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This paper describes a prototype network of automated in situ measurements of hyperspectral water reflectance suitable for satellite validation and water quality monitoring. Radiometric validation of satellite-derived water reflectance is essential to ensure that only reliable data, e.g., for estimating water quality parameters such as chlorophyll a concentration, reach end-users. Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for "water colour", the advantage of hyperspectral over multispectral in situ measurements is clear. Two hyperspectral measurement systems, PANTHYR (based on the mature TRIOS/ RAMSES radiometer) and HYPSTAR® (a newly designed radiometer), have been integrated here in theWATERHYPERNET network with SI-traceable calibration and characterisation. The systems have common data acquisition protocol, data processing and quality control. The choice of validation site and viewing geometry and installation considerations are described in detail. Three demonstration cases are described: 1. PANTHYR data from two sites are used to validate Sentinel-2/MSI (A- 2. HYPSTAR® data at six sites are used to validate Sentinel-3/OLCI (A- 3. PANTHYR and HYPSTAR® data in Belgian North Sea waters are used to monitor phytoplankton parameters, including Phaeocystis globosa, over two 5 month periods. Conclusion are drawn regarding the quality of Sentinel-2/MSI and Sentinel-3/OLCI data, including indications where improvements could be made. For example, a positive bias (mean difference) is found for ACOLITE_DSF processing of Sentinel-2 in clear waters (Acqua Alta) and clues are provided on how to improve this processing. The utility of these in situ measurements, even without accompanying hyperspectral satellite data, is demonstrated for phytoplankton monitoring. The future evolution of the WATERHYPERNET network is outlined, including geographical expansion, improvements to hardware reliability and to the measurement method (including uncertainty estimation) and plans for daily distribution of near real-time data. [ABSTRACT FROM AUTHOR]

Validation of water reflectance using in situ data is essential to ensure the quality of ocean colour satellite-derived products useful for water quality monitoring, like turbidity and chlorophyll-a concentration. Since December 2021, the HYPERNETS automated hyperspectral system has been collecting data in the optically complex and highly turbid waters of the Río de la Plata, an ideal scenario for testing atmospheric correction algorithms' performance. The site, located 60 km south of Buenos Aires (Argentina), is described in relation to the water reflectance spectral features and variability using high spatial resolution imagery and a methodology is proposed to objectively select a sensor-specific location of a reference pixel for satellite validation. Six months of data is used to evaluate surface water reflectance operational products from multi-spectral systems like Landsat 8&9/OLI (L89/OLI), Sentinel-2/MSI (S2/MSI) & Sentinel-3/OLCI (S3/OLCI), and PlanetScope SuperDoves (PS/SD), and also non standard products for Aqua/MODIS (Aqua/MODIS) and SNPP&JPSS1/VIIRS (SJ/VIIRS) missions. Moreover, the standard surface water reflectance product from the hyperspectral PRISMA mission could also be evaluated. The matchups show general good results when in situ measurements are compared to L2 standard products of high spatial resolution sensors that use land-based atmospheric correction approach, if sun glint contamination is avoided. Low mean relative percentage difference was found for S2/MSI (2.45%) and L89/OLI (-3.52%), but higher for PS/SD (30.7%). In turn, S3/OLCI medium resolution also showed low mean relative differences (2.31%), while SJ/VIIRS and Aqua/MODIS showed larger and negative differences (-16.35 for SJ/VIIRS and -35.6% for Aqua/MODIS) which showed a clear increase towards the shortest blue bands. The results show the great potential of the HYPERNETS automated system to provide high quality and quantity of data for validation of satellite data at all visible and near infrared (VNIR, 400-900 nm) wavelengths in a multi-mission perspective. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key geophysical variable that can be estimated at regional scales using remote sensing techniques, by making use of the known relationship between soil reflectivity and the dielectric constant in the microwave regime. In this context, the exploitation of available illuminators of opportunity that currently emit large amounts of power at microwave frequencies (compared to typical synthetic aperture radar systems) is promising. Some published techniques estimate SM by analyzing the interference pattern (IP) between direct and reflected signal as measured by a single antenna (i.e., IP technique). In this letter, a new approach to simulate the IP is proposed, in which the soil roughness is modeled straightforwardly using the second-order small perturbation model. Results illustrate that the “notch” in the VV-polarization IP (related to the Brewster angle) can only be directly observed for very low values of soil rms roughness ($s < 0.5$ cm). For typical values of soil roughness ($s\sim 1.2$ cm), the notch disappears and only a minimum in the IP is observed near the Brewster angle. [ABSTRACT FROM AUTHOR]

The spatial sampling interval, as related to the ability to digitize a soil profile with a certain number of features per unit length, depends on the profiling technique itself. From a variety of profiling techniques, roughness parameters are estimated at different sampling intervals. Since soil profiles have continuous spectral components, it is clear that roughness parameters are influenced by the sampling interval of the measurement device employed. In this work, we contributed to answer which sampling interval the profiles needed to be measured at to accurately account for the microwave response of agricultural surfaces. For this purpose, a 2-D laser profiler was built and used to measure surface soil roughness at field scale over agricultural sites in Argentina. Sampling intervals ranged from large (50 mm) to small ones (1 mm), with several intermediate values. Large- and intermediate-sampling-interval profiles were synthetically derived from nominal, 1 mm ones. With these data, the effect of sampling-interval-dependent roughness parameters on backscatter response was assessed using the theoretical backscatter model IEM2M. Simulations demonstrated that variations of roughness parameters depended on the working wavelength and was less important at L-band than at C- or X-band. In any case, an underestimation of the backscattering coefficient of about 1-4 dB was observed at larger sampling intervals. As a general rule a sampling interval of 15 mm can be recommended for L-band and 5 mm for C-band. [ABSTRACT FROM AUTHOR]

