Monitoring the water status of forests is paramount for assessing vegetation health, particularly in the context of increasing duration and intensity of droughts. In this study, a methodology was developed for estimating forest water potential at the canopy scale from ground-based L-band radiometry. The study uses radiometer data from a tower-based experiment of the SMAPVEX 19-21 campaign from April to October 2019 at Harvard Forest, MA, USA. The gravimetric and the relative water content of the forest stand was retrieved from radiometer-based vegetation optical depth. A model-based methodology was adapted and assessed to transform the relative water content estimates into values of forest water potential. A comparison and validation of the retrieved forest water potential was conducted with in situ measurements of leaf and xylem water potential to understand the limitations and potentials of the proposed approach for diurnal, weekly and monthly time scales. The radiometer-based water potential estimates of the forest stand were found to be consistent in time with rPearson correlations up to 0.6 and similar in value, down to RMSE = 0.14 [MPa], compared to their in situ measurements from individual trees in the radiometer footprint, showing encouraging retrieval capabilities. However, a major challenge was the bias between the radiometer-based estimates and the in situ measurements over longer times (weeks & months). Here, an approach using either air temperature or soil moisture to update the minimum water potential of the forest stand (FWPmin) was developed to adjust the mismatch. These results showcase the potential of microwave radiometry for continuous monitoring of plant water status at different spatial and temporal scales, which has long been awaited by forest ecologists and tree physiologists.

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m(2)]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 - December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g., AMSR-E), current (e.g., AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers.

A joint approach for estimating vertically continuous soil moisture profiles by combining P-band SAR polarimetry with soil hydrological modeling is proposed. The approach compares the decomposed soil scattering component from remotely sensed P-band SAR observations of NASA’s Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission with an ensemble of simulated counterparts based on the hydrological model HYDRUS-1D and the soil scattering model multi-layer small perturbation method (SPM). From the best fit between remote sensing and soil modeling, the most probable soil moisture profile can be retrieved. Estimated soil moisture profiles at individual monitoring stations across the U.S. are compared to in situ measurements, as well as the European ReAnalysis (ERA5) land and AirMOSS L4 products. Pearson’s coefficient of determination between estimated and auxiliary products prove the overall feasibility of the proposed method with respective R 2 of 0.92, 0.95, and 0.87.

Publication: Chaparro et al. (2024) This dataset contains estimates of Live Fuel Moisture Content (LFMC) in the Western United States. LFMC is the percentage of vegetation water mass over the dry biomass of the plants. Here, LFMC is retrieved by isolating the water component of the passive microwaves vegetation optical depth (VOD) signal at three frequencies: L-band (1.4 GHz), X-band (10.65 GHz) and Ku-band (18.7 GHz). Each frequency represents a different canopy sensing depth. To isolate LFMC from VOD, auxiliary information to account for the biomass and structure of the vegetation has been used: radar backscatter data from Sentinel-1 and canopy height data from GEDI/Sentinel-2. The dataset spans between April 2015 and December 2018 for L- and X-bands, and between April 2015 and July 2018 for Ku-band retrievals. Details: Period: April 2015 - December 2018 (daily resolution) Gridding: 0.25º Grid type: lat/lon Size: 73 (lat) x 104 (lon) x 1371 (time)

Accurate soil carbon data are important for understanding the permafrost response and potential carbon release to future climate change. However, there is a large discrepancy in current soil organic carbon (SOC) estimates in the Arctic, where sparse measurements are unable to capture SOC complexity over the vast and remote region. Polarimetric Synthetic Aperture Radar (SAR) data are sensitive to roughness and moisture conditions of soil and vegetation, and may provide useful information on surface and profile SOC properties ( Yi et al., 2021 , 2022 ). The NASA Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign acquired an abundance of full-polarimetric P- and L-band SAR data across Alaska and western Canada ( Miller et al., 2019 ), which provides opportunities to test new remote sensing applications. The main objective of this study is to investigate the potential of low-frequency polarimetric SAR data for regional SOC retrieval in the Arctic through data analysis and modeling. We chose the Alaska North Slope as our study area due to more in-situ data available in this area.

