Ensemble of Retrieval Algorithms and Electromagnetic Models for Soil and Vegetation Water Content Estimation from SAR Images

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Daily MODIS Snow Cover Maps for the European Alps from 2002 onwards at 250 m Horizontal Resolution Along with a Nearly Cloud-Free Version

Snow cover dynamics impact a whole range of systems in mountain regions, from society to economy to ecology; and they also affect downstream regions. Monitoring and analyzing snow cover dynamics has been facilitated with remote sensing products. Here, we present two high-resolution daily snow cover data sets for the entire European Alps covering the years 2002 to 2019, and with automatic updates. The first is based on moderate resolution imaging spectroradiometer (MODIS) and its implementation is specifically tailored to the complex terrain, exploiting the highest possible resolution available of 250 m. The second is a nearly cloud-free product derived from the first using temporal and spatial filters, which reduce average cloud cover from 41.9% to less than 0.1%. Validation has been performed using an extensive network of 312 ground stations, and for the cloud filtering also with cross-validation. Average overall accuracies were 93% for the initial and 91.5% for the cloud-filtered product using the ground stations; and 95.3% for the cross-validation of the cloud-filter. The data can be accessed online and via the R and python programming languages. Possible applications of the data include but are not limited to hydrology, cryosphere and climate

Cross-Comparison of MODIS and CloudSat Data as a Tool to Validate Local Cloud Cover Masks

This paper presents a cross-comparison of the data acquired by the MODIS and CloudSat sensors in order to understand the limit of the developed cloud-mask algorithm and to provide a quantitative validation assessment of cloud masks by using exclusively remotely sensed data. The analysis has been carried out by comparing both the intermediate levels of the cloud mask such as the brightness temperatures and the reflectance values for different channels, and the cloud mask itself with the cloud profiles as measured by the CloudSat sensor. The comparison between MODIS cloud tests and the CloudSat profiles indicates an agreement with hit rates (H) and Hanssen-Kuiper Skill Score (KSS) varying between 0.7 and 1.0 and 0.4 and 1.0, respectively. In this case, the low values of H and KSS are found due to the limitation of CloudSat to detect low clouds. The comparison between the cloud mask and the CloudSat profile determines H and KSS values between 0.6 and 1, except for one case. The CloudSat profile has also been compared with the Standard MODIS cloud mask in order to understand the improvement obtained in the use of local adapted thresholds. A comparison of MODIS and CALIPSO data is also presented

Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates

Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar

Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

In this contribution we analyze the performance of a monthly river discharge forecasting model with a Support Vector Regression (SVR) technique in a European alpine area. We considered as predictors the discharges of the antecedent months, snow-covered area (SCA), and meteorological and climatic variables for 14 catchments in South Tyrol (Northern Italy), as well as the long-term average discharge of the month of prediction, also regarded as a benchmark. Forecasts at a six-month lead time tend to perform no better than the benchmark, with an average 33% relative root mean square error (RMSE%) on test samples. However, at one month lead time, RMSE% was 22%, a non-negligible improvement over the benchmark; moreover, the SVR model reduces the frequency of higher errors associated with anomalous months. Predictions with a lead time of three months show an intermediate performance between those at one and six months lead time. Among the considered predictors, SCA alone reduces RMSE% to 6% and 5% compared to using monthly discharges only, for a lead time equal to one and three months, respectively, whereas meteorological parameters bring only minor improvements. The model also outperformed a simpler linear autoregressive model, and yielded the lowest volume error in forecasting with one month lead time, while at longer lead times the differences compared to the benchmarks are negligible. Our results suggest that although an SVR model may deliver better forecasts than its simpler linear alternatives, long lead-time hydrological forecasting in Alpine catchments remains a challenge. Catchment state variables may play a bigger role than catchment input variables; hence a focus on characterizing seasonal catchment storage—Rather than seasonal weather forecasting—Could be key for improving our predictive capacity.JRC.H.1-Water Resource

Synergy of Cassini SAR and altimeter acquisitions for the retrieval of dune field characteristics on Titan

This work focuses on the retrieval of Titan’s dune field characteristics addressing different radar modes. The main purpose of the proposed work is to exploit a possible synergy between SAR and altimeter acquisitions modes to provide information about dune field. Cassini has performed 86 Titan flybys in which several observations of dune fields have been collected in altimetry mode. There are several cases in which SAR and altimeter have been acquired over same areas covered by dune fields, such as during T28 (SAR) and T30 (altimeter) flybys. Altimetry together with SAR data have been used to derive the rms slopes of dunes (large scale) over Fensal area, this information has been employed to calculate SAR incidence angle with respect to dunes. We extracted backscattering coefficients of bright and dark areas detected in the analyzed SAR image in order to evaluate the angular response of scattering. Through the Geometric Optics model we retrieve roughness values (small scale rms slope) for both dune bright and dark areas

A Novel Approach Based on a Hierarchical Multiresolution Analysis of Optical Time Series to Reconstruct the Daily High-Resolution Snow Cover Area

High-resolution (HR) snow cover maps derived by remotely sensed images are an asset for data assimilation in hydrological models. However, the current satellite missions do not provide daily HR multispectral observations suitable for an accurate snow monitoring in alpine environments. On the contrary, low-resolution (LR) sensors acquire daily information of snow cover fraction (SCF) but at an inappropriate spatial scale. This article proposes a novel approach that combines multisource and multiscale acquisitions to infer the daily HR snow cover area (SCA) for mountainous basins. The approach builds on the assumption that interannual snow patterns are both affected by the local geomorphometry and meteorology. We derive these patterns through a hierarchical multistep approach based on historical statistical analyses on a long and sparse HR time-series. At each step, we obtain HR snow cover maps with higher number of reconstructed pixels but decreasing level of confidence. Historical data are used to estimate the probability that a HR pixel is covered by snow according to two possible multiscale strategies: 1) HR gap-filling, or 2) LR downscaling. These analyses lead to the identification of the patterns that regularly appear given certain conditions. When no systematic patterns are observed, we reinforce the inference of the pixel class by a generalized additive model that exploits not only the historical data, but also explicit geomorphometric, global snow, and multitemporal properties. The proposed approach has been validated on a catchment in the Sierra Nevada, USA, for three hydrological years (2017–2019) showing an average overall accuracy of 92%

Multi-Temporal X-Band Radar Interferometry Using Corner Reflectors: Application and Validation at the Corvara Landslide (Dolomites, Italy)

From the wide range of methods available to landslide researchers and practitioners for monitoring ground displacements, remote sensing techniques have increased in popularity. Radar interferometry methods with their ability to record movements in the order of millimeters have been more frequently applied in recent years. Multi-temporal interferometry can assist in monitoring landslides on the regional and slope scale and thereby assist in assessing related hazards and risks. Our study focuses on the Corvara landslides in the Italian Alps, a complex earthflow with spatially varying displacement patterns. We used radar imagery provided by the COSMO-SkyMed constellation and carried out a validation of the derived time-series data with differential GPS data. Movement rates were assessed using the Permanent Scatterers based Multi-Temporal Interferometry applied to 16 artificial Corner Reflectors installed on the source, track and accumulation zones of the landslide. The overall movement trends were well covered by Permanent Scatterers based Multi-Temporal Interferometry, however, fast acceleration phases and movements along the satellite track could not be assessed with adequate accuracy due to intrinsic limitations of the technique. Overall, despite the intrinsic limitations, Multi-Temporal Interferometry proved to be a promising method to monitor landslides characterized by a linear and relatively slow movement rates

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