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This work was supported by the “la Caixa” Foundation (ID 100010434) under Grant LCF/PR/MIT19/51840001 (MIT-Spain Seed Fund), by MISTI-Germany and MISTI-Belgium Seed Funds, by the XXXIII Ramón Areces Postdoctoral Fellowship, by the NASA (Grant 1510842), by grants PID2020-114623RB-C32 and MDM-2016-0600 funded by MCIN/AEI/10.13039/501100011033, by the European Regional Development Fund (ERDF), and by the NASA Postdoctoral Program at the NASA Goddard Space Flight Center. D. Chaparro thanks the AKS department (DLR) and the CEE department (MIT) for hosting and supervision.

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., David Chaparro has received funding by “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/MIT19/51840001, by the MIT-MISTI, by the XXXIII Ramón Areces Postdoctoral Fellowship, and by grants PID2020-114623RB-C32 and MDM-2016-0600 funded by MCIN/AEI/10.13039/501100011033 and by the European Regional Development Fund (ERDF, EU). María Piles was supported by grant RTI2018-096765-A-100 funded by MCIN/AEI/10.13039/501100011033 and by the European Regional Development Fund (ERDF, EU).

With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Ob-serving 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 multi-spectral 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-fre-quency 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.

Metrics such as Leaf Area Index (LAI) are key factors in agricultural monitoring to understand the health and predictive yield of crops. Knowing the spatial distribution and variability in more detail increases the precision of fertilizer and irrigation practices. Unmanned Aircraft Systems (UAS) provide a means to carry sensors at a low altitude below the clouds providing a much higher spatial and temporal resolution than previously seen with satellite remote sensing while also providing more spatially complete data as compared to ground methods. Being that LiDAR is an active sensor and does not depend on solar reflectance and its corresponding zenith angle like commonly used passive optical sensors, it can further improve upon these UAS characteristics. It can also sense further into the canopy as the laser signals can pass through small gaps and are not affected by the shadowing of plant features created by the canopy itself. Evaluating the penetration of these signals and investigating the gap fraction (GF) that relates to canopy density, we are able to retrieve LAI. However, as LiDAR is sensing all above-ground plant elements it may present the ability to estimate Plant Area Index (PAI) rather than LAI when monitoring an entire growing season for a cereal crop like winter wheat that begins browning during senescence. This study investigates the feasibility of using LiDAR to estimate LAI or similar crop canopy density metrics. As LiDAR sensors for UAS are just becoming more accessible, studies related to this topic are scarcely seen.In this study, a winter wheat field in Selhausen, Germany (~10 ha in size) was monitored throughout the growing season using the following methods: [1] air campaigns with a DJI Matrice 600 UAS with a YellowScan Surveyor LiDAR system, [2] a DJI Matrice 600 UAS with a Micasense RedEdge-M (five band) multispectral sensor, and [3] ground measurements using a SS1 SunScan ceptometer. The resulting LAI type metrics of the UAS LiDAR methods used were compared to methods commonly used with multispectral (MS) and ground instruments to assess the proposed method’s potential. Additionally, because both products are spatially complete unlike the ground measurements, the LiDAR and multispectral methods were compared for similarities in spatial patterns.The results showed promise in using UAS LiDAR to estimate metrics that relate to LAI. Pearson correlation coefficient between the LiDAR and multispectral methods were moderate to high (R= 0.39 – 0.66) over the growing season. The comparison of UAS LiDAR towards the ground reference was within a 3% difference at times before senescence. Later in the growing season, the discrepancy increased between LiDAR and MS sensor retrievals mainly because of plant browning related to changes in plant chlorophyll content. This study covers the benefits of using UAS mounted LiDAR for LAI related measurements and its potential for improving crop health monitoring for precision farming.

Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose.

A method of physical integration of scatteringmodels is presented here to estimate the backscattering coefficient(BSC) for L-band, brightness temperature (TB) for L- and C-Bandand the reflectance for visible (VIS) and near-infrared (NIR)region for dynamic vegetated terrain. The SPIN (SpectrumInvariant Interaction) model is obtained by solving vectorradiative transfer (VRT) equations kernel-based and therefore fordifferent wave interaction mechanisms. To demonstrate itsapplication for the microwave region, the measurements duringthe growing cycle of corn from the Eleventh Microwave, Water,and Energy Balance Experiment (MicroWEX-11) have been used.For the optical part the results are compared with the PROSAILmodel. By applying the SPIN model in the radar regime, it couldbe shown that the modeled backscattering coefficients (BSC)correlate strongly with the vertical polarization measurements(Pearson 0.83, R20.69) and are less correlated with the horizontalmeasurements (Pearson 0.45, R20.20). In addition, the modeledbrightness temperatures (L- and C-band) in both polarizationstates are also highly correlated with the MicroWEX-11measurements (L-band: Pearson 0.755, R2 0.57; C-band: Pearson0.73, R2 0.53). Finally, the optical results are consistent with theresults of other optical models (Pearson 0.99, R2 0.98).

Many attempts have been made and are undertaken to model the electromagnetic (EM) interaction with vegetation-coveredsoils at different frequencies. The modelling concepts and architectures range from empirical correlations to simple andsophisticated physical models [1, 2]. However, a general understanding of the physical properties and interactions along frequencyand for a wider part of the EM spectrum is difficult if the different models are treating the single frequencies and acquisitioncharacteristics (e.g. active or passive recording) separately. Due to the increasing fleet of earth observation sensors operating inmultiple frequency bands, demand for a spectrum-overarching EM modelling is becoming increasingly important. An integrativemodeling approach offers the chance that the different insights into biophysical and biochemical processes can be consideredconsistently including the respective dielectric properties and structural characteristics. With this objective we developed a kernel-driven electromagnetic interaction model using vector radiative transfer (VRT), called SPIN (Spectrum Invariant Interaction)model, which represents an integrated model approach for active and passive microwaves and passive optical as well as thermalsignatures [3]. Preparatory research for this study was the development of the RadOptics Model [4]. The main advantage of a kernel-driven approach is its analytical invertibility and the representation of the VRT as sum of linear equations, which leads toan enlargement of the information by including multiple frequencies. In addition, the model does not require semi-empirical or statistical calibration (i.e. all model parameters have physical meaning and none just for model adjustment.).