Upland peatlands provide vital ecosystem services, especially carbon storage and biodiversity. However, large areas of peatland are heavily degraded in the UK. When peat becomes exposed the potential for it to actively sequester carbon is greatly reduced and carbon stores are rapidly lost through erosion. Peatland restoration is a tool that addresses the government public service agreement targets for biodiversity, and soil and water protection in uplands. Blanket bogs are a UK Biodiversity Action Plan priority habitat. Many areas fall under designations for sites of protection under the EU habitats directive which is aimed at bringing the areas into ‘favourable condition’.The Moors for the Future Partnership is restoring large areas of badly eroded peat in the Peak District National Park to stabilise the surface and re-establish ecosystem functions. Monitoring is of pivotal importance to judge the success of the restoration work. This project assesses the suitability of high resolution remote sensing as an alternative monitoring tool to traditional field based plot surveys which are both time consuming and expensive. Remote sensing has been seen as a potential tool for mapping and monitoring peatlands, but to date the application of high spatial and spectral resolution remote sensing to monitoring peatland restoration has not been fully investigated. A floristic restoration trajectory has been established using a statistical classification (TWINSPAN) of vegetation cover data combined with expert knowledge of previous restoration, and autecology of the moorland species. Hyperspectral classification techniques were applied, including: Spectral Angle Mapping (SAM); Support Vector Machines (SVM); and maximum likelihood classification using both Minimum Noise Fraction (MNF), and narrow band vegetation indices. A successful classification of the restoration succession has been achieved. A predictive model for vegetation cover of plant functional types has been produced using a Partial Least Squares Regression and applied to the whole restoration site at the landscape-scale. RMSEs of between 10 and 16% indicate that the models can be used as a useful operational tool. A spectral library of key moorland species and their phenological response has been established using field spectroscopy in parallel to the image analysis. This has enabled the suggestion that the species are most separable from one another in July and it is recommended that this is the optimal month for remote sensing monitoring. This has facilitated the development of a set of recommendations for the most appropriate vegetation indices to use throughout the year depending species to be differentiated. High spatial and spectral resolution remote sensing data is needed to successfully characterise the vegetation response to restoration management in the upland peatland environment.

In this dissertation, three studies were done for radar and hyperspectral remote sensing applications using statistical techniques. The first study investigated a relationship between synthetic aperture radar backscatter and in situ soil properties for levee monitoring. A series of statistical analyses were performed to investigate potential correlations between three independent polarization channels of radar backscatter and various soil properties. The results showed a weak but considerable correlation between the cross-polarized (HV) radar backscatter coefficients and several soil properties. The second study performed effective statistical feature extraction for levee slide classification. Images about a levee are often very large, and it is difficult to monitor levee conditions quickly because of high computational cost and large memory requirement. Therefore, a time-efficient method to monitor levee conditions is necessary. The traditional support vector machine (SVM) did not work well on original radar images with three bands, requiring extraction of discriminative features. Gray level co-occurrence matrix is a powerful method to extract textural information from grey-scale images, but it may not be practical for a big data in terms of calculation time. In this study, very efficient feature extraction methods with spatial filtering were used, including a weighted average filter and a majority filter in conjunction with a nonlinear band normalization process. Feature extraction with these filters, along with normalized bands, yielded comparable results to gray level co-occurrence matrix with a much lower computational cost. The third study focused on the case when only a small number of ground truth labels were available for hyperspectral image classification. To overcome the difficulty of not having enough training samples, a semisupervised method was proposed. The main idea was to expand ground truth using a relationship between labeled and unlabeled data. A fast self-training algorithm was developed in this study. Reliable unlabeled samples were chosen based on SVM output with majority voting or weighted majority voting, and added to labeled data to build a better SVM classifier. The results showed that majority voting and weighted majority voting could effectively select reliable unlabeled data, and weighted majority voting yielded better performance than majority voting.

This study presents the integration of light detection and range (LiDAR) and hyperspectral remote sensing to create a three-dimensional bird habitat map in the Greenbelt Corridor of the Elm Fork of the Trinity River. This map permits to examine the relationship between forest stand structure, landscape heterogeneity, and bird community composition. A biannual bird census was conducted at this site during the breeding seasons of 2009 and 2010. Census data combined with the three-dimensional map suggest that local breeding bird abundance, community structure, and spatial distribution patterns are highly influenced by vertical heterogeneity of vegetation surface. For local breeding birds, vertical heterogeneity of canopy surface within stands, connectivity to adjacent forest patches, largest forest patch index, and habitat (vegetation) types proved to be the most influential factors to determine bird community assemblages. Results also highlight the critical role of secondary forests to increase functional connectivity of forest patches. Overall, three-dimensional habitat descriptions derived from integrated LiDAR and hyperspectral data serve as a powerful bird conservation tool that shows how the distribution of bird species relates to forest composition and structure at various scales.

Boreal peatlands store carbon sequestered from the atmosphere over millennia and the importance of this and the other ecosystem services these areas provide is now widely recognised. However, a changing climate will affect these environments and, consequently, the services they provide to the global population. The rate and direction of environmental change to peatlands is currently unclear and they have not yet been included in many climate models. This may in part be due to the ecological heterogeneity and spatial extent of these areas and the sparse sampling survey methods currently adopted. Hyperspectral remote sensing from satellite platforms may in future offer an approach to surveying and do so at the high spectral and spatial resolutions necessary to infer ecological change in these peatlands. However, work is required to develop methods of analysis to determine if hyperspectral data can be used to measure the overstorey vegetation of these areas. This will require an understanding of how annual and inter-annual cyclical changes affect the peatland plant canopy reflectances that would be recorded by hyperspectral sensors and how these reflectances can be related to state variable of interest to climate scientists, ecologists and peatland managers. There are significant areas of peatland within Scotland and, as it is towards the southern extreme of the boreal peatlands, these may be an early indicator of environment change to the wider boreal region. Calluna vulgaris, a hardy dwarf shrub, is the dominant overstorey species over much of these peatlands and could serve as a proxy for ecological, and consequently, environmental change. However, little has been done to understand how variations in leaf pigments or canopy structural parameters influence the spectral reflectance of Calluna through annual and inter-annual growth and senescence cycles. Nor has much work been done to develop methods of analysis to enable images acquired by hyperspectral remote sensing to be utilised to monitor change to these Calluna dominated peatlands over time. To advance understanding of the optical properties of Calluna leaves and canopies and develop methods to analyse hyperspectral images laboratory, field and modelling studies have been carried out in time series over a number of years. The leaf and canopy parameters significantly affecting reflectance have been identified and quantified. Differences between published Chlorophyll(a+b) in vivo absorption spectra and those determined were found. Carotenoids and Anthocyanins were also identified and quantified. The absorption spectra of these pigments were incorporated into a canopy reflectance model and this was coupled to a Calluna growth model. This combined model enabled the reflectance of Calluna canopies to be modelled in daily increments through annual and inter-annual growth and senescence cycles. Reasonable results were achieved in spectral regions where reflectance changed systematically but only for homogeneous Calluna stands. However, it was noted during this research that the area of support for the spectral measurements appeared to differ from that assumed from the specification provided by the spectroradiometer manufacturers. The directional response functions (DRFs) of two spectroradiometers were investigated and wavelength, or wavelength region, specific spatial dependences were noted. The effect that the DRFs of the spectroradiometers would have on reflectances recorded from Calluna canopies was investigated through a modelling study. Errors and inaccuracies in the spectra that would be recorded from these canopies, and commonly used biochemical indices derived from them, have been quantified.

