We report on apertureless near-field microscopy in the far infrared. We identify a configurational resonance of the scanning tip-surface system to be the dominating mechanism that forms the image. Experimental data such as the high imaging contrast and its spectral properties can be well explained and make the framework of a mesoscopic resonance an alternative to conventional scattering models that are used to interpret near-field data. Our findings are plausibly not restricted to the far infrared and may impact on near-field spectroscopy in general.

Abstract. Vegetation optical depth (VOD) retrieved from microwave radiometry correlates with the total amount of water in vegetation, based on theoretical and empirical evidence. Because the total amount of water in vegetation varies with relative water content (as well as with biomass), this correlation further suggests a possible relationship between VOD and plant water potential, a quantity that drives plant hydraulic behavior. Previous studies have found evidence for that relationship on the scale of satellite pixels tens of kilometers across, but these comparisons suffer from significant scaling error. Here we used small-scale remote sensing to test the link between remotely sensed VOD and plant water potential. We placed an L-band radiometer on a tower above the canopy looking down at red oak forest stand during the 2019 growing season in central Massachusetts, United States. We measured stem xylem and leaf water potentials of trees within the stand and retrieved VOD with a single-channel algorithm based on continuous radiometer measurements and measured soil moisture. VOD exhibited a diurnal cycle similar to that of leaf and stem water potential, with a peak at approximately 05:00 eastern daylight time (UTC−4). VOD was also positively correlated with both the measured dielectric constant and water potentials of stem xylem over the growing season. The presence of moisture on the leaves did not affect the observed relationship between VOD and stem water potential. We used our observed VOD–water-potential relationship to estimate stand-level values for a radiative transfer parameter and a plant hydraulic parameter, which compared well with the published literature. Our findings support the use of VOD for plant hydraulic studies in temperate forests.

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Timely and accurate large-scale data on agricultural activity are important for tracking and identifying management practices, cropland distribution, and for supporting food security programs. Needs of agricultural monitoring community involve a combination of high (<4 m) to moderate (<30 m) spatial resolutions, frequent revisit time, and open access, operational coverage for large spatial extents (Fritz et al., 2019). Because a majority of agricultural fields are just over 2 hectares in size, spatial resolutions should ideally be on the order of 100 m or less to adequately capture agricultural conditions and change (Yan & Roy, 2016). Revisit times on the order of weeks or less are desirable, because agricultural fields may undergo substantial change on diurnal or greater timescale due to processes such as tilling or precipitation (McNairn & Brisco, 2004). Collectively, since agriculture is closely tied to global markets and food security, there is a strong need for accurately monitoring agricultural activity at global scale (Becker-Reshef et al., 2019). Since retrievals made by Synthetic Aperture Radar (SAR) systems now meet or exceed these needs, they are well-suited for large-scale agricultural monitoring. Spaceborne SAR, such as the European Space Agency's (ESA) Sentinel-1 can map the Earth once every 6 to 12-days at moderate spatial resolution (Torres et al., 2012). SAR data provide valuable information useful for cropland identification, crop type classification and estimating yield (Betbeder et al., 2016; Huang et al., 2019; Whelen & Siqueira, 2018). For example, these data can be used to estimate biomass using backscatter magnitude, crop heights using interferometry and crop structure using polarimetry (Erten et al., 2016; Ferrazzoli et al., 1997; Wiseman et al., 2014). Also, unlike optical sensors, SAR can collect high quality data day and night and is largely unimpacted by atmospheric conditions, therefore having excellent potential for collecting dense time series. For these reasons, plus an increasing adoption of open data access policies, cheaper cloud computing resources, and a steady pipeline of future platforms, there has been a rapid increase in the use of SAR data sets with regards to agricultural applications and decision support systems.