Using Cosmic-Ray Neutron Probes to Monitor Landscape Scale Soil Water Content in Mixed Land Use Agricultural Systems

With an ever-increasing demand for natural resources and the societal need to understand and predict natural disasters, soil water content (SWC) observations remain a critical variable to monitor in order to optimally allocate resources, establish early warning systems, and improve weather forecasts.However, routine agricultural production practices of soil cultivation, planting, and harvest make the operation andmaintenance of direct contact point sensors for long-termmonitoring challenging. In this work, we explore the use of the newly established Cosmic-Ray Neutron Probe (CRNP) and method to monitor landscape average SWC in a mixed agricultural land use systemin northeastAustria.Thecalibrated CRNP landscape SWC values compare well against an independent in situ SWC probe network (MAE = 0.0286m3/m3) given the challenge of continuous in situ monitoring from probes across a heterogeneous agricultural landscape. The ability of the CRNP to provide real-time and accurate landscape SWC measurements makes it an ideal method for establishing long-term monitoring sites in agricultural ecosystems to aid in agricultural water and nutrient management decisions at the small tract of land scale as well as aiding in management decisions at larger scales

A carbon sink-driven approach to estimate gross primary production from microwave satellite observations

Global estimation of Gross Primary Production (GPP) - the uptake of atmospheric carbon dioxide by plants through photosynthesis - is commonly based on optical satellite remote sensing data. This presents a source-driven approach since it uses the amount of absorbed light, the main driver of photosynthesis, as a proxy for GPP. Vegetation Optical Depth (VOD) estimates obtained from microwave sensors provide an alternative and independent data source to estimate GPP on a global scale, which may complement existing GPP products. Recent studies have shown that VOD is related to aboveground biomass, and that both VOD and temporal changes in VOD relate to GPP. In this study, we build upon this concept and propose a model for estimating GPP from VOD. Since the model is driven by vegetation biomass, as observed through VOD, it presents a carbon sink-driven approach to quantify GPP and, therefore, is conceptually different from common source-driven approaches. The model developed in this study uses single frequencies from active or passive microwave VOD retrievals from C-, X- and Ku-band (Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E)) to estimate GPP at the global scale. We assessed the ability for temporal and spatial extrapolation of the model using global GPP from FLUXCOM and in situ GPP from FLUXNET. We further performed upscaling of in situ GPP based on different VOD data sets and compared these estimates with the FLUXCOM and MODerate-resolution Imaging Spectroradiometer (MODIS) GPP products. Our results show that the model developed for individual grid cells using VOD and change in VOD as input performs well in predicting temporal patterns in GPP for all VOD data sets. For spatial extrapolation of the model, however, additional input variables are needed to represent the spatial variability of the VOD-GPP relationship due to differences in vegetation type. As additional input variable, we included the grid cell median VOD (as a proxy for vegetation cover), which increased the model performance during cross validation. Mean annual GPP obtained for AMSR-E X-band data tends to overestimate mean annual GPP for FLUXCOM and MODIS but shows comparable latitudinal patterns. Overall, our findings demonstrate the potential of VOD for estimating GPP. The sink-driven approach provides additional information about GPP independent of optical data, which may contribute to our knowledge about the carbon source-sink balance in different ecosystems

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

Assessing the relationship between microwave vegetation optical depth and gross primary production

At the global scale, the uptake of atmospheric carbon dioxide by terrestrial ecosystems through photosynthesis is commonly estimated through vegetation indices or biophysical properties derived from optical remote sensing data. Microwave observations of vegetated areas are sensitive to different components of the vegetation layer than observations in the optical domain and may therefore provide complementary information on the vegetation state, which may be used in the estimation of Gross Primary Production (GPP). However, the relation between GPP and Vegetation Optical Depth (VOD), a biophysical quantity derived from microwave observations, is not yet known. This study aims to explore the relationship between VOD and GPP. VOD data were taken from different frequencies (L-, C-, and X-band) and from both active and passive microwave sensors, including the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS) mission, the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E) and a merged VOD data set from various passive microwave sensors. VOD data were compared against FLUXCOM GPP and Solar-Induced chlorophyll Fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). FLUXCOM GPP estimates are based on the upscaling of flux tower GPP observations using optical satellite data, while SIF observations present a measure of photosynthetic activity and are often used as a proxy for GPP. For relating VOD to GPP, three variables were analyzed: original VOD time series, temporal changes in VOD (ΔVOD), and positive changes in VOD (ΔVOD≥0). Results show widespread positive correlations between VOD and GPP with some negative correlations mainly occurring in dry and wet regions for active and passive VOD, respectively. Correlations between VOD and GPP were similar or higher than between VOD and SIF. When comparing the three variables for relating VOD to GPP, correlations with GPP were higher for the original VOD time series than for ΔVOD or ΔVOD≥0 in case of sparsely to moderately vegetated areas and evergreen forests, while the opposite was true for deciduous forests. Results suggest that original VOD time series should be used jointly with changes in VOD for the estimation of GPP across biomes, which may further benefit from combining active and passive VOD data