Tracking seasonal dynamics of evapotranspiration (ET) across global biomes and along seasonal time periods using remote sensing is vital for monitoring ecosystem health and indicating early signals of drought. In this study, we assess the potential of adding weather and illumination-independent signals from active and passive microwave remote sensing (SAR backscatter & vegetation optical depth, VOD) to the established set of ET products, like from optical/thermal remote sensing (MODIS, SEVIRI) and reanalysis (ERA-5 land, GLDAS) data.Our study covers a four-year period (2017-2020), including dry (2018 & 2019) and wet (2017) years. The study was conducted over eight ICOS sites across Europe. These sites are predominantly forested with a low biomass dynamic over the observation period.We find that the ET products from in situ Eddy Covariance (EC), MODIS, and GLDAS deviate relatively minor along the seasons (< 1 [mm/day]), but differ between years. Here, the years (2017-2020) indicate a slightly different ET rate between in situ measurements (EC) and derived products (MODIS & GLDAS), which is currently being investigated. The microwave-based indicators (backscatter & VOD) are proxies by their nature and serve as first-order indicators of relative dynamics allowing the identification of seasonal patterns of ET as well as their spatio-temporal anomalies along both dry and wet years.

This study presents a comparison between satellite-based vegetation optical depth (VOD) from multi-frequency radiometry (X-, C- and L-band), VOD-derived relative water content (RWC) and auxiliary data (e.g., evapotranspiration and soil moisture), which are investigated for their sensitivity to water status of tree canopies under dry and wet conditions for a temperate forest in Thuringia, Central Germany. For this, we estimated RWC directly from VOD normalization assuming no major changes in vegetation biomass or plant structure during the study period (2015-2019).Our results show that RWC seasonalities are aligned for all investigated frequencies showing its maximum in early summer when leaves and twigs of the top and low canopy are particularly wet and photosynthetically active. Investigating drought versus non-drought years, we observed that X-band RWC is the one better capturing drought status by exhibiting low values in the extreme drought year 2018 compared to the wet year 2017 while L-band RWC reflects the ecological memory from the extreme drought conditions in 2018 in year 2019 estimates.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (m g ) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of m g with help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

A P-band SAR moisture estimation method is introduced for complex soil permittivity and penetration depth estimation using fully polarimetric P-band SAR signals. This method combines eigen- and model-based decomposition techniques for separation of the total backscattering signal into three scattering components (soil, dihedral, and volume). The incorporation of a soil scattering model allows for the first time the estimation of complex soil permittivity and permittivity-based penetration depth. The proposed method needs no prior assumptions on land cover characteristics and is applicable to a variety of vegetation types. The technique is demonstrated for airborne P-band SAR measurements acquired during the AirMOSS campaign (2012–2015). The estimated complex permittivity agrees well with climate and soil conditions at different monitoring sites. Based on frequency and permittivity, P-band penetration depths vary from 5 cm to 35 cm. This value range is in accordance with previous studies in the literature. Comparison of the results is challenging due to the sparsity of vertical soil in situ sampling. It was found that the disagreement between in situ measurements and SAR-based estimates originates from the discrepancy between the in situ measuring depth of the top-soil layer (0–5 cm) and the median penetration depth of the P-band waves (24.5–27 cm).

The vegetation optical depth (VOD) variable contains information on plant water content and biomass. It can be estimated alongside soil moisture from currently operating satellite radiometer missions, such as SMOS (ESA) and SMAP (NASA). The estimation of water fluxes, such as plant water uptake (PWU) and transpiration rate (TR), from these earth system parameters (VOD, soil moisture) requires assessing water potential gradients and flow resistances in the soil, the vegetation and the atmosphere. Yet water flux estimation remains an elusive challenge especially on a global scale. In this concept study, we conduct a field-scale experiment to test mechanistic models for the estimation of seasonal water fluxes (PWU and TR) of a winter wheat stand using measurements of soil moisture, VOD, and relative air humidity (RH) in a controlled environment. We utilize microwave L-band observations from a tower-based radiometer to estimate VOD of a wheat stand during the 2017 growing season at the Selhausen test site in Germany. From VOD, we first extract the gravimetric moisture of vegetation and then determine the relative water content (RWC) and vegetation water potential (VWP) of the wheat field. Although the relative water content could be directly estimated from VOD, our results indicate this may be challenging for the phenological phases, when rapid biomass and plant structure development take place within the wheat canopy. We estimate water uptake from the soil to the wheat plants from the difference between the soil and vegetation potentials divided by the flow resistance from soil into wheat plants. The TR from the wheat plants into the atmosphere was obtained from the difference between the vegetation and atmosphere water potentials divided by the flow resistances from plants to the atmosphere. For this, the required soil matric potential (SMP), the vapor pressure deficit (VPD), and the flow resistances were obtained from on-site observations of soil, plant, and atmosphere together with simple mechanistic models. This pathfinder study shows that the L-band microwave radiation contains valuable information on vegetation water status that enables the estimation of water dynamics (up to fluxes) from the soil via wheat plants into the atmosphere, when combined with additional information of soil and atmosphere water content. Still, assumptions have to be made when estimating the vegetation water potential from relative water content as well as the water flow resistances between soil, wheat plants, and atmosphere. Moreover, direct validation of water flux estimates for the assessment of their absolute accuracy could not be performed due to a lack of in situ PWU and TR measurements. Nonetheless, our estimates of water status, potentials, and fluxes show the expected temporal dynamics, known from the literature, and intercompare reasonably well in absolute terms with independent TR estimates of the NASA ECOSTRESS mission, which relies on a Priestly–Taylor type of retrieval model. Our findings support that passive microwave remote-sensing techniques qualify for the estimation of vegetation water dynamics next to traditionally measured stand-scale or plot-scale techniques. They might shed light on future capabilities of monitoring water dynamics in the soil–plant–atmosphere system including wide-area, remote-sensing-based earth observation data.

The upcoming launch of the L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) will enable multi-frequency SAR observations when combined with existing C-band satellite missions (e.g., Sentinel-1). Due to the different penetration depths of the SAR signals, multi-frequency SAR offers great potential for field-scale agricultural monitoring and the estimation of soil and plant parameters. The SARSense campaign, conducted between June and August 2019 at the Selhausen agricultural test site near Jülich, Germany, has yielded a comprehensive dataset that includes both air- and space-borne C- and L-band SAR data, extensive in-situ field measurements of soil and plant parameters as well as unmanned aerial systems (UAS)-based multispectral and thermal infrared measurements and cosmic neutron sensing observations. The study provides both, an insight into the strengths and limitations of the acquired dataset as well as an analysis of the different behaviour of C- and L-band backscattering on changing soil moisture and plant parameters for taproot crops and cereals.

With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multispectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-frequency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.

A method for estimating complex soil permittivity (or moisture) and penetration depth based on SAR decomposition is presented. By combining model- and eigen-based decomposition techniques, SAR observations are separated into single scattering components (from soil & vegetation). The proposed method incorporates a multi-layer rough surface scattering model to simulate the soil scattering contribution. From the decomposed soil scattering component, permittivity and thus penetration depth can be estimated from SAR observations. Results are presented for the AirMOSS campaign within the MOISST site, OK, USA. As first results, a median value of 16.44 + 2.02 for the complex permittivity was estimated from the 19 P-band data takes at the SoilSCAPE in situ station in Canton, OK, USA. Overall, the retrieval results for the real part of the complex permittivity are similar to in situ values at 30 cm soil depth, with differences in respective median values of 3.44. The median of penetration depths at the Canton site is 23.91 cm,

