Widespread occurrence of anomalous C-band backscatter signals in arid environments caused by subsurface scattering

Backscatter measured by scatterometers and Synthetic Aperture Radars is sensitive to the dielectric properties of the soil and normally increases with increasing soil moisture content. However, when the soil is dry, the radar waves penetrate deeper into the soil, potentially sensing subsurface scatterers such as near-surface rocks and stones. In this paper we propose an exponential model to describe the impact of such subsurface scatterers on C-Band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on board of the METOP satellites. The model predicts an increase of the subsurface scattering contributions with decreasing soil wetness that may counteract the signal from the soil surface. This may cause anomalous backscatter signals that deteriorate soil moisture retrievals from ASCAT. We test whether this new model is able to explain ASCAT observations better than a bare soil backscatter model without a subsurface scattering term, using k-fold cross validation and the Bayesian Information Criterion for model selection. We find that arid landscapes with Leptosols and Arenosols represent ideal environmental conditions for the occurrence of subsurface scattering. Nonetheless, subsurface scattering may also become important in more humid environments during dry spells. We conclude that subsurface scattering is a widespread phenomenon that (i) needs to be accounted for in active microwave soil moisture retrievals and (ii) has a potential for soil mapping, particularly in arid and semi-arid environments

Determination of the freeze/thaw surface state from ERS-2 backscatter data

Zusammenfassung in deutscher SpracheEtwa zwei Drittel der globalen Landmassen erleben einen jährlichen Frost/Tau-Zyklus. Dieser bestimmt nicht nur den Beginn und die Dauer der Vegetations- und Anbauperiode, sondern hat auch eine große Wirkung auf den CO2-Austausch zwischen Land und Atmosphäre. Zahlreiche Anwendungen wie Klimastudien und Permafrostüberwachungen könnten ohne Kenntnis des Frost/Tau-Zyklus nicht durchgeführt werden. In der Forschungsgruppe Fernerkundung der TU Wien werden Methoden entwickelt, den Frost/Tau-Zyklus aus Satellitenbeobachtungen des Rückstreuverhaltens der Erdoberfläche abzuleiten. Der Algorithmus, der ursprünglich für Messungen des Advanced Scatterometer (ASCAT) entwickelt wurde, liefert Ergebnisse von 2007 bis heute. Allerdings sind für geowissenschaftliche Studien möglichst lange Zeitserien wünschenswert. Ziel dieser Arbeit war die Adaptierung des Algorithmus für Daten der Scatterometer (ESCAT) der European Remote Sensing (ERS) Satelliten, um die Verfügbarkeit der Frost/Tau-Zeitserien in die Vergangenheit zu verlängern. Um plausible Ergebnisse zu liefern, benötigt der Algorithmus ausreichend Messungen auf der ganzen kontinentalen Erdoberfläche unter möglichst vielen verschiedenen Frost/Tau-Zuständen. Der größte Unsicherheitsfaktor hierbei war deshalb die verglichen mit ASCAT geringere Anzahl an Beobachtungen, bedingt durch die Geometrie der ESCAT-Scatterometer und technische Probleme während der Mission. Für den Vergleich der Ergebnisse der Frost/Tau-Zustandsbestimmung von ESCAT- mit jenen von ASCAT-Daten wurden verschiedene Klima- und Vegetationsregionen ausgewählt. Der Vergleich liefert sehr positive Ergebnisse. Zur weiteren Beurteilung der Ergebnisse wurden die Frost/Tau-Zeitserien mit Boden- und Oberflächentemperaturmodellen des Global Land Data Assimilation System (GLDAS), Temperaturmessungen an Beobachtungsstationen des International Soil Moisture Network (ISMN) sowie dem Frost/Tau-Datensatz des National Snow and Ice Data Center (NSIDC) validiert. Die Ergebnisse zeigen eine hohe Übereinstimmung der ESCAT Zeitserien mit allen Datensätzen.The freeze/thaw cycle of the Earth's surface determines the timing and the length of the vegetation growing season and has a high impact on the land-atmosphere carbon dioxide exchange. For applications like permafrost monitoring and climate studies, information on the freeze/thaw state of the surface is highly valuable. The Remote Sensing research group at TU Wien has developed methods to retrieve global freeze/thaw states of the Earth's surface from backscatter measurements obtained from microwave scatterometers. The algorithm for the retrieval of the surface state was originally developed for data from the Advanced Scatterometer (ASCAT), covering the period from 2007 until present. Since geoscientific studies require data from different periods, it's desirable to have long time series available. The aim of this thesis was to investigate if the ASCAT surface state algorithm can also be applied on data from the scatterometer (ESCAT) on-board the European Remote Sensing (ERS) satellites in order to obtain prolonged freeze/thaw time series, despite the lower amount of available observations because of limitations in the observation geometry and technical problems during the mission. The algorithm requires a certain amount of observations under different conditions in order to derive a surface state, which made the data availability the largest factor of uncertainty when starting the work on the algorithm adaptation. Different climate and land cover regions were selected to compare the ESCAT surface state flags with those retrieved from ASCAT backscatter data. The overall outcome shows very satisfying results, contradicting the expectation that the low data availability might prevent a successful determination of the surface state from ESCAT data. Furthermore, the ESCAT surface state flags were validated against soil and surface temperature data from the Global Land Data Assimilation System (GLDAS) and in-situ networks, as well as against arctic freeze/thaw soil state from the National Snow and Ice Data Center (NSIDC). All validations show very good coherence between the datasets.7

Comparison of Long Short-Term Memory Networks and Random Forest for Sentinel-1 Time Series Based Large Scale Crop Classification

To ensure future food security, improved agricultural management approaches are required. For many of those applications, precise knowledge of the distribution of crop types is essential. Various machine and deep learning models have been used for automated crop classification using microwave remote sensing time series. However, the application of these approaches on a large spatial and temporal scale is barely investigated. In this study, the performance of two frequently used algorithms, Long Short-Term Memory (LSTM) networks and Random Forest (RF), for crop classification based on Sentinel-1 time series and meteorological data on a large spatial and temporal scale is assessed. For data from Austria, the Netherlands, and France and the years 2015–2019, scenarios with different spatial and temporal scales were defined. To quantify the complexity of these scenarios, the Fisher Discriminant measurement F1 (FDR1) was used. The results demonstrate that both classifiers achieve similar results for simple classification tasks with low FDR1 values. With increasing FDR1 values, however, LSTM networks outperform RF. This suggests that the ability of LSTM networks to learn long-term dependencies and identify the relation between radar time series and meteorological data becomes increasingly important for more complex applications. Thus, the study underlines the importance of deep learning models, including LSTM networks, for large-scale applications

Improving the Seasonal Representation of ASCAT Soil Moisture and Vegetation Dynamics in a Temperate Climate

Previous validation studies have demonstrated the accuracy of the Metop-A ASCAT soil moisture (SM) product, although over- and underestimation during different seasons of the year suggest a need for improving the retrieval algorithm. In this study, we analyzed whether adapting the vegetation characterization based on global parameters to regional conditions improves the seasonal representation of SM and vegetation optical depth ( τ ). SM and τ are retrieved from ASCAT using both a seasonal (mean climatological) and a dynamic vegetation characterization that allows for year-to-year changes. The retrieved SM and τ are compared with in situ and satellite SM, and with vegetation products (SMAP, AMSR2, and SPOT-VGT/PROBA-V). The study region is set in an agricultural area of Lower Austria that is characterized by heterogeneous land cover and topography, and features an experimental catchment equipped with a SM network (HOAL SoilNet). We found that a stronger vegetation correction within the SM retrieval improves the SM product considerably (increase of the Spearman correlation coefficient r s by 0.15 on average, and r s comparable to SMAP and AMSR2). The vegetation product derived with a dynamic vegetation characterization compares well to the reference datasets and reflects vegetation dynamics such as start and peak of season and harvest. Although some vegetation effects cannot be corrected by the adapted vegetation characterization, our results demonstrate the benefits of a parameterization optimized for regional conditions in this temperate climate zone

Sensitivity of Sentinel-1 Backscatter to Vegetation Dynamics: An Austrian Case Study

Crop monitoring is of great importance for e.g., yield prediction and increasing water use efficiency. The Copernicus Sentinel-1 mission operated by the European Space Agency provides the opportunity to monitor Earth’s surface using radar at high spatial and temporal resolution. Sentinel-1’s Synthetic Aperture Radar provides co- and cross-polarized backscatter, enabling the calculation of microwave indices. In this study, we assess the potential of Sentinel-1 VV and VH backscatter and their ratio VH/VV, the cross ratio (CR), to monitor crop conditions. A quantitative assessment is provided based on in situ reference data of vegetation variables for different crops under varying meteorological conditions. Vegetation Water Content (VWC), biomass, Leaf Area Index (LAI) and height are measured in situ for oilseed-rape, corn and winter cereals at different fields during two growing seasons. To quantify the sensitivity of backscatter and microwave indices to vegetation dynamics, linear and exponential models and machine learning methods have been applied to the Sentinel-1 data and in situ measurements. Using an exponential model, the CR can account for 87% and 63% of the variability in VWC for corn and winter cereals. In oilseed-rape, the coefficient of determination ( R 2 ) is lower ( R 2 = 0.34) due to the large difference in VWC between the two growing seasons and changes in vegetation structure that affect backscatter. Findings from the Random Forest analysis, which uses backscatter, microwave indices and soil moisture as input variables, show that CR is by and large the most important variable to estimate VWC. This study demonstrates, based on a quantitative analysis, the large potential of microwave indices for vegetation monitoring of VWC and phenology

A stand-alone remote sensing approach based on the use of the optical trapezoid model for detecting the irrigated areas

Under the current water scarcity scenario, the promotion of water saving strategies is essential for improving the sustainability of the irrigated agriculture. In particular, high resolution irrigated area maps are required for better understanding water uses and supporting water management authorities. The main purpose of this study was to provide a stand-alone remote sensing (RS) methodology for mapping irrigated areas. Specifically, an unsupervised classification approach on Normalized Difference Vegetation Index (NDVI) data was coupled with the OPtical TRApezoid Model (OPTRAM) for detecting actual irrigated areas without the use of any reference data. The proposed methodology was firstly applied and validated at the Marchfeld Cropland region (Austria) during the irrigation season 2021, showing a good agreement with an overall accuracy of 70%. Secondly, it was applied at the irrigation district Quota 102,50 (Italy) for the irrigation seasons 2019–2020. The results of the latter were instead compared with the data declared by the Reclamation Consortium, finding an overestimation of irrigated areas of 21%. In conclusion, this study suggests an easy-to-use approach, eventually independent of reference data such as agricultural statistical surveys or records and replicable under different agricultural settings in continental or Mediterranean climates to support stakeholders for regular estimation of irrigated areas in different growing years or detecting eventual unauthorized water uses. However, some uncertainties should be considered, needing further analyses for improving the accuracy of the proposed approach.This study was supported by the Research Project of National Relevance (PRIN 2017) entitled “INtegrated Computer modeling and monitoring for Irrigation Planning in Italy - INCIPIT” and by the research project “Strategie per migliorare l’efficienza d’uso dell’acqua per le colture mediterranee” (SaveIrriWater) Linea 2 Ricerca di Ateneo 2020–22 (Università degli Studi di Catania).Peer reviewe

Incorporating Advanced Scatterometer Surface and Root Zone Soil Moisture Products into the Calibration of a Conceptual Semi-Distributed Hydrological Model

The role of soil moisture is widely accepted as a significant factor in the mass and energy balance of catchments as a controller in surface and subsurface runoff generation. The paper examines the potential of a new dataset based on advanced scatterometer satellite remote sensing of soil moisture (ASCAT) for multiple objective calibrations of a dual-layer, conceptual, semi-distributed hydrological model. The surface and root zone soil moisture indexes based on ASCAT data were implemented into calibration of the hydrological model. Improvements not only in the instrument specifications, i.e., better temporal and spatial sampling, but also in the higher radiometric accuracy and retrieval algorithm, were applied. The analysis was performed in 209 catchments situated in different physiographic and climate zones of Austria for the period 2007–2018. We validated the model for two validation periods. The results show that multiple objective calibrations have a substantial positive effect on constraining the model parameters. The combined use of soil moisture and discharges in the calibration improved the soil moisture simulation in more than 73% of the catchments, except for the catchments with higher forest cover percentages. Improvements also occurred in the runoff model efficiency, in more than 27% of the catchments, mostly in the watersheds with a lower mean elevation and a higher proportion of farming land use, as well as in the Alpine catchments where the runoff is not significantly influenced by snowmelt and glacier runoff

Sentinel-1 cross ratio and vegetation optical depth: A comparison over Europe

Vegetation products based on microwave remote sensing observations, such as Vegetation Optical Depth (VOD), are increasingly used in a variety of applications. One disadvantage is the often coarse spatial resolution of tens of kilometers of products retrieved from microwave observations from spaceborne radiometers and scatterometers. This can potentially be overcome by using new high-resolution Synthetic Aperture Radar (SAR) observations from Sentinel-1. However, the sensitivity of Sentinel-1 backscatter to vegetation dynamics, or its use in radiative transfer models, such as the water cloud model, has only been tested at field to regional scale. In this study, we compared the cross-polarization ratio (CR) to vegetation dynamics as observed in microwave-based Vegetation Optical Depth from coarse-scale satellites over Europe. CR was obtained from Sentinel-1 VH and VV backscatter observations at 500 m sampling and resampled to the spatial resolution of VOD from the Advanced SCATterometer (ASCAT) on-board the Metop satellite series. Spatial patterns between median CR and ASCAT VOD correspond to each other and to vegetation patterns over Europe. Analysis of temporal correlation between CR and ASCAT VOD shows that high Pearson correlation coefficients (Rp) are found over croplands and grasslands (median Rp > 0.75). Over deciduous broadleaf forests, negative correlations are found. This is attributed to the effect of structural changes in the vegetation canopy which affect CR and ASCAT VOD in different ways. Additional analysis comparing CR to passive microwave-based VOD shows similar effects in deciduous broadleaf forests and high correlations over crop-and grasslands. Though the relationship between CR and VOD over deciduous forests is unclear, results suggest that CR is useful for monitoring vegetation dynamics over crop-and grassland and a potential path to high-resolution VOD.Water Resource

Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges

Agricultural droughts are extreme events which are often a result of interplays between multiple hydro-meteorological processes. Therefore, assessing drought occurrence, extent, duration and intensity is complex and requires the combined use of multiple variables, such as temperature, rainfall, soil moisture (SM) and vegetation state. The benefit of using information on SM and vegetation state is that they integrate information on precipitation, temperature and evapotranspiration, making them direct indicators of plant available water and vegetation productivity. Microwave remote sensing enables the retrieval of both SM and vegetation information, and satellite-based SM and vegetation products are available operationally and free of charge on a regional or global scale and daily basis. As a result, microwave remote sensing products play an increasingly important role in drought monitoring applications. Here, we provide an overview of recent developments in using microwave remote sensing for large-scale agricultural drought monitoring. We focus on the intricacy of monitoring the complex process of drought development using multiple variables. First, we give a brief introduction on fundamental concepts of microwave remote sensing together with an overview of recent research, development and applications of drought indicators derived from microwave-based satellite SM and vegetation observations. This is followed by a more detailed overview of the current research gaps and challenges in combining microwave-based SM and vegetation measurements with hydro-meteorological data sets. The potential of using microwave remote sensing for drought monitoring is demonstrated through a case study over Senegal using multiple satellite- and model-based data sets on rainfall, SM, vegetation and combinations thereof. The case study demonstrates the added-value of microwave-based SM and vegetation observations for drought monitoring applications. Finally, we provide an outlook on potential developments and opportunities.Mathematical Geodesy and Positionin