Bewertung von Vegetationsdynamik mittels weltraumgestützter aktiver Mikrowellenrückstreuungs-Beobachtungen

Abweichender Titel nach Übersetzung der Verfasserin/des VerfassersMicrowave observations of the Earths surface are sensitive to various environmental variables, including the water content in the soil and vegetation. Since vegetation and soil moisture influence the global carbon-, energy-, and hydrological cycle, their monitoring and mapping is pivotal to provide accurate input in global circulation and climate models. The advantage of microwave remote sensing, compared to well known visible near-infrared (VNIR) remote sensing, is that it is not impeded by cloud cover or dependent on solar illumination. For vegetation monitoring vegetation, optical depth (t) is often used, which is an attenuation parameter in the microwave domain and is related to the water content of the vegetation. So far t has mainly been retrieved from passive microwave observations using radiative transfer models. However, long-term active microwave observations are available from a series of scatterometers, which were originally developed for monitoring ocean winds, making them valuable instruments for monitoring land surface parameters. Although active microwave observations are more sensitive to surface roughness and vegetation geometry, their advantage over passive microwave observations is their better spatial resolution, radiometric accuracy and independence of surface temperature. Consequently, the aim of this thesis is to retrieve t from backscatter observations in order to improve our understanding of vegetation dynamics. Chapter I starts with the motivation for this research. This is followed by a paragraph describing the research questions and objectives and the thesis outline. In Chapter II an introduction to microwave theory is presented. This chapter first deals with the radar equation and scattering properties of natural media. After that follows a literature review focused on previous studies which have assessed the sensitivity of backscatter observations to vegetation dynamics and other land surface parameters. Chapter III presents the retrieval of vegetation optical depth from Metop Advanced Scatterometer (ASCAT) backscatter observations using model parameters of the vegetation correction term within the TU Wien soil moisture retrieval algorithm. A first comparison between vegetation optical depth derived from passive microwave observations (tp) and vegetation optical depth from ASCAT (ta) is performed. Global spatial patterns of ta and tp are qualitatively compared to each other. A temporal comparison is performed by calculating the Spearman Rank Coefficient between climatologies of ta and tp. The strong spatial and temporal correspondence between ta and tp suggest that ta is sensitive to vegetation dynamics in most regions. However, in boreal forests low mean values for ta are found compared to tp. A low temporal correlation is found in deserts and tropical forests, which is attributed to the low natural variability of vegetation in these regions. Furthermore, the retrieval of ta enables the investigation of the effect of the vegetation parameterization in the TU Wien soil moisture retrieval algorithm. Overall, the vegetation parameterization as implemented in the TU Wien algorithm improves the soil moisture retrievals. However, in regions with high inter-annual variability in vegetation dynamics the soil moisture retrieval is degraded, most likely due to the fixed climatology of the correction term. A comprehensive inter-comparison of vegetation products is performed and described in Chapter IV. The inter-comparison is done between ta from Metop ASCAT observations, a cross-ratio (CR) from VH and VV observations from SAC-D Aquarius, tp from AMSR2 observations and Leaf Area Index (LAI) from SPOT VEGETATION. Spatial patterns of the different products are compared and all products follow the expected patterns according to land cover and climate class. Low values for ta are found in high latitude boreal forests and these are attributed to low backscatter values during frozen conditions. It is suggested that these low values in ta are likely to cause a bias in the TU Wien soil moisture product. A temporal comparison between the products shows that the seasonal trajectories of ta are able to follow vegetation dynamics as found in LAI and tp. In deciduous broadleaf forests a disparity is found between the products derived from scatterometers and the other products. This brings to light a different response of scatterometers compared to radiometers, which is possibly caused by leaf fall and the resulting double-bounce scattering. Lastly, phenological parameters, i.e. start of season (SOS) and peak of season (POS), are calculated for all products and compared with the aim to identify differences in timing. Spatial patterns of SOS and POS are tightly coupled between all products, but lags are found between the microwave and VNIR products which vary with land cover and climate. The study confirms the potential of ta to monitor vegetation and phenological parameters. More importantly, it presents a first global comparison between ta and cross-polarized data. The strong coupling between ta and CR suggests that CR may be used in soil moisture retrieval algorithms to improve vegetation parameterization. One of the disadvantages of the ta retrieval was that it is only available as a seasonal product, i.e. 366 values. However, the estimation of the model parameters within the TUWien soil moisture retrieval algorithm has been improved in a way that ta can now be calculated for every day. Chapter V investigates if the time series of ta, and subsequently the TUWien vegetation correction term, are also sensitive to vegetation dynamics by comparing them to LAI. Furthermore, the ability of ta to reproduce inter-annual variability in vegetation dynamics is assessed. Time series of ta are retrieved over Mainland Australia for the period 2007 - 2014. This period contains years with distinct climatic conditions, including the Millenium Drought (2000 - 2009), and two years with large amounts of rainfall as a result of a change in climate modes, due mainly to the El Niño Southern Oscillation. It is found that ta and LAI are tightly coupled, especially over sparsely vegetated regions and grasslands. Over croplands they start to deviate, which is a result of a lag between the two products. In deciduous broadleaf forests negative correlations between the two products are found, as is also found in the previous two chapters, Chapter III and IV. Significant differences between the mean values of drier years and the anomalously wet years are found. Patterns of increased ta correspond to those of LAI and surface soil moisture. Especially in central Australia large changes in ta and LAI are found, where the flush of grasses in a normally barren region effects both products. This thesis developed and validated the retrieval of ta from spaceborne active microwave observations and assesses its ability to monitor vegetation dynamics. It also identifies and analyses differences that arise between VNIR, passive and active microwave remote sensing, especially in boreal and deciduous forests. Overall, the ta satisfactorily follows vegetation patterns and dynamics as observed in VNIR and passive microwave vegetation products. Therefore, missions like Metops European Polar System - Second Generation and Sentinel-1 could retrieve or use ta for vegetation mapping and algorithmic improvements.9

