Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data

A general framework for the integration of multi-sensor data for dry and fresh biomass retrieval is proposed and tested in Alpine meadows and pastures. To this purpose, hyperspectral spectroradiometer (as simulation of hyperspectral imagery) and biomass samples were collected in field campaigns and Copernicus Sentinel-1 Interferometric Wide (IW) swath SAR backscattering coefficients were used. First, a genetic algorithm feature selection was performed on hyperspectral data, and afterwards the resulting most sensitive bands where combined with SAR data within a support vector regression (SVR) model. The most sensitive hyperspectral bands were mainly located in different regions of the SWIR range for both fresh and dry biomass, and in the red and near-infrared regions mainly for dry biomass, but with less influence for fresh biomass. The R (2) correlation values between the sampled and the estimated biomass range from 0.24 to 0.71. The relatively low performances are mainly related to the saturation effect in the optical bands, as well as to the paucity of points for high values of biomass. The methodology allows a better understanding of the interaction between grassland systems and the electromagnetic spectrum by offering a model with a reduced number of narrow bands in the context of a multi-sensor integration

Amélioration de la caractérisation de la neige et du sol arctique afin d’améliorer la prédiction de l’équivalent en eau de la neige en télédétection micro-ondes

Le phénomène de l’amplification arctique consiste en une augmentation plus prononcée des températures de surface dans cette région que sur le reste du globe. Ce phénomène est notamment dû à la diminution marquée du couvert nival provoquant un déséquilibre dans le bilan d’énergie de surface via une réduction généralisée de l’albédo (rétroaction positive). L’accélération du réchauffement est jusqu’à trois fois plus élevée dans ces régions. Il est donc primordial, dans un contexte de changement climatique arctique, de poursuivre et d’améliorer le suivi à grande échelle du couvert nival afin de mieux comprendre les processus gouvernant la variabilité spatio-temporelle du manteau neigeux. Plus spécifiquement, l’Équivalent en Eau de la Neige (EEN) est généralement utilisé pour quantifier deux propriétés (hauteur et densité) de la neige. Son estimation à grande échelle dans les régions éloignées tel que l’Arctique provient actuellement essentiellement de produits en micro-ondes passives satellitaires. Cependant, il existe encore beaucoup d’incertitudes sur les techniques d’assimilation de l’ÉEN par satellite et ce projet vise une réduction de l’erreur liée à l’estimation de l’ÉEN en explorant deux des principales sources de biais tels que : 1) la variabilité spatiale de l’épaisseur et des différentes couches du manteau neigeux arctique liées à la topographie et la végétation au sol influençant l’estimation de l’ÉEN; et 2) les modèles de transfert radiatif micro-ondes de la neige et du sol ne bénéficient pas actuellement d’une bonne paramétrisation en conditions arctiques, là où les erreurs liées à l’assimilation de l’ÉEN sont les plus importantes. L’objectif global est donc d’analyser les propriétés géophysiques du couvert nival en utilisant des outils de télédétection et de modélisation pour diminuer l’erreur liée à la variabilité spatiale locale dans l’estimation du ÉEN à grande échelle, tout en améliorant la compréhension des processus locaux qui affectent cette variabilité. Premièrement, une analyse haute résolution à l’aide de l’algorithme Random Forest a permis de prédire la hauteur de neige à une résolution spatiale de 10 m avec une RMSE de 8 cm (23%) et d’en apprendre davantage sur les processus de distribution de la neige en Arctique. Deuxièmement, la variabilité du manteaux neigeux arctique (hauteur et microstructure) a été incorporée dans des simulations en transfert radiatif micro-ondes de la neige et comparée au capteur satellitaire SSMIS. L’ajout de variabilité améliore la RMSE des simulations de 8K par rapport à un manteau neigeux uniforme. Finalement, une paramétrisation du sol gelé est présentée à l’aide de mesures de rugosité provenant de photogrammétrie (Structure-from-Motion). Cela a permis d’investiguer trois modèles de réflectivité micro-ondes du sol ainsi que la permittivité effective du sol gelé avec une rugosité SfM d’une précision de 0.1 mm. Ces données de rugosité SfM avec une permittivité optimisée (ε'_19 = 3.3, ε'_37 = 3.6) réduisent significativement l’erreur des températures de brillance simulées par rapport à des mesures au sols (RMSE = 3.1K, R^2 = 0.71) pour toutes les fréquences et polarisations. Cette thèse offre une caractérisation des variables de surface (neige et sol) en Arctique en transfert radiatif micro-ondes qui bénéficie aux multiples modélisations (climatiques et hydrologiques) de la cryosphère

Modelling of Multi-Frequency Microwave Backscatter and Emission of Land Surface by a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP)