Human activities may have, through land use and management changes, an impact on the large amounts of carbon sequestered in soils. Increasingly, inventories of Soil Organic Carbon (SOC) stocks are requested at the regional/national level for countries involved in the Kyoto Protocol and as input in biogeochemical models. However, SOC is characterised by a high spatial variability and a slow temporal dynamic, which reduce our ability to detect and explain SOC stock changes. At the regional scale, local (e.g. land use, topography) and global (e.g. climate) driving factors interact in a complex way and are likely to generate high uncertainties. This thesis represents an effort in assessing and detecting SOC stocks and SOC stock changes at the regional scale. Specifically, our objective was to explore two possible strategies to cope with spatial variability: (i) increase the sampling density using a fast analytical technique (reflectance spectroscopy) and (ii) use information on land use change history to decrease spatial variability in landscape units. In the first part of the thesis, we investigate the capabilities of laboratory, portable and imaging spectroscopy to determine SOC concentrations in croplands. It is shown that laboratory and portable spectroscopy can reach accuracy levels comparable to a standard analytical method (Walkley & Black). Imaging spectroscopy, even with somewhat lower performance, can enhance the estimation of mean SOC stocks by providing a very large sampling density. Outside of the controlled environment of a laboratory, portable and imaging spectroscopy fail to demonstrate reasonable calibration stability over a range of soil types and surface conditions. In the second part of the thesis, we found after re-sampling a soil database collected in 1950-1960 that land use change history has a significant impact on SOC dynamic in forest. Building on this, a bookkeeping model based on historical land use maps (1868-2005) and SOC response curves was developed to assess the temporal evolution SOC stocks. A small carbon sink was observed in the study area due to the reclamation of heathland and the establishment of coniferous plantations.

Made available in DSpace on 2014-06-11T19:22:26Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-05-23Bitstream added on 2014-06-13T18:49:06Z : No. of bitstreams: 1 ennes_r_me_prud.pdf: 1370679 bytes, checksum: eb4f2a4378f82b93f832cc48f75c59ef (MD5) / Os recentes avanços na tecnologia de Sensoriamento Remoto proporcionaram o desenvolvimento de sensores orbitais com altíssima resolução espectral, capazes de fornecer medidas radiométricas em bandas estreitas e contínuas para cada pixel da imagem, definindo curvas espectrais com potencial de discriminar diferentes componentes da matéria. Diante disso, o objetivo geral deste trabalho foi de avaliar a contribuição de imagens hiperespectrais na identificação de componentes opticamente ativos presentes em um corpo d’água, considerado de boa qualidade. Para tanto, uma imagem hiperespectral Hyperion foi adquirida simultaneamente com variáveis limnológicas coletadas em alguns pontos georreferenciados no reservatório de Itupararanga. Após a correção atmosférica da imagem, extraíram curvas espectrais nos locais geográficos dos pontos, nos quais se aplicaram técnicas de análise de espectros, tais como, remoção do contínuo, razão de bandas e análise derivativa. Os dados hiperespectrais originais e os resultantes da aplicação de técnicas foram correlacionados com algumas variáveis limnológicas... / The recent improvements in the technology of Remote Sensing are providing the development of sensors with high spectral resolution that can supply radiometric measurements in narrow and continuous bands for each pixel of the image, defining spectral curves with potential of separating several components of the matter. Due to that, the general objective of this work was evaluating the contribution of hyperspectral images in the identification of optically active constituents present in a body of water, considered good quality. To reach the proposed objective, a hyperspectral imagery of EO-1/Hyperion orbital sensor was acquired simultaneously with limnological variables collected in some points in the body of water. After correcting the atmospheric effects, in the geographical locations of those points, spectral curves of the hyperspectral image were extracted, in which techniques of spectral analysis were applied, such as, continuum removal, derivative analysis and ratio analysis. The hyperspectral original data and the resultants of the application of techniques were correlated with some limnological variables. Of the applied techniques, the derivative analysis provided better differentiation among the optically active constituents... (Complete abstract, click electronic access below)

Submitted by Geyciane Santos (geyciane_thamires@hotmail.com) on 2015-11-12T21:28:54Z No. of bitstreams: 1 Dissertaçaõ- Eliezer Augusto Litaiff De São Paulo Aguiar.pdf: 2274569 bytes, checksum: b7933e0a28ece498ba8623ddcb4e7382 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-11-16T18:59:59Z (GMT) No. of bitstreams: 1 Dissertaçaõ- Eliezer Augusto Litaiff De São Paulo Aguiar.pdf: 2274569 bytes, checksum: b7933e0a28ece498ba8623ddcb4e7382 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2015-11-16T19:11:43Z (GMT) No. of bitstreams: 1 Dissertaçaõ- Eliezer Augusto Litaiff De São Paulo Aguiar.pdf: 2274569 bytes, checksum: b7933e0a28ece498ba8623ddcb4e7382 (MD5) / Made available in DSpace on 2015-11-16T19:11:43Z (GMT). No. of bitstreams: 1 Dissertaçaõ- Eliezer Augusto Litaiff De São Paulo Aguiar.pdf: 2274569 bytes, checksum: b7933e0a28ece498ba8623ddcb4e7382 (MD5) Previous issue date: 2014-04-16 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Hyperspectral remote sensing allows for radiometric measurements of a target in a large number of narrow spectral bands. The data collected by these sensors can be transformed into information about different vegetation covers that are related to biophysical aspects of vegetation. Vegetation being an important element of ecosystems, their studies gain emphasis, especially for seeking knowledge about its variations, distributional patterns, cycles, physiological and morphological changes. With this data was used Hyperion that enables the acquisition of data sufficient to reconstruct absorption bands in the spectra of the pixels associated with the chlorophyll content, water content in leaves and features of lignin and cellulose spectral resolution, which may be important parameters in differentiating vegetation types . On the other hand the poor signal to noise ratio ( SNR) of the sensor, especially in the SWIR, relationship is an obstacle to proper measurement of these features without interference from noise. This research aimed to conduct an analysis of the spectral response of the sensor with the Hyperion plant communities present in the Ducke Reserve (Forest plateau, slope, and lowland campinarana) and quantified by means of unsupervised classification of these communities. Using ENVI Flaash application, atmospheric correction was performed based on the radiative transfer model MODTRAN - 4. Floristic characterization of the study area was raised regarding esttudos related flora of the Reserve. It was possible to perform a summary analysis of the spectral characteristics of the sensor as present an interaction with vegetation between the near infrared (0,7 - 2,5μm) and middle infrared (3 - 6μm), where we can distinguish stage of phenology, canopy structure and water content on leaf responses in four plant communities. / O sensoriamento remoto hiperespectral permite obter medidas radiométricas de um alvo em um grande número de estreitas bandas espectrais. Os dados coletados por estes sensores podem ser transformados em informações sobre diferentes coberturas vegetais que estão relacionadas com aspectos biofísicos da vegetação. Sendo a vegetação um importante elemento dos ecossistemas, seus estudos ganham ênfase, sobretudo, por buscarem conhecimentos acerca de suas variações, padrões distributivos, ciclos, modificações fisiológicas e morfológicas. Com isso foi utilizado dados do sensor Hyperion que possibilita a aquisição de dados com resolução espectral suficiente para reconstruir bandas de absorção nos espectros dos pixels relacionados com o conteúdo de clorofila, teor de água nas folhas e feições de lignina e celulose, as quais podem ser parâmetros importantes na diferenciação de tipologias vegetais. Por outro lado, a pobre relação sinal-ruído (SNR) do sensor, especialmente no SWIR, é um obstáculo para a medição adequada dessas feições sem a interferência de ruídos. Esta pesquisa teve como objetivo realizar uma análise da resposta espectral do sensor Hyperion com as comunidades vegetais presentes na Reserva Ducke (Floresta de platô, declividade, campinarana e baixio) e quantificar por meio de classificação não-supervisionada essas comunidades. Utilizando o aplicativo FLAASH do ENVI, foi realizada a correção atmosférica baseando-se no modelo de transferência radiativa MODTRAN-4. Na caracterização florística da área de estudo foi levantado estudos relacionados referente a flora da Reserva. Foi possível realizar uma análise sucinta das características das respostas espectrais, pois o sensor apresente uma interação com a vegetação entre o Infravermelho próximo (0,7 - 2,5μm) e o Infravermelho médio (3 - 6μm), onde podemos distinguir o estágio de fenologia, estrutura do dossel e quantidade de água na folha nas quatro comunidades vegetais.

