Tundra Snow Cover Properties from \u3cem\u3eIn-Situ\u3c/em\u3e Observation and Multi-Scale Passive Microwave Remote Sensing

Tundra snow cover is important to monitor as it influences local, regional, and global scale surface water balance, energy fluxes, and ecosystem and permafrost dynamics. Moreover, recent global circulation models (GCM) predict a pronounced shift in high latitude winter precipitation and mean annual air temperature due to the feedback between air temperature and snow extent. At regional and hemispheric scales, the estimation of snow extent, snow depth and, snow water equivalent (SWE) is important because high latitude snow cover both forces and reacts to atmospheric circulation patterns. Moreover, snow cover has implications on soil moisture dynamics, the depth, formation and growth of the permafrost active layer, the vegetation seasonality, and the respiration of C02. In Canada, daily snow depth observations are available from 1955 to present for most meteorological stations. Moreover, despite the abundance and dominance of a northern snow cover, most, if not all, long term snow monitoring sites are located south of 550N. Stations in high latitudes are extremely sparse and coastally biased. In Arctic regions, it can be logistically difficult and very expensive to acquire both spatially and temporally extensive in-situ snow data. Thus, the possibility of using satellite remote sensing to estimate snow cover properties is appealing for research in remote northern regions. Remote sensing techniques have been employed to monitor the snow since the 1960s when the visible light channels were used to map snow extent. Since then, satellite remote sensing has expanded to provide information on snow extent, depth, wetness, and SWE. However, the utility of satellite sensors to provide useful, operational tundra snow cover data depends on sensor parameters and data resolution. Passive microwave data are the only currently operational sources for providing estimates of dry snow extent, SWE and snow depth. Currently, no operational passive microwave algorithms exist for the spatially expansive tundra and high Arctic regions. The heterogeneity of sub-satellite grid tundra snow and terrain are the main limiting factors in using conventional SWE retrieval algorithm techniques. Moreover, there is a lack of in-situ data for algorithm development and testing. The overall objective of this research is to improve operational capabilities for estimating end of winter, pre-melt tundra SWE in a representative tundra study area using satellite passive microwave data. The study area for the project is located in the Daring-Exeter-Yamba portion of the Upper-Coppermine River Basin in the Northwest Territories. The size, orientation and boundaries of the study area were defined based on the satellite EASE grid (25 x 25 km) centroid located closest to the Tundra Ecosystem Research Station operated by the Government of the Northwest Territories. Data were collected during intensive late winter field campaigns in 2004, 2005, 2006, 2007, 2008, and 2009. During each field campaign, snow depth, density and stratigraphy were recorded at sites throughout the study area. During the 2005 and 2008 seasons, multi-scale airborne passive microwave radiometer data were also acquired. During the 2007 season, ground based passive microwave radiometer data were acquired. For each year, temporally coincident AMSR-E satellite Tb were obtained. The spatial distribution of snow depth, density and SWE in the study area is controlled by the interaction of blowing snow with terrain and land cover. Despite the spatial heterogeneity of snow cover, several inter-annual consistencies were identified. Tundra snow density is consistent when considered on a site-by-site basis and among different terrain types. A regional average density of 0.294 g/cm3 was derived from the six years of measurements. When applied to site snow depths, there is little difference in SWE derived from either the site or the regional average density. SWE is more variable from site to site and year to year than density which requires the use of a terrain based Classification to better quantify regional SWE. The variability in SWE was least on lakes and flat tundra, while greater on slopes and plateaus. Despite the variability, the interannual ratios of SWE among different terrain types does not change that much. The variability (CV) in among terrain categories was quite similar. The overall weighted mean CV for the study area was 0.40, which is a useful regional generalization. The terrain and landscape based classification scheme was used to generalize and extrapolate tundra SWE. Deriving a weighted mean SWE based on the spatial proportion of landscape and terrain features was shown as a method for generalizing the regional distribution of tundra SWE. The SWE data from each year were compared to AMSR-E satellite Tb. Within each season and among each of the seasons, there was little difference in 19 GHz Tb. However, there was always a large decrease in 37 GHz Tb from early November through April. The change in ΔTb37-19 throughout each season showed that the Tb at 37 GHz is sensitive to parameters which evolve over a winter season. A principal component analysis (PCA) showed that there are differences in ΔTb37-19 among different EASE grids and that land cover may have an influence on regional Tb. However, the PCA showed little relationship between end of season ΔTb37-19 and lake fraction. A good relationship was found between ΔTb37-19 and in-situ SWE. A quadratic function was fitted to explain 89 percent of the variance in SWE from the ΔTb37-19. The quadratic relationship provides a good fit between the data; however, the nature of the relationship is opposite to the expected linear relationship between ΔTb37-19 and SWE. Airborne Tb data were used to examine how different snow, land cover and terrain properties influence microwave emission. In flat tundra, there was a significant relationship between SWE and high resolution ΔTb37-19. On lakes and slopes, no strong relationships were found between SWE and high resolution ΔTb37-19. Due to the complexity of snow and terrain in high resolution footprints, it was a challenge to isolate a relationship between SWE and Tb. However, as the airborne footprint size increased the amplitude of variability in Tb decrease considerably to the point that Tb in large footprints is not sensitive to local scale variability in SWE. As such, most of the variability evident in the high and mid resolution airborne data will not persist at the EASE grid scale. Despite the many challenges, algorithm development should be possible at the satellite scale. The AMSR-E ΔTb37-19 changes from year to year in response to differences in snow cover properties. However, the multiple years of in-situ snow data remain the most important contribution in linking Tb with SWE

Spatial and Temporal Variation of Sea Ice Geophysical Properties and Microwave Remote Sensing Observations: The SIMS'90 Experiment

In this paper we present results from a sea ice field experiment conducted coincidentally with overflights of orbital and aerial remote sensing instrumentation in Resolute Passage and Barrow Strait, Northwest Territories, Canada. Our principal focus is to describe the spatial and temporal distribution of selected geophysical variables in the context of how microwave energy interacts with this seasonally varying snow-covered sea ice surface. Over the duration of the experiment, snow crystal size, structure, and snow volume salinities changed sufficiently to affect synthetic aperture radar (SAR) scattering; thermal profiles through the snow cover were diurnally driven; ice surface microscale roughness increased due to sublimation of water vapour from the snow pack onto the ice surface; and bulk ice surface; and bulk ice salinities did not change. Results from the SAR data analysis indicate that the geophysical structure of multiyear ice created a larger and more rapid change in the seasonal SAR scattering signature than did the structure for early consolidated smooth first-year ice. These results are considered fundamental to measurement and monitoring of the seasonal evolution of the snow-covered arctic sea ice surface using SAR remote sensing.Key words: snow, sea ice, synthetic aperture radar, seasonal evolution, remote sensingRÉSUMÉ. On présente dans cet article les résultats d’expériences sur le terrain portant sur la glace marine, menées paralltAernent à des survols d’appareils de télédétection en orbite ou aéroportés, dans la baie Resolute et le détroit de Barrow (Territoires du Nord-Ouest). Notre objectif principalest de décrire la distribution spatiale et temporelle de variables géophysiques choisies, en considérant la façon dont l’énergie micro-onde réagit avec la surface de glace marine couverte de neige et qui varie avec les saisons. Pendant la durée des expériences, la taille des cristaux de neige, leur structure et la salinité du volume nival ont changé suffisamment pour influer sur la diffusion du radar à antenne synthétique (RAAS); les profils thermiques à travers le couvert nival suivaient un rythme diurne; la rugosité à petite échelle de la surface de la glace augmentait par suite de la sublimation de la vapeur d’eau venant de la neige qui y était accumulée; et la salinité de la masse de glace n’était pas modifiée. Les résultats de l’analyse des données recueillies avec le RAAS montrent que la structure géophysique de la glace de plusieurs années créait un changement plus important et plus rapide dans la signature saisonnière de la diffusion du RAAS, que ne le faisait la structure de la glace lisse de l’année récemment consolidée. On pense que ces résultats sont très importants pour les mesures et la surveillance, à l’aide de la télédétection au RAAS, de l’évolution saisonnière de la surface de la glace marine arctique recouverte de neige.Mots clés: neige, glace marine, radar à antenne synthétique, évolution saisonnière. télédétectio

SMOS based high resolution soil moisture estimates for Desert locust preventive management

This paper presents the first attempt to include soil moisture information from remote sensing in the tools available to desert locust managers. The soil moisture requirements were first assessed with the users. The main objectives of this paper are: i) to describe and validate the algorithms used to produce a soil moisture dataset at 1 km resolution relevant to desert locust management based on DisPATCh methodology applied to SMOS and ii) the development of an innovative approach to derive high-resolution (100 m) soil moisture products from Sentinel-1 in synergy with SMOS data. For the purpose of soil moisture validation, 4 soil moisture stations where installed in desert areas (one in each user country). The soil moisture 1 km product was thoroughly validated and its accuracy is amongst the best available soil moisture products. Current comparison with in-situ soil moisture stations shows good values of correlation (R>0.7R>0.7) and low RMSE (below 0.04 m3 m−3). The low number of acquisitions on wet dates has limited the development of the soil moisture 100 m product over the Users Areas. The Soil Moisture product at 1 km will be integrated into the national and global Desert Locust early warning systems in national locust centres and at DLIS-FAO, respectively

Global evaluation of SMAP/Sentinel-1 soil moisture products

MAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.Peer ReviewedPostprint (published version

HUMAN AND CLIMATE IMPACTS ON FLOODING VIA REMOTE SENSING, BIG DATA ANALYTICS, AND MODELING

Over the last 20 years, the amount of streamflow has greatly increased and spring snowmelt floods have occurred more frequently in the north-central U.S. In the Red River of the North Basin (RRB) overlying portions of North Dakota and Minnesota, six of the 13 major floods over the past 100 years have occurred since the late 1990s. Based on numerous previous studies as well as senior flood forecasters’ experiences, recent hydrological changes related to human modifications [e.g. artificial subsurface drainage (SSD) expansion] and climate change are potential causes of notable forecasting failures over the past decade. My dissertation focuses on the operational and scientific gaps in current forecasting models and observational data and provides insights and value to both the practitioner and the research community. First, the current flood forecasting model needs both the location and installation timing of SSD and SSD physics. SSD maps were developed using satellite “big” data and a machine learning technique. Next, using the maps with a land surface model, the impacts of SSD expansion on regional hydrological changes were quantified. In combination with model physics, the inherent uncertainty in the airborne gamma snow survey observations hinders the accurate flood forecasting model. The operational airborne gamma snow water equivalent (SWE) measurements were improved by updating antecedent surface moisture conditions using satellite observations on soil moisture. From a long-term perspective, flood forecasters and state governments need knowledge of historical changes in snowpack and snowmelt to help flood management and to develop strategies to adapt to climate changes. However, historical snowmelt trends have not been quantified in the north-central U.S. due to the limited historical snow data. To overcome this, the current available historical long-term SWE products were evaluated across diverse regions and conditions. Using the most reliable SWE product, a trend analysis quantified the magnitude of change extreme snowpack and melt events over the past 36 years. Collectively, this body of research demonstrates that human and climate impacts, as well as limited and noisy data, cause uncertainties in flood prediction in the great plains, but integrated approaches using remote sensing, big data analytics, and modeling can quantify the hydrological changes and reduce the uncertainties. This dissertation improves the practice of flood forecasting in Red River of the North Basin and advances research in hydrology and snow science

Spatiotemporal analyses of soil moisture from point to footprint scale in two different hydroclimatic regions

This paper presents time stability analyses of soil moisture at different spatial measurement support scales (point scale and airborne remote sensing (RS) footprint scale 800 m × 800 m) in two different hydroclimatic regions. The data used in the analyses consist of in situ and passive microwave remotely sensed soil moisture data from the Southern Great Plains Hydrology Experiments 1997 and 1999 (SGP97 and SGP99) conducted in the Little Washita (LW) watershed, Oklahoma, and the Soil Moisture Experiments 2002 and 2005 (SMEX02 and SMEX05) in the Walnut Creek (WC) watershed, Iowa. Results show that in both the regions soil properties (i.e., percent silt, percent sand, and soil texture) and topography (elevation and slope) are significant physical controls jointly affecting the spatiotemporal evolution and time stability of soil moisture at both point and footprint scales. In Iowa, using point‐scale soil moisture measurements, the WC11 field was found to be more time stable (TS) than the WC12 field. The common TS points using data across the 3 year period (2002–2005) were mostly located at moderate to high elevations in both the fields. Furthermore, the soil texture at these locations consists of either loam or clay loam soil. Drainage features and cropping practices also affected the field‐scale soil moisture variability in the WC fields. In Oklahoma, the field having a flat topography (LW21) showed the worst TS features compared to the fields having gently rolling topography (LW03 and LW13). The LW13 field (silt loam) exhibited better time stability than the LW03 field (sandy loam) and the LW21 field (silt loam). At the RS footprint scale, in Iowa, the analysis of variance (ANOVA) tests show that the percent clay and percent sand are better able to discern the TS features of the footprints compared to the soil texture. The best soil indicator of soil moisture time stability is the loam soil texture. Furthermore, the hilltops (slope ∼0%–0.45%) exhibited the best TS characteristics in Iowa. On the other hand, in Oklahoma, ANOVA results show that the footprints with sandy loam and loam soil texture are better indicators of the time stability phenomena. In terms of the hillslope position, footprints with mild slope (0.93%–1.85%) are the best indicators of TS footprints. Also, at both point and footprint scales in both the regions, land use–land cover type does not influence soil moisture time stability

On the Links between Microwave and Solar Wavelength Interactions with Snow-Covered First-Year Sea Ice