An active–passive microwave retrieval algorithm for simultaneous determination of soil surface roughness parameters [vertical root-mean-square (RMS) height ( ${s}$ ) and horizontal correlation length ( ${l}$ )] is presented for bare soils. The algorithm is based on active–passive microwave covariation, including the improved Integral Equation Method (I 2 EM), and is tested with global soil moisture active passive (SMAP) observations. The estimated retrieval results for ${s}$ and ${l}$ are overall consistent with values in the literature, indicating the validity of the proposed algorithm. Sensitivity analyses showed that the developed roughness retrieval algorithm is independent of permittivity for ${\varepsilon }_{s} > 10$ [-]. Furthermore, the physical model basis of this approach (I 2 EM) allows the application of different autocorrelation functions (ACF), such as Gaussian and exponential ACFs. Global roughness retrieval results confirm bare areas in deserts such as Sahara or Gobi. However, the type of ACF used within roughness parameter estimation is important. Retrieval results for the Gaussian ACF describe a rougher surface than retrieval results for the exponential ACF. No correlations were found between roughness results and the amount of precipitation or the soil texture, which could be due to the coarse spatial resolution of the SMAP data. The extension of this approach to vegetated soils is planned as an add-on study.

A retrieval methodology for forest water potential from ground-based L-band radiometry is proposed. It contains the estimation of the gravimetric and the relative water content of a forest stand and tests in situ- and model-based functions to transform these estimates into forest water potential. The retrieval is based on vegetation optical depth data from a tower-based experiment of the SMAPVEX 19–21 campaign for the period from April to October 2019 at Harvard Forest, MA, USA. In addition, comparison and validation with in situ measurements on leaf and xylem water potential as well as on leaf wetness and complex permittivity are foreseen to understand limitations and potentials of the proposed approach. As a first result the radiometer-based water potential estimates of the forest stand are concurrent in time and similar in value with their in situ (xylem) counterparts from single trees in the radiometer footprint.

The attenuation of microwave emissions through the canopy is quantified by the vegetation optical depth (VOD), which is related to the amount of water, the biomass and the structure of vegetation. To provide microwave-derived plant water estimates, one must account for biomass/structure contributions in order to extract the water component from the VOD. This study uses Aquarius scatterometer data to build an L-band global seasonality of vegetation volume fraction (δ), representative of biomass/structure dynamics. The dynamic range of δ is adapted for its application in a gravimetric moisture (Mg) retrieval model. Results show that δ ranging from 0 to 3.35.10- 4 is needed for modelling physically reasonable Mg values. The global average of δ shows consistent spatial patterns across vegetation distributions, and δ seasonality is coherent with the phenology of the studied vegetation types. These findings enable the separation of information on vegetation water and biomass/structure inherent within VOD.

With new Earth Observation (EO) and Geoinformation (GI) data sources increasingly becoming available, evermore new skills for data collection, processing, analysis and application are required. They are needed not only from scientists, but also from practitioners working in businesses, public and private EO*GI and related sectors. Aligning the continuously evolving skill sets demanded by the market and existing academic and vocational training programmes is not an easy task. Training programmes should be grounded in real needs of the sector and its labour market. To do this, it is necessary to identify the knowledge and skills needed, and map their interconnectivity in specific frameworks, which can later be used for the definition of new curricula or job-oriented learning paths. This paper presents a framework for the EO*GI sector, based on a Body of Knowledge (BoK), by creating a complete set of concepts with a semantic structure underneath that supports academia and industry. Creating and updating the BoK is supported by an editing tool, the Living Textbook and by experts in the EO*GI domain, who contributes to the BoK’s enrichment.

