAR-2 data. But the RVI value is vaguely related to such land use or biomass at the present preliminary experiment. The continuous research by the additional PALSAR-2 full-polarimetry data should be desired.

scholarly journals Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2102
Author(s):  
Mohamad Hamze ◽  
Nicolas Baghdadi ◽  
Marcel M. El Hajj ◽  
Mehrez Zribi ◽  
Hassan Bazzi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Bare Soil ◽  
Data Availability ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Alos Palsar ◽  
Times Series ◽  
L Band ◽  
Sar Data ◽  
Error Bias

Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms <1.5 cm and most SSM values higher than 10 vol.%, the use of Hrms as an input in the NNs decreases the underestimation of the SSM (bias ranges from −4.5 to 0 vol.%) and provides a more accurate estimation of the SSM with a decrease in the RMSE by approximately 2 vol.%. Moreover, for Hrms values between 1.5 and 2.0 cm, the overestimation of SSM slightly decreases (bias decreased by around 1.0 vol.%) without a significant improvement of the RMSE. In addition, for Hrms >2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively.

scholarly journals Microwave Vegetation Index from Multi-Angular Observations and Its Application in Vegetation Properties Retrieval: Theoretical Modelling

2019 ◽  
Vol 11 (6) ◽  
pp. 730 ◽  
Author(s):  
Somayeh Talebiesfandarani ◽  
Tianjie Zhao ◽  
Jiancheng Shi ◽  
Paolo Ferrazzoli ◽  
Jean-Pierre Wigneron ◽  
...  
Keyword(s):  
Water Content ◽  
Vegetation Index ◽  
Soil Layer ◽  
Global Scale ◽  
Optical Observations ◽  
Theoretical Modelling ◽  
Vegetation Monitoring ◽  
L Band ◽  
Vegetation Water ◽  
Global Vegetation

Monitoring global vegetation dynamics is of great importance for many environmental applications. The vegetation optical depth (VOD), derived from passive microwave observation, is sensitive to the water content in all aboveground vegetation and could serve as complementary information to optical observations for global vegetation monitoring. The microwave vegetation index (MVI), which is originally derived from the zero-order model, is a potential approach to derive VOD and vegetation water content (VWC), however, it has limited application at dense vegetation in the global scale. In this study, we preferred to use a more complex vegetation model, the Tor Vergata model, which takes into account multi-scattering effects inside the vegetation and between the vegetation and soil layer. Validation with ground-based measurements proved this model is an efficient tool to describe the microwave emissions of corn and wheat. The MVI has been derived through two methods: (i) polarization independent ( MVI B P ) and (ii) time invariant ( MVI B T ), based on model simulations at the L band. Results show that the MVI B T has a stronger sensitivity to vegetation properties compared with MVI B P . MVI B T is used to retrieve VOD and VWC, and the results were compared to physical VOD and measured VWC. Comparisons indicated that MVI B T has a great potential to retrieve VOD and VWC. By using L band time-series information, the performance of MVIs could be enhanced and its application in a global scale could be improved while paying attention to vegetation structure and saturation effects.

scholarly journals Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands

2018 ◽  
Vol 10 (9) ◽  
pp. 1355 ◽  
Author(s):  
Luciana Pereira ◽  
Luiz Furtado ◽  
Evlyn Novo ◽  
Sidnei Sant’Anna ◽  
Veraldo Liesenberg ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Floodplain Forest ◽  
Single Frequency ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Area Index ◽  
Ground Biomass ◽  
L Band ◽  
Sar Data

The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha−1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.

Estimation of Vegetation Water Content From the Radar Vegetation Index at L-Band

2016 ◽  
Vol 54 (2) ◽  
pp. 981-989 ◽  
Author(s):  
Yuancheng Huang ◽  
Jeffrey P. Walker ◽  
Ying Gao ◽  
Xiaoling Wu ◽  
Alessandra Monerris
Keyword(s):  
Water Content ◽  
Vegetation Index ◽  
Vegetation Water Content ◽  
L Band ◽  
Vegetation Water

scholarly journals Multi-Temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1756 ◽  
Author(s):  
Xiaojie Liu ◽  
Chaoying Zhao ◽  
Qin Zhang ◽  
Jianbing Peng ◽  
Wu Zhu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Landslide Inventory ◽  
Synthetic Aperture ◽  
Small Scale ◽  
Alos Palsar ◽  
Multi Temporal ◽  
Loess Landslides ◽  
L Band ◽  
Sar Data

The Interferometric Synthetic Aperture Radar (InSAR) technique is a well-developed remote sensing tool which has been widely used in the investigation of landslides. Average deformation rates are calculated by weighted averaging (stacking) of the interferograms to detect small-scale loess landslides. Heifangtai loess terrace, Gansu province China, is taken as a test area. Aiming to generate multi-temporal landslide inventory maps and to analyze the landslide evolution features from December 2006 to November 2017, a large number of Synthetic Aperture Radar (SAR) datasets acquired by L-band ascending ALOS/PALSAR, L-band ascending and descending ALOS/PALSAR-2, X-band ascending and descending TerraSAR-X and C-band descending Sentinel-1A/B images covering different evolution stages of Heifangtai terrace are fully exploited. Firstly, the surface deformation of Heifangtai terrace is calculated for independent SAR data using the InSAR technique. Subsequently, InSAR-derived deformation maps, SAR intensity images and a DEM gradient map are jointly used to detect potential loess landslides by setting the appropriate thresholds. More than 40 active loess landslides are identified and mapped. The accuracy of the landslide identification results is verified by comparison with published literatures, the results of geological field surveys and remote sensing images. Furthermore, the spatiotemporal evolution characteristics of the landslides during the last 11 years are revealed for the first time. Finally, strengths and limitations of different wavelength SAR data, and the effects of track direction, geometric distortions of SAR images and the differences in local incidence angle between two adjacent satellite tracks in terms of small-scale loess landslides identification, are analyzed and summarized, and some suggestions are given to guide the future identification of small-scale loess landslides with the InSAR technique.

Sign in / Sign up

Export Citation Format

Share Document