An Observing System Simulation Experiment (OSSE) for the Aquarius/SAC-D mission has been developed for assessing the accuracy of soil moisture retrievals from passive L-band remote sensing. The implementation of the OSSE is based on the following: a 1-km land surface model over the Red-Arkansas River Basin, a forward microwave emission model to simulate the radiometer observations, a realistic orbital and sensor model to resample the measurements mimicking Aquarius operation, and an inverse soil moisture retrieval model. The simulation implements a zero-order radiative transfer model. Retrieval is performed by direct inversion of the forward model. The Aquarius OSSE attempts to capture the influence of various error sources, such as land surface heterogeneity, instrument noise, and retrieval ancillary parameter uncertainty, all on the accuracy of Aquarius surface soil moisture retrievals. In order to assess the impact of these error sources on the estimated volumetric soil moisture, a quantitative error analysis is performed by comparison of footprint-scale synthetic soil moisture with “true” soil moisture fields obtained from the direct aggregation of the original 1-km soil moisture field input to the forward model. Results show that, in heavily vegetated areas, soil moisture retrievals have a positive bias that can be suppressed with an alternative aggregation strategy for ancillary parameter vegetation water content (VWC). Retrieval accuracy was also evaluated when adding errors to 1-km VWC (which are intended to account for errors in VWC derived from remote sensing data). For soil moisture retrieval root-mean-square error on the order of 0.05 m3/m3, the error in VWC should be less than 12%. [ABSTRACT FROM PUBLISHER]

An Observing System Simulation Experiment (OSSE) for the Aquarius/SAC-D mission that includes different models for forward and retrieval processes is presented. This OSSE is implemented to study the errors related to the use of simple retrieval models in passive microwave applications. To this end, a theoretical forward model was introduced, which is suitable to reproduce some of the complexities related to canopy vegetation scattering. So far, this OSSE has been successfully exploited to study the artifacts in the retrieved soil moisture associated to: 1) uncertainties and aggregation of the ancillary parameters needed for the retrieval, and 2) instrumental noise effects. In this paper, we attempt to model the influence of this “model asymmetry” (different forward and inverse model) in the estimated soil moisture. These asymmetries are related to the fact that the emissivity of real surfaces is complex and strongly dependent on land cover type and condition. In particular, surface covered by average to dense vegetation presents complex scattering properties, related to canopy structure. Using this theoretical model, the difficulties related to retrieving soil moisture from passive data with a simple model are studied. The accuracy of the soil moisture estimation is analyzed in order to illustrate the impact of discrepancies between both models. In general, retrieved soil moisture performs worse over dense vegetated areas and under wet conditions. Furthermore, accuracy is highly dependent on land cover. [ABSTRACT FROM PUBLISHER]

The inability to monitor wetland drag coefficients at a regional scale is rooted in the difficulty to determine vegetation structure from remote sensing data. Based on the fact that the backscattering coefficient is sensitive to marsh vegetation structure, this paper presents a methodology to estimate the drag coefficient from a combination of SAR images, interaction models and ancillary data. We use as test case a severe fire event occurred in the Paraná River Delta (Argentina) at the beginning of 2008, when 10% of the herbaceous vegetation was burned up. A map of the reduction of the wetland drag coefficient is presented. [ABSTRACT FROM AUTHOR]

Wetlands are areas where the presence of water at or near the soil surface drives the natural system. Imaging radars (SARs) have distinct characteristics which make them of significant value for monitoring and mapping wetland inundation dynamics. The presence or absence of water (which has a much higher dielectric constant than dry or wet soil) in wetlands may significantly alter the signal detected from these areas depending on the dominant vegetation type, density, and height. The objective of this paper is to present our current research efforts to explain and correctly simulate the radar response of wetland vegetation/inundation mixtures, and use simulations as an aid for retrieval applications. The radar response of junco marshes under different flood conditions and vegetation stages is analysed using a set of 13 multipolarization ENVISAT ASAR scenes acquired over the Parana River Delta marshes during the period 2003-2005. The main aspect of the approach followed is the simulation of SAR wave interactions with vegetation and water, using an adapted and improved version of the EM model developed at Tor Vergata University. The results obtained indicate that with the refined EM model, it is possible to represent with a good accuracy VV and HH SAR responses of junco marshes for a variety of environmental conditions. Further work and data are needed to explain measured HV backscattering. The general agreement obtained between simulations and observations permitted the development of a simple retrieval scheme, and estimates of water level below the canopy were obtained for different environmental conditions. RMS errors of forward simulations and retrievals are reported and discussed. [ABSTRACT FROM AUTHOR]