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

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale

Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques

Surface soil moisture (SM) retrieval over agricultural areas from polarimetric synthetic aperture radar (PolSAR) has long been restricted by vegetation attenuation, simplified polarimetric scattering modelling, and limited SAR measurements. This study proposes a modified polarimetric decomposition framework to retrieve SM from multi-incidence and multitemporal PolSAR observations. The framework is constructed by combining the X-Bragg model, the extended double Fresnel scattering model and the generalised volume scattering model (GVSM). Compared with traditional decomposition models, the proposed framework considers the depolarisation of dihedral scattering and the diverse vegetation contribution. Under the assumption that SM is invariant for the PolSAR observations at two different incidence angles and that vegetation scattering does not change between two consecutive measurements, analytical parameter solutions, including the dielectric constant of soil and crop stem, can be obtained by solving multivariable nonlinear equations. The proposed framework is applied to the time series of L-band uninhabited aerial vehicle synthetic aperture radar data acquired during the Soil Moisture Active Passive Validation Experiment in 2012. In this study, we assess retrieval performance by comparing the inversion results with in-situ measurements over bean, canola, corn, soybean, wheat and winter wheat areas and comparing the different performance of SM retrieval between the GVSM and Yamaguchi volume scattering models. Given that SM estimation is inherently influenced by crop phenology and empirical parameters which are introduced in the scattering models, we also investigate the influence of surface depolarisation angle and co-pol phase difference on SM estimation. Results show that the proposed retrieval framework provides an inversion accuracy of RMSE<6.0% and a correlation of R≥0.6 with an inversion rate larger than 90%. Over wheat and winter wheat fields, a correlation of 0.8 between SM estimates and measurements is observed when the surface scattering is dominant. Specifically, stem permittivity, which is retrieved synchronously with SM also shows a linear relationship with crop biomass and plant water content over bean, corn, soybean and wheat fields. We also find that a priori knowledge of surface depolarisation angle, co-pol phase difference and adaptive volume scattering could help to improve the performance of the proposed SM retrieval framework. However, the GVSM model is still not fully adaptive because the co-pol power ratio of volume scattering is potentially influenced by ground scattering.This work was supported by the National Natural Science Foundation of China [grant numbers 61971318, 41771377, 41901286, 42071295, 41901284, U2033216]; the China Postdoctoral Science Foundation [grant number 2018M642914]. This work was supported in part by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI), and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

Recent Advancement of Synthetic Aperture Radar (SAR) Systems and Their Applications to Crop Growth Monitoring

Synthetic aperture radars (SARs) propagate and measure the scattering of energy at microwave frequencies. These wavelengths are sensitive to the dielectric properties and structural characteristics of targets, and less affected by weather conditions than sensors that operate in optical wavelengths. Given these advantages, SARs are appealing for use in operational crop growth monitoring. Engineering advancements in SAR technologies, new processing algorithms, and the availability of open-access SAR data, have led to the recent acceleration in the uptake of this technology to map and monitor Earth systems. The exploitation of SAR is now demonstrated in a wide range of operational land applications, including the mapping and monitoring of agricultural ecosystems. This chapter provides an overview of—(1) recent advancements in SAR systems; (2) a summary of SAR information sources, followed by the applications in crop monitoring including crop classification, crop parameter estimation, and change detection; and (3) summary and perspectives for future application development

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Soil Moisture Retrieval from Microwave Remote Sensing Observations

This chapter mainly describes the vegetated soil moisture retrieval approaches based on microwave remote sensing data. It will be comprised of three topics: (1) SAR polarimetric decomposition is to model the full coherency matrix as a summation of the surface, dihedral, and volume scattering mechanisms. After removing the volume scattering component, the soil moisture is estimated from the surface and dihedral scattering components. Particularly, various dynamic volume scattering models will be critically reviewed, allowing the readers to select the appropriate one to capture the complex variations of the volume scattering mechanism with crop phenological growth. (2) Radiative transfer model is to express the radar backscattering coefficient as the incoherent summation of different scattering components. Hereby, we will review the water cloud model and its several extensions for enhanced soil moisture retrieval. (3) Compared to the active radar, the passive radiometer possesses high temporal resolution but coarse spatial resolution. The third topic is dedicated to review the microwave emission models and the active-passive combined approaches, in the context of Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) missions

Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

Methoden und Techniken der Fernerkundung fungieren als wichtige Hilfsmittel im regionalen Umweltmanagement. Um diese zu optimieren, untersucht die folgende Arbeit sowohl die Verwendung als auch Synergien verschiedener Sensoren aus unterschiedlichen Wellenlängenbereichen. Der Fokus liegt auf der Modellentwicklung zur Ableitung von Pflanzenparametern aus fernerkundlichen Bestandsmessungen sowie auf deren Bewertung. Zu den verwendeten komplementären Fernerkundungssystemen zählen die Sensoren EO-1 Hyperion und ALI, Envisat ASAR sowie TerraSAR-X. Für die optischen Hyper- und Multispektralsysteme werden die Reflexion verschiedener Spektralbereiche sowie die Performanz der daraus abgeleiteten Vegetationsindizes untersucht und bewertet. Im Hinblick auf die verwendeten Radarsysteme konzentriert sich die Untersuchung auf Parameter wie Wellenlänge, Einfallswinkel, Radarrückstreuung und Polarisation. Die Eigenschaften verschiedener Parameterkombinationen werden hierbei dargestellt und der komplementäre Beitrag der Radarfernerkundung zur Wachstumsüberwachung bewertet. Hierzu wurden zwei Testgebiete, eines für Winterweizen in der Nordchinesischen Tiefebene und eines für Reis im Nordosten Chinas ausgewählt. In beiden Gebieten wurden während der Wachstumsperioden umfangreiche Feldmessungen von Bestandsparametern während der Satellitenüberflüge oder zeitnah dazu durchgeführt. Mit Hilfe von linearen Regressionsmodellen zwischen Satellitendaten und Biomasse wird die Sensitivität hyperspektraler Reflexion und Radarrückstreuung im Hinblick auf das Wachstum des Winterweizens untersucht. Für die optischen Daten werden drei verschiedene Modelvarianten untersucht: traditionelle Vegetationsindices berechnet aus Multispektraldaten, traditionelle Vegetationsindices berechnet aus Hyperspektraldaten sowie die Berechnung von Normalised Ratio Indices (NRI) basierend auf allen möglichen 2-Band Kombinationen im Spektralbereich zwischen 400 und 2500 nm. Weiterhin wird die gemessene Biomasse mit der gleichpolarisierten (VV) C-Band Rückstreuung des Envisat ASAR Sensors linear in Beziehung gesetzt. Um den komplementären Informationsgehalt von Hyperspektral und Radardaten zu nutzen, werden optische und Radardaten für die Parameterableitung kombiniert eingesetzt. Das Hauptziel für das Reisanbaugebiet im Nordosten Chinas ist das Verständnis über die kohärente Dualpolarimetrische X-Band Rückstreuung zu verschiedenen phänologischen Wachstumsstadien. Hierfür werden die gleichpolarisierte TerraSAR-X Rückstreuung (HH und VV) sowie abgeleitete polarimetrische Parameter untersucht und mit verschiedenen Ebenen im Bestand in Beziehung gesetzt. Weiterhin wird der Einfluss der Variation von Einfallswinkel und Auflösung auf die Bestandsparameterableitung quantifiziert. Neben der Signatur von HH und VV ermöglichen vor allem die polarimetrischen Parameter Phasendifferenz, Ratio, Koherenz und Entropy-Alpha die Bestimmung bestimmter Wachstumsstadien. Die Ergebnisse der Arbeit zeigen, dass die komplementären Fernerkundungssysteme Optik und Radar die Ableitung von Pflanzenparametern und die Bestimmung von Heterogenitäten in den Beständen ermöglichen. Die Synergien diesbezüglich müssen auch in Zukunft weiter untersucht werden, da neue und immer variablere Fernerkundungssysteme zur Verfügung stehen werden und das Umweltmanagement weiter verbessern können

Polarimetric Synthetic Aperture Radar, Principles and Application

Demonstrates the benefits of the usage of fully polarimetric synthetic aperture radar data in applications of Earth remote sensing, with educational and development purposes. Includes numerous up-to-date examples with real data from spaceborne platforms and possibility to use a software to support lecture practicals. Reviews theoretical principles in an intuitive way for each application topic. Covers in depth five application domains (forests, agriculture, cryosphere, urban, and oceans), with reference also to hazard monitorin