The precise positioning and long-term monitoring of the grounding line, forming the boundary between grounded and floating ice of marine ice sheets and tidewater glaciers, is critical for assessing ice sheet/glacier stability, ice sheet/glacier mass balance calculations and numerical ice modelling. However, mapping of the grounding line is a challenging task, since it is a subglacial and transient feature. Remote sensing techniques do not map the grounding line directly but locate surface features in the grounding zone that serve as proxies for the true grounding line position. The large variety of methods, products and publications, as well as an inconsistent use of terminology additionally complicate the topic. We present the first detailed review of existing remote sensing techniques and data for grounding line mapping. Benefits and limitations of the different techniques and data sources are discussed and illustrated with examples. We identify a present tradeoff between accuracy and both spatial coverage and temporal resolution of the derived grounding line products. Rather new methods like Sentinel-1-SAR (Synthetic Aperture Radar) Differential Range Offset Tracking (DROT) and Pseudo Crossover Radar Altimetry (PCRA) offer the potential for recent measurements and to fill gaps in some areas. Nevertheless, there is a high demand of new spaceborne radar sensors that provide time series of very short repeat pass data to enable regular grounding line monitoring with most accurate Differential SAR Interferometry (DInSAR) on a continental scale.

In summer 2019 the SARSense campaign was held in Jülich, Germany, to provide insights into the potentials and specifications of the ESA Copernicus candidate mission ROSE-L (Radar Observation System for Europe). ROSE-L will consist of two satellites that carry a polarimetric L-band SAR. Since the L-band signal can penetrate through many natural materials such as vegetation, dry snow and ice, the mission will provide additional information that cannot be gathered by the Copernicus Sentinel-1 C-band SAR mission. The overall objective of the SARSense 2019 campaign is to analyze the mission design concerning its potential for agricultural monitoring services including target applications such as soil moisture monitoring, irrigation management, crop type discrimination, food security and precision farming. The SARSense in situ measurements of soil moisture, soil temperature, vegetation properties, UAS-based multispectral and thermal mapping, as well as the airborne SAR observations are presented as well as strategies for soil moisture retrieval and first analysis.

Changing climate patterns have increased hydrological extremes in many regions [1]. This impacts water and carbon cycles, potentially modifying vegetation processes and thus terrestrial carbon uptake. It is therefore crucial to understand the relationship between the main water pools linked to vegetation (i.e., soil moisture, plant water storage, and atmospheric water deficit), and how vegetation responds to changes of these pools. Hence, the goal of this research is to understand the water pools and fluxes in the soil-plant-atmosphere continuum (SPAC) and their relationship with vegetation responses. Our study spans from April 2015 to March 2019 and is structured in two parts: Firstly, relative water content (RWC) is estimated using a multi-sensor approach to monitor water storage in plants. This is at the core of our research approach towards water pool monitoring within SPAC. Here, we will present a RWC dataset derived from gravimetric moisture content (mg) estimates using the method first proposed in [2], and further validated in [3]. This allows retrieving RWC and mg independently from biomass influences. Here, we apply this method using a sensor synergy including (i) vegetation optical depth from SMAP L-band radiometer (L-VOD), (ii) vegetation height (VH) from ICESat-2 Lidar and (iii) vegetation volume fraction (d) from AQUARIUS L-band radar. RWC status and temporal dynamics will be discussed. Secondly, water dynamics in the SPAC and their impact on leaf changes are analyzed. We will present a global, time-lag correlation analysis among: (i) the developed RWC maps, (ii) surface soil moisture from SMAP (SM), (iii) vapor pressure deficit (VPD; from MERRA reanalysis [4]), and (iv) leaf area index (LAI; from MODIS [5]). Resulting time-lag and correlation maps, as well as analyses of LAI dynamics as a function of SPAC, will be presented at the conference. References [1] IPCC. (2013). Annex I: Atlas of global and regional climate projections. In: van Oldenborgh, et al. (Eds.) Climate Change 2013: The Physical Science Basis (pp. 1311-1393). Cambridge University Press. [2] Fink, A., et al. (2018). Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach. In IGARSS 2018 (pp. 353-356). IEEE. [3] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens. 2019, 11(20), 2353 [4] NASA (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. Accessed 2020-01-14 from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/. [5] Myneni, R., et al. (2015). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. Accessed 2020-01-14 from https://doi.org/10.5067/MODIS/MOD15A2H.006.