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

Laser microdissection-assisted analysis of the functional fate of iron deficiency-induced root hairs in cucumber.

Iron ranks fourth in the sequence of abundance of the elements in the Earth's crust, but its low bio-availability often limits plant growth. When present in suboptimal amounts, the acquisition of iron by plants is aided by a suite of responses, comprising molecular and developmental changes that facilitate the uptake of iron from sparingly soluble pools. The expression of genes involved in the mobilization of iron (CsHA1), the reduction of ferric chelates (CsFRO1), and in the uptake of ferrous iron (CsIRT1) was investigated in epidermal cells of Fe-sufficient and Fe-deficient cucumber (Cucumis sativum L.) roots using the Laser Microdissection and Pressure Catapulting (LMPC) method. Growing plants hydroponically in media deprived of iron induced the differentiation of almost all epidermal cells into root hairs. No root hairs were formed under iron-replete conditions. The formation of root hairs in response to Fe starvation was associated with a dramatic increase in message levels of CsFRO1, CsIRT1, and the iron-inducible H(+)-ATPase isoform CsHA1, when compared to epidermal cells of Fe-sufficient plants. On the contrary, transcripts of a housekeeping ATPase isoform, CsHA2, were not detected in root hairs, suggesting that Fe-deficiency-induced acidification is predominantly mediated by CsHA1. These data show that the formation of root hairs in response to iron deficiency is associated with cell-specific accumulation of transcripts that are involved in iron acquisition. The results also show that this includes the differential regulation of ATPase isoforms with similar function, but supposedly different characteristics, to counteract the imbalance in nutrient supply efficiently

Application of neural networks for the retrieval of forest woody volume from SAR multifrequency data at L and C bands.

This work aims at investigating the potential of L (ALOS/PALSAR) and C (ENVISAT/ASAR) band SAR images in forest biomass monitoring and setting up a retrieval algorithm, based on Artificial Neural Networks (ANN), for estimating the Woody Volume (WV, in m3/ha) from combined satellite acquisitions. The investigation was carried out on two test areas in central Italy, where ground WV measurements were available. An innovative retrieval algorithm based on ANN was developed for estimating WV from L and C bands SAR data. The novelty consists of an accurate training of the ANN with several thousands of data, which allowed the implementation of a very robust algorithm. The RMSE values found on San Rossore area were ?40 m3/ha (L band data only), and 25-30 m3/ha (L with C band). On Molise, by using combined data at L and C bands, RMSE<30m3/ha was obtained. Keywords: ANN; backscattering; Woody Volume; LiDAR; ALOS/PALSAR; ENVISAT/ASAR

Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas

In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo‐SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in‐situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L‐, C‐ and X‐bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non‐forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient (°) was computed for each sensor‐polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L‐ and X‐ bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors

Machine-Learning Applications for the Retrieval of Forest Biomass from Airborne P-Band SAR Data

This study aimed at evaluating the potential of machine learning (ML) for estimating forest biomass from polarimetric Synthetic Aperture Radar (SAR) data. Retrieval algorithms based on two different machine-learning methods, namely Artificial Neural Networks (ANNs) and Supported Vector Regressions (SVRs), were implemented and validated using the airborne polarimetric SAR data derived from the AfriSAR, BioSAR, and TropiSAR campaigns. These datasets, composed of polarimetric airborne SAR data at P-band and corresponding biomass values from in situ and LiDAR measurements, were made available by the European Space Agency (ESA) in the framework of the Biomass Retrieval Algorithm Inter-Comparison Exercise (BRIX). The sensitivity of the SAR measurements at all polarizations to the target biomass was evaluated on the entire set of data from all the campaigns, and separately on the dataset of each campaign. Based on the results of the sensitivity analysis, the retrieval was attempted by implementing general algorithms, using the entire dataset, and specific algorithms, using data of each campaign. Algorithm inputs are the SAR data and the corresponding local incidence angles, and output is the estimated biomass. To allow the comparison, both ANN and SVR were trained using the same subset of data, composed of 50% of the available dataset, and validated on the remaining part of the dataset. The validation of the algorithms demonstrated that both machine-learning methods were able to estimate the forest biomass with comparable accuracies. In detail, the validation of the general ANN algorithm resulted in a correlation coefficient R = 0.88, RMSE = 60 t/ha, and negligible BIAS, while the specific ANN for data obtained R from 0.78 to 0.94 and RMSE between 15 and 50 t/ha, depending on the dataset. Similarly, the general SVR was able to estimate the target parameter with R = 0.84, RMSE = 69 t/ha, and BIAS negligible, while the specific algorithms obtained 0.22 ≤ R ≤ 0.92 and 19 ≤ RMSE ≤ 70 (t/ha). The study also pointed out that the computational cost is similar for both methods. In this respect, the training is the only time-demanding part, while applying the trained algorithm to the validation set or to any other dataset occurs in near real time. As a final step of the study, the ANN and SVR algorithms were applied to the available SAR images for obtaining biomass maps from the available SAR images

