Novel clustering schemes for full and compact polarimetric SAR data: An application for rice phenology characterization

Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier’s parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological stages.This work was supported in part by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI), and the European Funds for Regional Development (EFRD) under Project TEC 2017-85244-C 2-1-P. The work of Dipankar Mandal was supported by the Ministry of Human Resource Development, Government of India (New Delhi, India) towards his Ph.D. assistantship through grant no. RSPHD0210

Application Of Polarimetric SAR For Surface Parameter Inversion And Land Cover Mapping Over Agricultural Areas

In this thesis, novel methodology is developed to extract surface parameters under vegetation cover and to map crop types, from the polarimetric Synthetic Aperture Radar (PolSAR) images over agricultural areas. The extracted surface parameters provide crucial information for monitoring crop growth, nutrient release efficiency, water capacity, and crop production. To estimate surface parameters, it is essential to remove the volume scattering caused by the crop canopy, which makes developing an efficient volume scattering model very critical. In this thesis, a simplified adaptive volume scattering model (SAVSM) is developed to describe the vegetation scattering as crop changes over time through considering the probability density function of the crop orientation. The SAVSM achieved the best performance in fields of wheat, soybean and corn at various growth stages being in convert with the crop phenological development compared with current models that are mostly suitable for forest canopy. To remove the volume scattering component, in this thesis, an adaptive two-component model-based decomposition (ATCD) was developed, in which the surface scattering is a X-Bragg scattering, whereas the volume scattering is the SAVSM. The volumetric soil moisture derived from the ATCD is more consistent with the verifiable ground conditions compared with other model-based decomposition methods with its RMSE improved significantly decreasing from 19 [vol.%] to 7 [vol.%]. However, the estimation by the ATCD is biased when the measured soil moisture is greater than 30 [vol.%]. To overcome this issue, in this thesis, an integrated surface parameter inversion scheme (ISPIS) is proposed, in which a calibrated Integral Equation Model together with the SAVSM is employed. The derived soil moisture and surface roughness are more consistent with verifiable observations with the overall RMSE of 6.12 [vol.%] and 0.48, respectively

Introduction to radar scattering application in remote sensing and diagnostics: Review

The manuscript reviews the current literature on scattering applications of RADAR (Radio Detecting And Ranging) in remote sensing and diagnostics. This paper gives prime features for a variety of RADAR applications ranging from forest and climate monitoring to weather forecast, sea status, planetary information, and mapping of natural disasters such as the ones caused by earthquakes. Both the fundamental parameters involved in scattering mechanisms of RADAR applications and the factors affecting RADAR performances are also discusse

Sentinel-1 interferometric coherence as a vegetation index for agriculture

In this study, the use of Sentinel-1 interferometric coherence data as a tool for crop monitoring has been explored. For this purpose, time series of images acquired by Sentinel-1 and 2 spanning 2017 have been analysed. The study site is an agricultural area in Sevilla, Spain, where 16 different crop species were cultivated during that year. The time series of 6-day repeat-pass coherence measured at each polarimetric channel (VV and VH), as well as their difference, have been compared to the NDVI and to the backscattering ratio (VH/VV) and other indices based on backscatter. The contribution of different decorrelation sources and the effect of the bias from the space-averaged sample coherence magnitude estimation have been evaluated. Likewise, the usage of 12 days as temporal baseline was tested. The study has been carried for three different orbits, characterised by different incidence angles and acquisition times. All results support using coherence as a measure for monitoring the crop growing season, as it shows good correlations with the NDVI (R2>0.7), and its temporal evolution fits well the main phenological stages of the crops. Although each crop shows its own evolution, the performance of coherence as a vegetation index is high for most of them. VV is generally more correlated with the NDVI than VH. For crop types characterised by low plant density, this difference decreases, with VH even showing higher correlation values in some cases. For a few crop types, such as rice, the backscattering ratio outperforms the coherence in following the growth stages of the plants. Since both coherence and backscattering are directly computed from the radar images, they could be used as complementary sources of information for this purpose. Notably, the measured coherence performs well without the need of compensating the thermal noise decorrelation or the bias due to the finite equivalent number of looks.This work was supported in part by the European Space Agency under Project SEOM-S14SCI-Land (SInCohMap), and in part by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development under Project PID2020-117303GB-C22

Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach

It is widely recognized that Synthetic Aperture Radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such its, for example, hydraulic conductivity, values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing anti land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure basest on the Extended Kalman Filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing