Preface: the environmental mapping and analysis program (EnMAP) mission: preparing for its scientific exploitation

Open access; distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) licenseThe imaging spectroscopy mission EnMAP aims to assess the state and evolution of terrestrialandaquaticecosystems,examinethemultifacetedimpactsofhumanactivities,andsupport a sustainable use of natural resources. Once in operation (scheduled to launch in 2019), EnMAP will provide high-quality observations in the visible to near-infrared and shortwave-infrared spectral range. The scientific preparation of the mission comprises an extensive science program. This special issue presents a collection of research articles, demonstrating the potential of EnMAP for various applications along with overview articles on the mission and software tools developed within its scientific preparation.Ye

Orbital Navigation Using Resident Space Object Observations

With the population of Resident Space Objects (RSOs) in low earth orbit growing steadily year by year, there is an increasing challenge to track and map this population. While dedicated space and ground-based RSO detectors have done well, there has been an increasing amount of space-based detectors that assist in maintaining the RSO catalog. With continual RSO knowledge improvements, it may be possible to one day use RSO observations as a means of space-based navigation. This paper explores how this RSO information could one day be used in the attitude and orbit determination of the satellite. By leveraging the measurement parallax of nearby RSOs on the star tracker detector, the star tracker can be used to provide both orbit and attitude information to the navigation filter on-board the spacecraft, providing a useful backup to a standard GPS receiver. This paper presents preliminary work on a combined orbit / attitude Kalman filter that includes RSO observations from standard star trackers

Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net

Hyperspectral imaging can help better understand the characteristics of different materials, compared with traditional image systems. However, only high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS) images can generally be captured at video rate in practice. In this paper, we propose a model-based deep learning approach for merging an HrMS and LrHS images to generate a high-resolution hyperspectral (HrHS) image. In specific, we construct a novel MS/HS fusion model which takes the observation models of low-resolution images and the low-rankness knowledge along the spectral mode of HrHS image into consideration. Then we design an iterative algorithm to solve the model by exploiting the proximal gradient method. And then, by unfolding the designed algorithm, we construct a deep network, called MS/HS Fusion Net, with learning the proximal operators and model parameters by convolutional neural networks. Experimental results on simulated and real data substantiate the superiority of our method both visually and quantitatively as compared with state-of-the-art methods along this line of research.Comment: 10 pages, 7 figure

Anomaly detection for replacement model in hyperspectral imaging

In this paper we consider Anomaly Detection in the hyperspectral context, and we extend the popular RX detector, initially designed under the standard additive model, to the replacement model case. Indeed, in this more realistic framework, the target, if present, is supposed to replace a part of the background. We show how to estimate this background power variation to improve the standard RX scheme. The obtained Replacement RX (RRX) is shown to be closed-form and outperforms the standard RX on a real data benchmark experiment

Prediction of topsoil organic carbon using airborne and satellite hyperspectral imagery

The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R2 = 0.60–0.62, while models for the Hyperion sensor showed R2 = 0.49–0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600–700 nm), and the short wave infrared region between ~2000–2250 nm. The use of SMLR and spectral indices based on reference channels at ~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method

Méthodes de séparation aveugle de sources et application à la télédétection spatiale

Cette thèse concerne la séparation aveugle de sources, qui consiste à estimer un ensemble de signaux sources inconnus à partir d'un ensemble de signaux observés qui sont des mélanges à paramètres inconnus de ces signaux sources. C'est dans ce cadre que le travail de recherche de cette thèse concerne le développement et l'utilisation de méthodes linéaires innovantes de séparation de sources pour des applications en imagerie de télédétection spatiale. Des méthodes de séparation de sources sont utilisées pour prétraiter une image multispectrale en vue d'une classification supervisée de ses pixels. Deux nouvelles méthodes hybrides non-supervisées, baptisées 2D-Corr-NLS et 2D-Corr-NMF, sont proposées pour l'extraction de cartes d'abondances à partir d'une image multispectrale contenant des pixels purs. Ces deux méthodes combinent l'analyse en composantes parcimonieuses, le clustering et les méthodes basées sur les contraintes de non-négativité. Une nouvelle méthode non-supervisée, baptisée 2D-VM, est proposée pour l'extraction de spectres à partir d'une image hyperspectrale contenant des pixels purs. Cette méthode est basée sur l'analyse en composantes parcimonieuses. Enfin, une nouvelle méthode est proposée pour l'extraction de spectres à partir d'une image hyperspectrale ne contenant pas de pixels purs, combinée avec une image multispectrale, de très haute résolution spatiale, contenant des pixels purs. Cette méthode est fondée sur la factorisation en matrices non-négatives couplée avec les moindres carrés non-négatifs. Comparées à des méthodes de la littérature, d'excellents résultats sont obtenus par les approches méthodologiques proposées.This thesis concerns the blind source separation problem, which consists in estimating a set of unknown source signals from a set of observed signals which are mixtures of these source signals, with unknown mixing coefficients. In this thesis, we develop and use innovative linear source separation methods for applications in remote sensing imagery. Source separation methods are used and applied in order to preprocess a multispectral image for a supervised classification of this image. Two new unsupervised methods, called 2D-Corr-NLS and 2D-Corr-NMF, are proposed in order to extract abundance maps from a multispectral image with pure pixels. These methods are based on sparse component analysis, clustering and non-negativity constraints. A new unsupervised method, called 2D-VM, is proposed in order to extract endmember spectra from a hyperspectral image with pure pixels. This method is based on sparse component analysis. Also, a new method is proposed for extracting endmember spectra from a hyperspectral image without pure pixels, combined with a very high spatial resolution multispectral image with pure pixels. This method is based on non-negative matrix factorization coupled with non-negative least squares. Compared to literature methods, excellent results are obtained by the proposed methodological approaches

Assessment and mapping of soil water repellency using remote sensing and prediction of its effect on surface runoff and phosphorus losses : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

The soil water repellency spatial and temporal dynamics remain ambiguous. Water repellency is an inherent soil property that refers to the impedance in dry soil wetting. This phenomenon was ascribable to the hydrophobic compounds coating the soil particles and has emerged as a recalcitrant issue impacting multiple processes upon agroecosystems. The apprehensions around soil water repellency include its impact on surface runoff, plant growth, and nutrients losses (e.g. phosphorus). The soil hydrophobic compounds, which are intrinsic constituents of the soil carbon pool, have different sources including plant leaves and roots, soil microbial communities and fungi. Previous methods for water repellency measurements are laborious, time-consuming and costly. The raison d'être of this thesis was to i) explore and test novel approaches for estimation of soil water repellency in pastoral ecosystems, and ii) study the factors controlling soil water repellency and assess its impact on surface runoff volumes and phosphorus losses in surface runoff. In the present work, multiple remote sensing approaches were tested to assess and map soil water repellency at multiple scales. The liaison between water repellency and soil surface reflectance was exploited to access the water repellency using the satellite multispectral reflectance and hyperspectral satellite data. A novel approach implicating the use of time series of surface reflectance and water deficit data was used to study the impact of both surface biomass and soil moisture temporal dynamics on the occurrence of water repellency and carbon content in pastoral systems. Multispectral broadband data from both Landsat-7 and Sentinel-2 satellites showed big potential for assessing soil water repellency and carbon content in permanent pastures. Partial least square regression models were calibrated and cross-validated using topsoil measurement of water repellency and soil carbon from 41 and 35 pastoral sites that were matched with reflectance spectra from Landsat-7 and Sentinel-2, respectively. Soil carbon showed higher predictability compared to water repellency with R2v=0.50, RMSEv=2.58 when using Landsat-7 spectra. The higher predictability performance for water repellency persistence was reached using Sentinel-2 spectral (R2v=0.45; RMSEv=0.98). However, using hyperspectral narrowband data from the Hyperion satellite showed a higher prediction accuracy (R2v=0.78; RMSEv=0.58). Prediction performance was generally higher when using the calibration sets, indicating the possibility of improving these prediction models when using larger datasets. A novel approach was tested using multiple predictors for soil water repellency occurrence. The predictors included time series of surface biomass assessed through normalised difference vegetation index (NDVI) and soil moisture data estimated through water deficit and synthetic aperture radar satellite data. The results showed an attractive opportunity for water repellency and soil carbon mapping. Three machine learning algorithms including artificial neural networks, random forest, and support vector machine were trained and cross-validated using multiple configurations of satellite time-series data and topsoil measurement from 58 pastoral sites. Random forest and support vector machine (RMSEv=0.82 and 0.87, respectively) outperformed artificial neural networks (RMSEv=1.23). With increasingly available remote sensing data, the use of satellite time-series data will open unprecedented opportunities for soil carbon, water repellency mapping, and potentially other functional chemical and physical soil attributes. To understand water repellency dynamics and evaluate their impact on surface runoff and phosphorus losses in pastoral soils, two experiments were conducted. The first experiment aimed to understand the relationship between the actual water repellency persistence and water content in drying hydrophobic soils. The second experiment had the objective to evaluate the impact of soil water repellency on the surface runoff and phosphorus losses in runoff. Results from the first experiment showed that the actual water repellency increased dramatically when water content decreased, especially when moisture dropped below a critical value. Using lab measurements, the actual water repellency was modelled using a simple sigmoidal model, as a function of water content, the potential water repellency, and two characteristic parameters related to the response curve shape. Results from the runoff trial showed that the surface runoff was influenced by soil water repellency to some extent (R2=0.46). Although more than 90 % of phosphorus losses happened in incidental losses following fertiliser application, the data point to non-incidental phosphorus loads being related to soil water repellency (R2=0.56). These results bespoke the effect of soil water repellency on background phosphorus losses through surface runoff during post-summer runoff events in pastoral ecosystems

Multiscale Imaging of Evapotranspiration

Evapotranspiration (ET; evaporation + transpiration) is central to a wide range of biological, chemical, and physical processes in the Earth system. Accurate remote sensing of ET is challenging due to the interrelated and generally scale dependent nature of the physical factors which contribute to the process. The evaporation of water from porous media like sands and soils is an important subset of the complete ET problem. Chapter 1 presents a laboratory investigation into this question, examining the effects of grain size and composition on the evolution of drying sands. The effects of composition are found to be 2-5x greater than the effects of grain size, indicating that differences in heating caused by differences in reflectance may dominate hydrologic differences caused by grain size variation. In order to relate the results of Chapter 1 to the satellite image archive, however, the question of information loss between hyperspectral (measurements at 100s of wavelength intervals) laboratory measurements and multispectral (≤ 12 wavelength intervals) satellite images must be addressed. Chapter 2 focuses on this question as applied to substrate materials such as sediment, soil, rock, and non-photosynthetic vegetation. The results indicate that the continuum that is resolved by multispectral sensors is sufficient to resolve the gradient between sand-rich and clay-rich soils, and that this gradient is also a dominant feature in hyperspectral mixing spaces where the actual absorptions can be resolved. Multispectral measurements can be converted to biogeophysically relevant quantities using spectral mixture analysis (SMA). However, retrospective multitemporal analysis first requires cross-sensor calibration of the mixture model. Chapter 3 presents this calibration, allowing multispectral image data to be used interchangeably throughout the Landsat 4-8 archive. In addition, a theoretical explanation is advanced for the observed superior scaling properties of SMA-derived fraction images over spectral indices. The physical quantities estimated by the spectral mixture model are then compared to simultaneously imaged surface temperature, as well as to the derived parameters of ET Fraction and Moisture Availability. SMA-derived vegetation abundance is found to produce substantially more informative ET maps, and SMA-derived substrate fraction is found to yield a surprisingly strong linear relationship with surface temperature. These results provide context for agricultural applications. Chapter 5 investigates the question of mapping and monitoring rice agricultural using optical and thermal satellite image time series. Thermal image time series are found to produce more accurate maps of rice presence/absence, but optical image time series are found to produce more accurate maps of rice crop timing. Chapter 6 takes a more global approach, investigating the spatial structure of agricultural networks for a diverse set of landscapes. Surprisingly consistent scaling relations are found. These relations are assessed in the context of a network-based approach to land cover analysis, with potential implications for the scale dependence of ET estimates. In sum, this thesis present a novel approach to improving ET estimation based on a synthesis of complementary laboratory measurements, satellite image analysis, and field observations. Alone, each of these independent sources of information provides novel insights. Viewed together, these insights form the basis of a more accurate and complete geophysical understanding of the ET phenomenon