An enhanced resolution brightness temperature product for future conical scanning microwave radiometers

An enhanced spatial resolution brightness temperature product is proposed for future conical scan microwave radiometers. The technique is developed for Copernicus Imaging Microwave Radiometer (CIMR) measurements that are simulated using the CIMR antenna pattern at the L-band and the measurement geometry proposed in the Phase A study led by Airbus. An inverse antenna pattern reconstruction method is proposed. Reconstructions are obtained using two CIMR configurations, namely, using measurements collected at L-band by the forward (FWD) scans only, and combining forward and backward (FWD+BWD) scans. Two spatial grids are adopted, namely, 3 km x 3 km and 36 km x 36 km. Simulation results, referred to synthetic and realistic reference brightness fields, demonstrate the soundness of the proposed scheme that provides brightness temperature fields reconstructed at a spatial resolution up to ~ 1.9 times finer than the measured field when using the FWD+BWD combination.The work of Claudio Estatico was supported in part by the Gruppo Nazionale di Calcolo Scientifico–Istituto Nazionale di Alta Matematica (GNCS-INDAM), Italy. This work has been produced for the European Space Agency (ESA) in the frame of the Copernicus Program as a partnership between ESA and the European Commission.Peer ReviewedPostprint (author's final draft

Estimation of Indonesian Peat Forest Carbon Emissions Based on Soil Moisture Active Passive (SMAP) Satellite Image

Calculation of carbon emission in tropical peatland forest still unsolved problem. In this paper, we propose a method to calculate carbon emission by using Soil Moisture Active Passive (SMAP) satellite. The SMAP images on January 2018 enhanced 1.3 radiometer Global Daily 9 km, EASE grid overlayed with peat map. The water table mapping obtained by using empirical relation between soil moisture and water table in January 2018 shows a pattern according to some observation. The carbon emission map on January 2018 shows the average is about 280 gC km-2

Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations

Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0–5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016–2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions

Making the best use of GRACE, GRACE‐FO and SMAP data through a constrained Bayesian data‐model integration

The Gravity Recovery and Climate Experiment (GRACE, 2003–2017) and its Follow-On mission GRACE-FO (2018-now) provide global estimates of the vertically integrated Terrestrial Water Storage Changes (TWSC). Since 2015, the Soil Moisture Active Passive (SMAP) radiometer observes global L-band brightness temperatures, which are sensitive to near-surface soil moisture. In this study, we introduce our newly developed Constrained Bayesian (ConBay) optimization approach to merge the TWSC of GRACE/GRACE-FO along with SMAP soil moisture data into the ∼10 km resolution W3RA water balance model. ConBay is formulated based on two hierarchical multivariate state-space models to (I) separate land hydrology compartments from GRACE/GRACE-FO TWSC, and (II) constrain the estimation of surface soil water storage based on the SMAP data. The numerical implementation is demonstrated over the High Plain (HP) aquifer in the United States between 2015 and 2021. The implementation of ConBay is compared with an unconstrained Bayesian formulation, and our validations are performed against in-situ USGS groundwater level observations and the European Space Agency (ESA)'s Climate Change Initiative (CCI) soil moisture data. Our results indicate that the single GRACE/GRACE-FO assimilation improves particularly the groundwater compartment. Adding SMAP data to the ConBay approach controls the updates assigned to the surface storage compartments. For example, correlation coefficients between the ESA CCI and the ConBay-derived surface soil water storage (0.8) that are considerably higher than those derived from the unconstrained experiment (−0.3) in the North HP. The percentage of updates introduced to the W3RA groundwater storage is also decreased from 64% to 57%

Mapping Firn Saturation Over Greenland Using NASA's Soil Moisture Active Passive Satellite