Incidentally discovered pheochromocytoma and aldosterone-producing adenoma in the same adrenal gland

Simultaneous occurrence of pheochromocytoma and aldosterone-producing adrenocortical tumor has been rarely reported in patients with symptoms or findings suggestive for both neoplasms. Herein, we report and discuss on a challenging case of synchronous pheochromocytoma and aldosterone-producing adenoma incidentally detected in the same adrenal gland and documented by biochemical studies and pathological examination

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

Filamentous sieve element proteins are able to limit phloem mass flow, but not phytoplasma spread

In Fabaceae, dispersion of forisomes\u2014highly ordered aggregates of sieve element proteins\u2014in response to phytoplasma infection was proposed to limit phloem mass flow and, hence, prevent pathogen spread. In this study, the involvement of filamentous sieve element proteins in the containment of phytoplasmas was investigated in non-Fabaceae plants. Healthy and infected Arabidopsis plants lacking one or two genes related to sieve element filament formation\u2014AtSEOR1 (At3g01680), AtSEOR2 (At3g01670), and AtPP2-A1 (At4g19840)\u2014were analysed. TEM images revealed that phytoplasma infection induces phloem protein filament formation in both the wild-type and mutant lines. This result suggests that, in contrast to previous hypotheses, sieve element filaments can be produced independently of AtSEOR1 and AtSEOR2 genes. Filament presence was accompanied by a compensatory overexpression of sieve element protein genes in infected mutant lines in comparison with wild-type lines. No correlation was found between phloem mass flow limitation and phytoplasma titre, which suggests that sieve element proteins are involved in defence mechanisms other than mechanical limitation of the pathogen

Remote Sensing of Forest Biomass Using GNSS Reflectometry

In this study, the capability of Global Navigation Satellite System Reflectometry in evaluating forest biomass from space has been investigated by using data coming from the TechDemoSat-1 (TDS-1) mission of Surrey Satellite Technology Ltd. and from the Cyclone Satellite System (CyGNSS) mission of NASA. The analysis has been first conducted using TDS-1 data on a local scale, by selecting five test areas located in different parts of the Earth's surface. The areas were chosen as examples of various forest coverages, including equatorial and boreal forests. Then, the analysis has been extended by using CyGNSS to a global scale, including any type of forest coverage. The peak of the Delay Doppler Map calibrated to retrieve an "equivalent" reflectivity has been exploited for this investigation and its sensitivity to forest parameters has been evaluated by a direct comparison with vegetation optical depth (VOD) derived from the Soil Moisture Active Passive L-band radiometer, with a pantropical aboveground biomass (AGB) map and then with a tree height (H) global map derived from the Geoscience Laser Altimeter System installed on-board the ICEsat satellite. The sensitivity analysis confirmed the decreasing trend of the observed equivalent reflectivity for increasing biomass, with correlation coefficients 0.31 ≤ R ≤ 0.54 depending on the target parameter (VOD, AGB, or H) and on the considered dataset (local or global). These correlations were not sufficient to retrieve the target parameters by simple inversion of the direct relationships. The retrieval has been therefore based on Artificial Neural Networks making it possible to add other inputs (e.g., the incidence angle, the signal to noise ratio, and the lat/lon information in case of global maps) to the algorithm. Although not directly correlated to the biomass, these inputs helped in improving the retrieval accuracy. The algorithm was tested on both the selected areas and globally, showing a promising ability to retrieve the target parameter, either AGB or H, with correlation coefficients R ≃ 0.8