Electromagnetic (EM) energy at solar and microwavelengths will interact with a snow-covered sea ice volume as a function of its geophysical properties. The seasonal metamorphosis of the snow cover modulates the relative distribution of the three main interaction mechanisms of EM energy: reflection, transmission, and absorption. We use a combination of modeling and observational data to illustrate how the total relative scattering cross section (Sigma 0) at microwavelengths can be used to estimate the surface climatological shortwave albedo and the transmitted Photosynthetically Active Radiation (PAR) for a snow-covered, first-year sea ice volume typical of the Canadian Arctic. Modeling results indicate that both 5.3 and 9.25 GHz frequencies, at HH polarization and incidence angles of 20 degrees, 30 degrees, and 40 degrees can be used to estimate the daily averaged integrated climatological albedo (Alpha). The models at 5.3 GHz, HH polarization, at 20 degree, 30 degree, and 40 degree incidence angles were equally precise in predications of Alpha. The models at 9.25 GHz were slightly less precise, particularly at the 40 degree incidence angle. The reduction in precision at the 40 degree incidence angle was attributed to the increased sensitivity at both 5.3 and 9.25 GHz to the snow surface scattering term (Sigma 0 ss) used in computation of the total relative scattering cross section (Sigma 0). Prediction of subsnow PAR was also possible using the same combination of microwave sensor variables utilized in prediction of Alpha, but because subice algal communities have evolved to be low light sensitive, the majority of the growth cycle occurs prior to significant changes in Sigma 0. A method of remote estimation of snow thickness is required to be scientifically useful. Observational data from the European ERS-1 SAR were used to confirm the appropriateness of the modeled relationships between Sigma 0, Alpha, and PAR. Over a time series spanning all conditions used in the modeled relationships, the same general patterns were observed between Sigma, Alpha, and PAR.Key words: microwave scattering models, snow, sea ice, climatological shortwave radiation, photosynthetically active radiation, microwave remote sensingL'énergie électromagnétique à des ondes ultra-courtes et solaires va interagir avec un volume de glace de mer couverte de neige, en fonction de ses propriétés géophysiques. La métamorphose saisonnière du couvert nival module la distribution relative des trois grands mécanismes d'interaction de l'énergie électromagnétique: réflexion, transmission et absorption. On utilise une combinaison de résultats de modélisation et de données d'observation pour illustrer la façon dont la coupe transversale totale de diffusion relative (sigma-zero) à des longueurs d'onde ultra-courtes peut être utilisée pour estimer l'albédo climatologique en ondes courtes de la surface et le rayonnement photosynthétiquement utilisable (RPU) pour un volume de glace de mer nouvelle couverte de neige, typique de l'Arctique canadien. Les résultats de modélisation indiquent qu'on peut utiliser les deux fréquences de 5,3 et 9,25 GHz, ayant une polarisation HH et des angles d'incidence de 20, 30 et 40° pour estimer la moyenne quotidienne de l'albédo climatologique intégré (alpha). Les modèles à 5,3 GHz, ayant une polarisation HH et des angles d'incidence de 20, 30 et 40° prédisaient alpha avec le même degré de précision. Les modèles à 9,25 GHz étaient légèrement moins précis, surtout en ce qui concerne l'angle d'incidence de 40°. La réduction de précision à l'angle d'incidence de 40° était attribuée à une augmentation de sensibilité, aux deux fréquences de 5,3 et 9,25 GHz, au terme de diffusion de la surface nivale (sigma-zero-ss) utilisé dans le calcul de la coupe transversale totale de diffusion relative (sigma-zero). Pour prédire le RPU sous la couche nivale, on a également pu utiliser la même combinaison de variables de capteurs d'ondes ultra-courtes que celle utilisée pour prédire alpha. Mais parce que les communautés d'algues vivant sous la glace ont développé un niveau de photosensibilité élevé, la plupart du cycle de croissance se produit avant que des changements importants n'aient lieu dans sigma-zero. Il faut développer une méthode d'estimation de l'épaisseur nivale par la télédétection pour que cette méthode soit utilisable du point de vue scientifique. On a utilisé des données d'observation prises au RALS dans le cadre du ERS-1 européen pour confirmer la pertinence des rapports de modélisation entre sigma-zero, alpha et le RPU. Dans une série chronologique couvrant toutes les conditions utilisées dans les rapports de modélisation, on a observé les mêmes grandes tendances entre sigma-zero, alpha et le RPU.Mots clés: modèles de diffusion d’hyperfréquences, neige, glace de mer, rayonnement climatologique de courtes longueurs d’onde, rayonnement photosynthétiquement utilisable, télédétection des ondes ultra-courte

Estimating wheat yield: an approach for estimating number of grains using cross-polarised ENVISAT-1 ASAR data

In this paper an attempt to model wheat yield is made by exploiting characteristic interaction of cross-polarised SAR with wheat crop. SAR backscatter from a crop field is affected by the density, structure, volume and the moisture content of various components of plant (viz. head, stem, leaf) alongwith soil moisture. Hence, to effectively handle the influence of each of these components of the plant on SAR backscatter, a plant parameter, termed as Interaction Factor (IF) is conceptualised by combining volume, moisture, height for each of the component and density of plant. For this purpose, detailed experiment over farmers' fields was carried out in synchrony with SAR acquisition involving in-depth measurements on volume, moisture content and height of various components of wheat plant, number of grains, plant density and soil moisture. Stepwise regression analysis revealed that IFHead significantly affects the shallow incidence angle, cross-polarised C-band SAR backscatter. IFHead is also highly correlated to the number of grains. This is attributed to the fact that parameters of the wheat head from which IFHead is calculated, namely moisture, volume and height, determine eventual number of grains. The study offers an approach for estimating wheat yield by retrieving number of grains from shallow incidence angle cross-polarised SAR data

Potential of Spaceborne X & L-Band SAR-Data for Soil Moisture Mapping Using GIS and its Application to Hydrological Modelling: the Example of Gottleuba Catchment, Saxony / Germany

Hydrological modelling is a powerful tool for hydrologists and engineers involved in the planning and development of integrated approach for the management of water resources. With the recent advent of computational power and the growing availability of spatial data, RS and GIS technologies can augment to a great extent the conventional methods used in rainfall runoff studies; it is possible to accurately describe watershed characteristics in particularly when determining runoff response to rainfall input. The main objective of this study is to apply the potential of spaceborne SAR data for soil moisture retrieval in order to improve the spatial input parameters required for hydrological modelling. For the spatial database creation, high resolution 2 m aerial laser scanning Digital Terrain Model (DTM), soil map, and landuse map were used. Rainfall records were transformed into a runoff through hydrological parameterisation of the watershed and the river network using HEC-HMS software for rainfall runoff simulation. The Soil Conservation Services Curve Number (SCS-CN) and Soil Moisture Accounting (SMA) loss methods were selected to calculate the infiltration losses. In microwave remote sensing, the study of how the microwave interacts with the earth terrain has always been interesting in interpreting the satellite SAR images. In this research soil moisture was derived from two different types of Spaceborne SAR data; TerraSAR-X and ALOS PALSAR (L band). The developed integrated hydrological model was applied to the test site of the Gottleuba Catchment area which covers approximately 400 sqkm, located south of Pirna (Saxony, Germany). To validate the model historical precipitation data of the past ten years were performed. The validated model was further optimized using the extracted soil moisture from SAR data. The simulation results showed a reasonable match between the simulated and the observed hydrographs. Quantitatively the study concluded that based on SAR data, the model could be used as an expeditious tool of soil moisture mapping which required for hydrological modelling

Satellite Microwave Remote Sensing of Boreal-Arctic Land Surface State and Meteorology from AMSR-E

High latitude regions are undergoing significant climate-related change and represent an integral component of the Earth’s climate system. Near-surface vapor pressure deficit, soil temperature, and soil moisture are essential state variables for monitoring high latitude climate and estimating the response of terrestrial ecosystems to climate change. Methods are developed and evaluated to retrieve surface soil temperature, daily maximum/minimum air temperature, and land surface wetness information from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite for eight Boreal forest and Arctic tundra biophysical monitoring sites across Alaska and northern Canada. Daily vapor pressure deficit is determined by employing AMSR-E daily maximum/minimum air temperature retrievals. The seasonal pattern of microwave emission and relative accuracy of the estimated land surface state are influenced strongly by landscape properties including the presence of open water, vegetation type and seasonal phenology, snow cover and freeze-thaw transitions. Daily maximum/minimum air temperature is retrieved with RMSEs of 2.88 K and 2.31 K, respectively. Soil temperature is retrieved with RMSE of 3.1 K. Vapor pressure deficit (VPD) is retrieved to within 427.9 Pa using thermal information from AMSR-E. AMSR-E thermal information imparted 27% of the overall error in VPD estimation with the remaining error attributable to underlying algorithm assumptions. Land surface wetness information derived from AMSR-E corresponded with soil moisture observations and simple soil moisture models at locations with tundra, grassland, and mixed -forest/cropland land covers (r = 0.49 to r = 0.76). AMSR-E 6.9 GHz land surface wetness showed little correspondence to soil moisture observation or model estimates at locations with \u3e 20% open water and \u3e 5 m2 m-2 Leaf Area Index, despite efforts to remove the impact of open water and vegetation biomass. Additional information on open water fraction and vegetation phenology derived from AMSR-E 6.9 GHz corresponds well with independent satellite observations from MODIS, Sea-Winds, and JERS-1. The techniques and interpretations of high-latitude terrestrial brightness temperature signatures presented in this investigation will likely prove useful for future passive microwave missions and ecosystem modeling