Mapping the spatial extent of recently identified englacial hydrological features (i.e., ice slabs and perennial firn aquifers) formed by meters-thick water-saturated firn layers over the percolation facies of the Greenland Ice Sheet using L -band microwave radiometry has recently been demonstrated. However, these initial maps are binary, and do not provide a parameter to estimate the spatial variability in the thickness and volumetric fraction of meltwater stored within the firn pore space. Here, we exploit enhanced-resolution vertical-polarization L -band brightness temperature ( $T_{V}^{B}$ ) imagery (2015&ndash;2019) generated using observations collected over Greenland by NASA&rsquo;s Soil Moisture Active Passive (SMAP) satellite and a simple two-layer L -band brightness temperature model. We map water-saturated firn layers via a &ldquo;firn saturation&rdquo; parameter, and interpret our results together with ice slab and perennial firn aquifer spatial extents, estimates of snow accumulation simulated via the Regional Atmospheric Climate Model (RACMOp2.3), and airborne radar surveys collected via NASA&rsquo;s Operation IceBridge (OIB) campaigns. We find that variable firn saturation parameter values are mapped in lower snow accumulation ice slab areas in western, northern, and northeastern Greenland, where firn is colder and water-saturated firn layers seasonally refreeze as solid-ice. Higher firn saturation parameter values are mapped in higher snow accumulation perennial firn aquifer areas in southeastern, southern, and northwestern Greenland, where firn is near the melting point, and meters-thick water-saturated firn layers exist. Our results have implications for identifying expansive englacial reservoirs that store significant volumes of meltwater in locations that are vulnerable to meltwater-induced hydrofracturing and accelerated outlet glacier flow year-round.View less &nbsp;</p

Mapping Soil Moisture from Remotely Sensed and In-situ Data with Statistical Methods

Soil moisture is an important factor for accurate prediction of agricultural productivity and rainfall runoff with hydrological models. Remote sensing satellites such as Soil Moisture Active Passive (SMAP) offer synoptic views of soil moisture distribution at a regional-to-global scale. To use the soil moisture product from these satellites, however, requires a downscaling of the data from an usually large instantaneous field of view (i.e. 36 km) to the watershed analysis scales ranging from 30 m to 1 km. In addition, validation of the soil moisture products using the ground station observations without an upscaling treatment would lead to cross-level fallacy. In the literature of geographical analysis, scale is one of the top research concens because of the needs for multi-source geospatial data fusion. This dissertation research introduced a multi-level soil moisture data assimilation and processing methodology framework based on spatial information theories. The research contains three sections: downscaling using machine learning and geographically weighted regression, upscaling ground network observation to calibrate satellite data, and spatial and temporal multi-scale data assimilation using spatio-temporal interpolation. (1) Soil moisture downscaling In the first section, a downscaling method is designed using 1-km geospatial data to obtain subpixel soil moisture from the 9-km soil moisture product of the SMAP satellite. The geospatial data includes normalized difference vegetation index (NDVI), land surface temperature (LST), gross primary productivity (GPP), topographical moisture index (TMI), with all resampled to 1-km resolution. The machine learning algorithm – random forest was used to create a prediction model of the soil moisture at a 1-km resolution. The 1-km soil moisture product was compared with the ground samples from the West Texas Mesonet (WTM) station data. The residual was then interpolated to compensate the unpredicted variability of the model. The entire process was based on the concept of regression kriging- where the regression was done by the random forest model. Results show that the downscaling approach was able to achieve better accuracy than the current statistical downscaling methods. (2) Station network data upscaling The Texas Soil Observation Network (TxSON) network was designed to test the feasibility of upscaling the in-situ data to match the scale of the SMAP data. I advanced the upscaling method by using the Voronoi polygons and block kriging with a Gaussian kernel aggregation. The upscaling algorithm was calibrated using different spatial aggregation parameters, such as the fishnet cell size and Gaussian kernel standard deviation. The use of the kriging can significantly reduce the spatial autocorrelation among the TxSON stations because of its declustering ability. The result proved the new upscaling method was better than the traditional ones. (3) Multi-scale data fusion in a spatio-temporal framework None of the current works for soil moisture statistical downscaling honors time and space equally. It is important, however, that the soil moisture products are consistent in both domains. In this section, the space-time kriging model for soil moisture downscaling and upscaling computation framework designed in the last two sections is implemented to create a spatio-temporal integrated solution to soil moisture multi-scale mapping. The present work has its novelty in using spatial statistics to reconcile the scale difference from satellite data and ground observations, and therefore proposes new theories and solutions for dealing with the modifiable areal unit problem (MAUP) incurred in soil moisture mapping from satellite and ground stations

Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique

De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large résolution spatiale (25-50 km) n’en font pas un outil adéquat pour des applications hydrologiques à une échelle locale telles que la modélisation et la prévision hydrologiques. Dans de nombreuses études, une désagrégation d’échelle de l’humidité du sol des produits satellites micro-ondes est faite puis validée avec des mesures in-situ. Toutefois, l’utilisation de ces données issues d’une désagrégation d’échelle n’a pas encore été pleinement étudiée pour des applications en hydrologie. Ainsi, l’objectif de cette thèse est de proposer une méthode de désagrégation d’échelle de l’humidité du sol issue de données satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) à différentes résolutions spatiales afin d’évaluer leur apport sur l’amélioration potentielle des modélisations et prévisions hydrologiques. À partir d’un modèle de forêt aléatoire, une désagrégation d’échelle de l’humidité du sol de SMAP l’amène de 36-km de résolution initialement à des produits finaux à 9-, 3- et 1-km de résolution. Les prédicteurs utilisés sont à haute résolution spatiale et de sources différentes telles que Sentinel-1A, MODIS et SRTM. L'humidité du sol issue de cette désagrégation d’échelle est ensuite assimilée dans un modèle hydrologique distribué à base physique pour tenter d’améliorer les sorties de débit. Ces expériences sont menées sur les bassins versants des rivières Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situés aux États-Unis. De plus, le modèle assimile aussi des données d’humidité du sol en profondeur issue d’une extrapolation verticale des données SMAP. Par ailleurs, les données d’humidité du sol SMAP et les mesures in-situ sont combinées par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilé dans le modèle hydrologique pour tenter d’améliorer la prévision hydrologique sur le bassin versant Au Saumon situé au Québec. Les résultats montrent que l'utilisation de l’humidité du sol à fine résolution spatiale issue de la désagrégation d’échelle améliore la représentation de la variabilité spatiale de l’humidité du sol. En effet, le produit à 1- km de résolution fournit plus de détails que les produits à 3- et 9-km ou que le produit SMAP de base à 36-km de résolution. De même, l’utilisation du produit de fusion SMAP/ in-situ améliore la qualité et la représentation spatiale de l’humidité du sol. Sur le bassin versant Susquehanna, la modélisation hydrologique s’améliore avec l’assimilation du produit de désagrégation d’échelle à 9-km, sans avoir recours à des résolutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la résolution spatiale la plus fine à 1- km qui offre les meilleurs résultats de modélisation hydrologique. L’assimilation de l’humidité du sol en profondeur issue de l’extrapolation verticale des données SMAP n’améliore que peu la qualité du modèle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon améliore la qualité de la prévision du débit, même si celle-ci n’est pas très significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting

Deep Image Prior for Disentangling Mixed Pixels

A mixed pixel in remotely sensed images measures the reflectance and emission from multiple target types (e.g., tree, grass, and building) from a certain area. Mixed pixels exist commonly in spaceborne hyper-/multi-spectral images due to sensor limitations, causing the signature ambiguity problem and impeding high-resolution remote sensing mapping. Disentangling mixed pixels into the underlying constituent components is a challenging ill-posed inverse problem, which requires efficient modeling of spatial prior information and other application-dependent prior knowledge concerning the mixed pixel generation process. The recent deep image prior (DIP) approach and other application-dependent prior information are integrated into a Bayesian framework in the research, which allows comprehensive usage of different prior knowledge. The research improves mixed pixel disentangling using the Bayesian DIP in three key applications: spectral unmixing (SU), subpixel mapping (SPM), and soil moisture product downscaling (SMD). The main contributions are summarized as follows. First, to improve the decomposition of mixed pixels into pure material spectra (i.e., endmembers) and their constituting fractions (i.e., abundances) in SU, a designed deep fully convolutional neural network (DCNN) and a new spectral mixture model (SMM) with heterogeneous noise are integrated into a Bayesian framework that is efficiently solved by a new iterative optimization algorithm. Second, to improve the decomposition of mixed pixels into class labels of subpixels in SPM, a dedicated DCNN architecture and a new discrete SMM are integrated into the Bayesian framework to allow the use of both spatial prior and the forward model. Third, to improve the decomposition of mixed pixels into soil moisture concentrations of subpixels in SMD, a new DIP architecture and a forward degradation model are integrated into the Bayesian framework that is solved by the stochastic gradient descent approach. These new Bayesian approaches improve the state-of-the-art in their respective applications (i.e., SU, SPM, and SMD), which can be potentially utilized for solving other ill-posed inverse problems where simultaneously modeling of the spatial prior and other prior knowledge is needed

Selection of the key earth observation sensors and platforms focusing on applications for Polar Regions in the scope of Copernicus system 2020-2030

An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030. Payload selection is constrained by the variables that can be measured, the power consumption, and weight of the instrument, and the required accuracy and spatial resolution (horizontal or vertical). It involved 20 measurements with observation gaps according to the user requirements that were detected in the top 10 use cases in the scope of Copernicus space infrastructure, 9 potential applied technologies, and 39 available commercial platforms. Additional Earth Observation (EO) infrastructures are proposed to reduce measurements gaps, based on a weighting system that assigned high relevance for measurements associated to Marine for Weather Forecast over Polar Regions. This study concludes with a rank and mapping of the potential technologies and the suitable commercial platforms to cover most of the requirements of the top ten use cases, analyzing the Marine for Weather Forecast, Sea Ice Monitoring, Fishing Pressure, and Agriculture and Forestry: Hydric stress as the priority use cases.Peer ReviewedPostprint (published version

Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices

Supplementary data to this article can be found online at https://doi.org/10.1016/j.rse.2019.111303.Monitoring vegetation carbon in tropical regions is essential to the global carbon assessment and to evaluate the actions oriented to the reduction of forest degradation. Mainly, satellite optical vegetation indices and LiDAR data have been used to this purpose. These two techniques are limited by cloud cover and are sensitive only to the top of vegetation. In addition, the vegetation attenuation to the soil microwave emission, represented by the vegetation optical depth (VOD), has been applied for biomass estimation using frequencies ranging from 4 to 30¿GHz (C- to K-bands). Atmosphere is transparent to microwaves and their sensitivity to canopy layers depends on the frequency, with lower frequencies having greater penetration depths. In this regard, L-band VOD (1.4¿GHz) is expected to enhance the ability to estimate carbon stocks. This study compares the sensitivity of different VOD products (from L, C, and X-bands) and an optical vegetation index (EVI) to the above-ground carbon density (ACD). It quantifies the contribution of ACD and forest cover proportion to the VOD/EVI signals. The study is conducted in Peru, southern Colombia and Panama, where ACD maps have been derived from airborne LiDAR. Results confirm the enhanced sensitivity of L-band VOD to ACD when compared to higher frequency bands, and show that the sensitivity of all VOD bands decreases in the densest forests. ACD explains 34% and forest cover 30% of L-band VOD variance, and these proportions gradually decrease for EVI, C-, and X-band VOD, respectively. Results are consistent through different categories of altitude and carbon density. This pattern is found in most of the studied regions and in flooded forests. Results also show that C-, X-band VOD and EVI provide complementary information to L-band VOD, especially in flooded forests and in mountains, indicating that synergistic approaches could lead to improved retrievals in these regions. Although the assessment of vegetation carbon in the densest forests requires further research, results from this study support the use of new L-band VOD estimates for mapping the carbon of tropical forests.Peer ReviewedPostprint (author's final draft