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

Radar Satellite Imagery for Detecting Bark Beetle Outbreaks in Forests

Purpose of Review The overall objective of this paper is to review the state of knowledge on the application of radar data for detecting bark beetle attacks in forests. Due to the increased availability of high spatial and temporal resolution radar data (e.g. Sentinel-1 (S1)), the question is how this time series data can support operational forest management with respect to forest insect damage prevention. Furthermore, available radar systems will be listed and their potential for detecting bark beetle attacks will be discussed. To increase the understanding of the potential of radar time series for detecting bark beetle outbreaks, a theoretical background about the interaction of the radar signals with the forest canopy is given. Finally, gaps in the available knowledge are identified and future research questions are formulated which could advance our understanding of using radar data for detecting forest bark beetle attacks. Recent Findings. Few studies already demonstrate the high potential of S1 time series data for forest disturbance mapping in general. It was demonstrated that multi-temporal S1 data provide an excellent data source of describing the phenological characteristics of forests, which provide the basic knowledge for detecting bark beetle induced forest damages. It has been found that the optimal time for data acquisition is April to June for the pre-event and August to October for the post-event acquisitions.Summary. For detecting bark beetle induced forest damages, the literature review shows that mono-temporal radar data are of limited use, that shorter wavelength (e.g. C-band; X-band) have a higher potential than longer wavelength such as L-band and that the current S1 time series data have a high potential for operational applications.European Space Agenc

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

Investigating vegetation water dynamics and drought using Metop ASCAT over the North American Grasslands

In this study, we examined the ASCAT backscatter data from Metop-A from 2007 to 2016 to characterize spatial and temporal variability in the vegetation parameters of the TU Wien Soil Moisture Retrieval approach (TUW SMR) across the North American Grasslands. The vegetation parameters are the slope and curvature of a second order Taylor polynomial used to describe the incidence angle dependence of backscatter σ°. A recent development allows the vegetation parameters to be determined dynamically using the local slope values within a prescribed temporal window. Seasonal, interannual and diurnal variations in the vegetation parameters were found to vary across grassland cover types, reflecting variations in soil moisture availability and growing season length. While the slope has always been considered a measure of vegetation density, our results show that curvature also contains information about vegetation. Drought events in 2011 and 2012 resulted in extensive negative σ 40∘ and soil moisture anomalies during the maximum biomass period. Contiguous anomalies in slope and curvature were observed where the severity and persistence of the drought were enough to impact vegetation. Observed diurnal differences in slope and curvature suggest that daily moisture transport within the vegetation influences the relative dominance of scattering from the vegetation and soil surface.21923517ESA Climate Change Initiative Phase 2 Soil Moisture Projec