Soil visible and near-infrared spectroscopy provides a non-destructive, rapid and low-cost approach to quantify various soil physical and chemical properties based on their reflectance in the spectral range of 400–2500 nm. With an increasing number of large-scale soil spectral libraries established across the world and new space-borne hyperspectral sensors, there is a need to explore methods to extract informative features from reflectance spectra and produce accurate soil spectroscopic models using machine learning. Features generated from regional or large-scale soil spectral data play a key role in the quantitative spectroscopic model for soil properties. The Land Use/Land Cover Area Frame Survey (LUCAS) soil library was used to explore PLS-derived components and fractal features generated from soil spectra in this study. The gradient-boosting method performed well when coupled with extracted features on the estimation of several soil properties. Transfer learning based on convolutional neural networks (CNNs) was proposed to make the model developed from laboratory data transferable for airborne hyperspectral data. The soil clay map was successfully derived using HyMap imagery and the fine-tuned CNN model developed from LUCAS mineral soils, as deep learning has the potential to learn transferable features that generalise from the source domain to target domain. The external environmental factors like the presence of vegetation restrain the application of imaging spectroscopy. The reflectance data can be transformed into a vegetation suppressed domain with a force invariance approach, the performance of which was evaluated in an agricultural area using CASI airborne hyperspectral data. However, the relationship between vegetation and acquired spectra is complicated, and more efforts should put on removing the effects of external factors to make the model transferable from one sensor to another.:Abstract I Kurzfassung III Table of Contents V List of Figures IX List of Tables XIII List of Abbreviations XV 1 Introduction 1 1.1 Motivation 1 1.2 Soil spectra from different platforms 2 1.3 Soil property quantification using spectral data 4 1.4 Feature representation of soil spectra 5 1.5 Objectives 6 1.6 Thesis structure 7 2 Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra 9 2.1 Abstract 10 2.2 Introduction 10 2.3 Materials and methods 13 2.3.1 The LUCAS soil spectral library 13 2.3.2 Partial least squares algorithm 15 2.3.3 Gradient-Boosted Decision Trees 15 2.3.4 Calculation of relative variable importance 16 2.3.5 Assessment 17 2.4 Results 17 2.4.1 Overview of the spectral measurement 17 2.4.2 Results of PLS regression for the estimation of soil properties 19 2.4.3 Results of PLS-GBDT for the estimation of soil properties 21 2.4.4 Relative important variables derived from PLS regression and the gradient-boosting method 24 2.5 Discussion 28 2.5.1 Dimension reduction for high-dimensional soil spectra 28 2.5.2 GBDT for quantitative soil spectroscopic modelling 29 2.6 Conclusions 30 3 Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared Spectroscopy Using Fractal-Based Feature Extraction 31 3.1 Abstract 32 3.2 Introduction 32 3.3 Materials and Methods 35 3.3.1 The LUCAS topsoil dataset 35 3.3.2 Fractal feature extraction method 37 3.3.3 Gradient-boosting regression model 37 3.3.4 Evaluation 41 3.4 Results 42 3.4.1 Fractal features for soil spectroscopy 42 3.4.2 Effects of different step and window size on extracted fractal features 45 3.4.3 Modelling soil properties with fractal features 47 3.4.3 Comparison with PLS regression 49 3.5 Discussion 51 3.5.1 The importance of fractal dimension for soil spectra 51 3.5.2 Modelling soil properties with fractal features 52 3.6 Conclusions 53 4 Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery 55 4.1 Abstract 55 4.2 Introduction 56 4.3 Materials and Methods 59 4.3.1 Datasets 59 4.3.2 Methods 62 4.3.3 Assessment 67 4.4 Results and Discussion 67 4.4.1 Interpretation of mineral and organic soils from LUCAS dataset 67 4.4.2 1D-CNN and spectral index for LUCAS soil clay content estimation 69 4.4.3 Application of transfer learning for soil clay content mapping using the pre-trained 1D-CNN model 72 4.4.4 Comparison between spectral index and transfer learning 74 4.4.5 Large-scale soil spectral library for digital soil mapping at the local scale using hyperspectral imagery 75 4.5 Conclusions 75 5 A Case Study of Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery 77 5.1 Abstract 78 5.2 Introduction 78 5.3 Materials and Methods 81 5.3.1 Study area of Zhangye Oasis 81 5.3.2 Data description 82 5.3.3 Methods 83 5.3.3 Model performance assessment 85 5.4 Results and Discussion 86 5.4.1 The correlation between NDVI and soil salinity 86 5.4.2 Vegetation suppression performance using the Forced Invariance Approach 86 5.4.3 Estimation of soil properties using airborne hyperspectral data 88 5.5 Conclusions 90 6 Conclusions and Outlook 93 Bibliography 97 Acknowledgements 117