Emission and backscattering signals of land surfaces at different frequencies have distinctive responses to soil and vegetation physical states. The use of multi-frequency combined active and passive microwave signals provides complementary information to better understand and interpret the observed signals in relation to surface states and the underlying physical processes. Such a capability also improves our ability to retrieve surface parameters and states such as soil moisture, freeze-thaw dynamics and vegetation biomass and vegetation water content (VWC) for ecosystem monitoring. We present here a prototype Community Land Active Passive Microwave Radiative Transfer Modelling platform (CLAP) for simulating both backscatter (&sigma;0) and emission (TB) signals of land surfaces, in which the CLAP is backboned by an air-to-soil transition model (ATS) (accounting for surface dielectric roughness) integrated with the Advanced Integral Equation Model (AIEM) for modelling soil surface scattering, and the Tor Vergata model for modelling vegetation scattering and the interaction between vegetation and soil parts. The CLAP was used to simulate both ground-based and space-borne multi-frequency microwave measurements collected at the Maqu observatory on the eastern Tibetan plateau. The ground-based systems include a scatterometer system (1&ndash;10 GHz) and an L-band microwave radiometer. The space-borne measurements are obtained from the X-band and C-band Advanced Microwave Scanning Radiometer 2 (AMSR2) radiation observations. The impacts of different vegetation properties (i.e., structure, water and temperature dynamics) and soil conditions (i.e., different moisture and temperature profiles) on the microwave signals were investigated by CLAP simulation for understanding factors that can account for diurnal variations of the observed signals. The results show that the dynamic VWC partially accounts for the diurnal variation of the observed signal at the low frequencies (i.e., S- and L-bands), while the diurnal variation of the observed signals at high frequencies (i.e., X- and C-bands) is more due to vegetation temperature changing, which implies the necessity to first disentangle the impact of vegetation temperature for the use of high frequency microwave signals. The model derived vegetation optical depth &tau; differs in terms of frequencies and different model parameterizations, while its diurnal variation depends on the diurnal variation of VWC regardless of frequency. After normalizing &tau; at multi-frequency by wavenumber, difference is still observed among different frequencies. This indicates that &tau; is indeed frequency-dependent, and &tau; for each frequency is suggested to be applied in the retrieval of soil and vegetation parameters. Moreover, &tau; at different frequencies (e.g., X-band and L-band) cannot be simply combined for constructing accurate long time series microwave-based vegetation product. To this purpose, it is suggested to investigate the role of the leaf water potential in regulating plant water use and its impact on the normalized &tau; at multi-frequency. Overall, the CLAP is expected to improve our capability for understanding and applying current and future multi-frequency space-borne microwave systems (e.g. those from ROSE-L and CIMR) for vegetation monitoring.</p

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

The Evaluation of the Additive Information Contained in New Ecohydrological Measurements

The development of ecohydrological frameworks and theories under the ongoing global climate crisis depends on the development of new and advanced ecohydrological measurements. Currently, numerous of datasets have been collected at plot and ecosystem levels to understand the complex interactions of along the soil, plant, and atmosphere continuum. The development of new measurements costs considerable effort, time, and funding. Therefore, it is important to quantify if new measurements contain useful information for ecohydrological studies, and how the information encoded in the new measurements are transferred to understand and predict key environmental variables. In this dissertation, we show that predictions in vegetation dynamics can be improved by adding observations from advanced satellite and in situ sensor systems. First, we evaluated new satellite soil moisture and vegetation optical depth measurements by Soil Moisture Passive Active (SMAP). We find that there is more opportunity for SMAP soil moisture and vegetation observations to be useful in locations where the daily vegetation climatology cannot adequately reflect observed vegetation dynamics. We also find that the uncertainties SMAP dual channel algorithm (DCA) are largely contributed by uncertainty of the algorithm’s inputs (horizontal and vertical polarized brightness temperature), while the informational uncertainty of the SMAP DCA model itself is more related to the retrieval quality of soil moisture. The informational redundancy and synergy of the two brightness temperature measurements from SMAP are tightly related to the SMAP soil moisture retrieval quality. Finally, we apply a similar informational analysis to new isotopic measurements at the National Ecological Observation Network (NEON). We find that majority of the information from the isotope measurements collected by NEON is unique, which cannot be obtained by other meteorological variables. Carbon isotope (δ13C) provides more additional information about LH in arid locations, while the water isotope (δ2H) provides more additional information about LH at locations with higher aridity, lower mean annual temperature, and lower mean site elevation. These studies show that informational analysis is useful to evaluated how additional information is encoded in new ecohydrological measurements

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Soil Moisture Estimation by SAR in Alpine Fields Using Gaussian Process Regressor Trained by Model Simulations

In this paper, we address the problem of retrieving soil moisture over a grassland alpine area from Synthetic Aperture Radar (SAR) data using a statistical algorithm trained by simulations of a physical model. A time series of C-band VV-polarized Wide Swath images acquired by Envisat Advanced SAR (ASAR) in the snow-free periods of 2010 and 2011 was simulated using a discrete radiative transfer model (RTM). The test area was located in the Mazia valley, South Tyrol (Italy), where the main land types are meadows and pastures. Soil moisture was collected from five meteorological stations, two of which situated in meadows and the rest in pastures. The smallest and the highest RMSEs of the RTM simulations were 0.78 dB and 1.91 dB, respectively. After backscattering simulation, the top soil moisture was estimated using Gaussian Process Regression (GPR). GPR was trained with the backscatter model simulations (including terrain features) for 2010, and then used to predict moisture from radar observations acquired in 2011. The relative importance of different input features was also assessed. The RMSE of the predicted soil moisture for the largest training data set (including aspect as a terrain feature) was 5.6% Vol. and the corresponding correlation coefficient was 0.84