Indiana University-Purdue University Indianapolis (IUPUI) / Nuisance blue-green algal blooms contribute to aesthetic degradation of water resources and produce toxins that can have serious adverse human health effects. Current field-based methods for detecting blooms are costly and time consuming, delaying management decisions. Remote sensing techniques which utilize the optical properties of blue-green algal pigments (chlorophyll a and phycocyanin) can provide rapid detection of blue-green algal distribution. Coupled with physical and chemical data from lakes, remote sensing can provide an efficient method for tracking cyanobacteria bloom occurrence and toxin production potential to inform long-term management strategies. In-situ field reflectance spectra were collected at 54 sampling sites on two turbid, productive Indianapolis reservoirs using ASD Fieldspec (UV/VNIR) spectroradiometers. Groundtruth samples were analyzed for in-vitro pigment concentrations and other physical and chemical water quality parameters. Empirical algorithms by Gitelson et al. (1986, 1994), Mittenzwey et al. (1991), Dekker (1993), and Schalles et al. (1998), were applied using a combined dataset divided into a calibration and validation set. Modified semi-empirical algorithms by Simis et al. (2005) were applied to all field spectra to predict phycocyanin concentrations. Algorithm accuracy was tested through a least-squares regression and residual analysis. Results show that for prediction of chlorophyll a concentrations within the range of 18 to 170 ppb, empirical algorithms yielded coefficients of determination as high as 0.71, RMSE 17.59 ppb, for an aggregated dataset (n=54, p<0.0001). The Schalles et al. (2000) empirical algorithm for estimation of phycocyanin concentrations within the range of 2 to 160 ppb resulted in an r2 value of 0.70, RMSE 23.97 ppb (n=48, p<0.0001). The Simis et al. (2005) semi-empirical algorithm for estimation of chlorophyll a and phycocyanin concentrations yielded coefficients of determination of 0.69, RMSE 20.51 ppb (n=54, p<0.0001) and 0.85, RMSE 24.61 pbb (n=49, p<0.0001), respectively. Results suggest the Simis et al. (2005) algorithm is robust, where error is highest in water with phycocyanin concentrations of less than 10 ppb and in water where chlorophyll a dominates (Chl:PC>2). A strong correlation between measured phycocyanin concentrations and blue-green algal biovolume measurements was also observed (r2=0.95, p<0.0001).

L'augmentation importante de la population mondiale, et par conséquent de ses besoins, exerce une pression de plus en plus importante sur les surfaces forestières. L'outil le mieux adapté au suivi des forêts, à l'échelle du globe, est la télédétection. C'est dans ce contexte que se situe ce travail de thèse, qui vise à améliorer l'estimation des paramètres biophysiques des arbres à partir de données de télédétection. L'originalité de ce travail a été d'étudier cette estimation des paramètres biophysiques en menant plusieurs études de sensibilité avec une démarche expérimentale sur des données expérimentales et sur des données simulées. Tout d'abord, l'étude s'est portée sur des séries temporelles de mesures de diffusiométrie radar obtenues sur deux sites : l'un constitué d'un cèdre en zone tempérée et l'autre d'une parcelle de forêt tropicale. Puis, cette étude de sensibilité a été poursuivie en imageant, avec une résolution élevée, plusieurs parcelles aux configurations différentes à l'intérieur d'une forêt de pin. Enfin, des données optiques et radars simulées ont été fusionnés afin d'évaluer l'apport de la fusion de données optique et radar dans l'inversion des paramètres biophysiques. / The significant increase of the world population, and therefore its needs, pushes increasingly high in forest areas. The best tool for monitoring forest across the globe is remote sensing. It is in this context that this thesis, which aims to improve the retrieval of biophysical parameters of trees from remote sensing data, takes place. The originality of this work was to study the estimation of biophysical parameters across multiple sensitivity studies on experimental data and simulated data. First, the study focused on the time series of radar scatterometry measurements obtained on two sites: one characterized by a cedar in the temperate zone and the other by a forest plot of rainforest. Then, the sensitivity analysis was continued by imaging with high resolution, several forest plots with different configurations within a pine forest. Finally, simulated radar and optical data were combined to evaluate the contribution of optical and radar data fusion in the inversion of biophysical parameters.