The presence, duration, and amount of surface canopy water (SCW) is important in microwave remote sensing for agricultural applications. Our current understanding of the effect of SCW on total backscatter and the underlying mechanisms is limited. The aim of this study is to investigate the effect of SCW on backscatter as a function of frequency and polarization, and to understand the underlying mechanisms. For this purpose, the radiative transfer model developed at the Tor Vergata University was used to simulate the total backscatter at L-, C-, and X-band. First, simulations from the standard Tor Vergata model were compared to L-band observations. Then, two additional implementations of the model were developed to account for the effect of SCW and the presence of water on the soil surface on radar backscatter. Representing SCW by the inclusion of additional water in the vegetation leads to an increase in vegetation volume scattering and a reduction in the contribution from double bounce and direct scattering from the ground. This increases total backscatter, particularly at lower frequencies. Results suggest that the difference between backscatter in the presence and absence of SCW can be up to around 2.5 dB in L-band and likely less at higher frequencies. The effect of water on the canopy (SCW) reaches its maximum during the mid and late season as the crop reached its maximum biomass. The influence of dew on the reflectivity of the soil surface resulted in a difference of up to 3.8 dB between backscatter in the presence and absence of SCW. In particular, at low frequencies and low vegetation cover, the presence of water on the soil surface needs to be taken into account to correctly capture the sub-daily dynamics in backscatter. The findings of this study are relevant for current and future SAR missions including Sentinel-1, ROSE-L, NISAR, SAOCOM, ALOS, CosmoSkyMed, TerraSAR-X, TanDEM-X and constellations such as those of ICEYE, and Capella which have dawn/dusk overpasses or multiple overpasses per day.

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Microwave observations are sensitive to vegetation water content (VWC). Consequently, the increasing temporal and spatial resolution of spaceborne microwave observations creates a unique opportunity to study vegetation water dynamics and its role in the diurnal water cycle. However, we currently have a limited understanding of sub-daily variations in the VWC and how they affect microwave observations. This is partly due to the challenges associated with measuring internal VWC for validation, particularly non-destructively, and at timescales of less than a day. In this study, we aimed to (1) use field sensors to reconstruct diurnal and continuous records of internal VWC of corn and (2) use these records to interpret the sub-daily behaviour of a 10 d time series of polarimetric L-band backscatter with high temporal resolution. Sub-daily variations in internal VWC were calculated based on the cumulative difference between estimated transpiration and sap flow rates at the base of the stems. Destructive samples were used to constrain the estimates and for validation. The inclusion of continuous surface canopy water estimates (dew or interception) and surface soil moisture allowed us to attribute hour-to-hour backscatter dynamics either to internal VWC, surface canopy water, or soil moisture variations. Our results showed that internal VWC varied by 10 %–20 % during the day in non-stressed conditions, and the effect on backscatter was significant. Diurnal variations in internal VWC and nocturnal dew formation affected vertically polarized backscatter most. Moreover, multiple linear regression suggested that the diurnal cycle of VWC on a typical dry day leads to a 2 (HH, horizontally, and cross-polarized) to almost 4 (VV, vertically, polarized) times higher diurnal backscatter variation than the soil moisture drydown does. These results demonstrate that radar observations have the potential to provide unprecedented insight into the role of vegetation water dynamics in land–atmosphere interactions at sub-daily timescales.@en

For a good interpretation of radar backscatter sensitivity to vegetation water dynamics, we need to know which parts of the vegetation layer control that backscatter. However, backscatter sensitivity to different depths in the canopy is poorly understood. This is partly caused by a lack of observational data to describe the vertical moisture distribution. In this study, we aimed to understand the sensitivity of L-band backscatter to water at different heights in a corn canopy. We studied changes in the contribution of different vertical layers to total backscatter throughout the season and during the day. Using detailed field measurements, we first determined the vertical distribution of moisture in the plants, and its seasonal and sub-daily variation. Then, these measurements were used to define different sublayers in a multi-layer water cloud model (WCM). To calibrate and validate the WCM, we used hyper-temporal tower-based polarimetric L-band scatterometer data. WCM simulations showed a shift in dominant scattering from the lowest 50 cm to 50–100 cm during the season in all polarizations, mainly due to leaf and ear growth and corresponding scattering and attenuation. Dew and rainfall interception raised sensitivity to upper parts of the canopy and lowered sensitivity to lower parts. The methodology and results presented in this study demonstrate the importance of the vertical moisture distribution on scattering from vegetation. These insights are essential to avoid misinterpretation and spurious artefacts during retrieval of soil moisture and vegetation parameters.

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The presence of surface water on the canopy affects radar backscatter. However, its influence on the relationship between radar backscatter and crop biophysical parameters has not been investigated. The aim of this study was to quantify the influence of surface canopy water (SCW) on the relationship between L-band radar backscatter and biophysical variables of interest in agricultural monitoring. In this study, we investigated the effect of SCW on the relationship between co- and cross-polarized radar backscatter, cross ratios (VH/VV and HV/HH), and radar vegetation index (RVI) and dry biomass, vegetation water content (VWC), plant height and leaf area index (LAI). In addition, the effect of SCW on estimated vegetation optical depth (VOD) and its relationship with internal VWC was investigated. The analysis was based on data collected during a field experiment in Florida, USA in 2018. A corn field was scanned with a truck-mounted, fully polarimetric, L-band radar along with continuous monitoring of SCW (dew, interception) and soil moisture every 15 min for 58 days. In addition, pre-dawn destructive sampling was conducted to measure internal vegetation water content and dry biomass. Results showed that the presence of SCW can increase the radar backscatter up to 2 dB and this effect was lower for cross ratios (CRs) and RVI. The Spearman's rank correlations between radar observables and biophysical parameters were, on average, 0.2 higher for dry vegetation compared to wet vegetation. The estimated VOD from wet vegetation was generally higher than those from dry vegetation, which led to different fitting parameter (so-called b) values in the linear fit between VOD and VWC. The results presented here underscore the importance of considering the influence of SCW on the retrieval of biophysical variables of interest in agricultural monitoring. In particular, they highlight the importance of overpass time, and the impact that daily patterns in dew and interception can have on the retrieval of biophysical variables of interest.

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The objective of this study was to investigate the effect of diurnal variation in internal and surface canopy water on L-band backscatter in the context of the influence of overpass time on agricultural applications. A unique and intensive dataset was collected during a full growing season of corn in Florida, USA in 2018. L-band data was collected by using a fully polarized scatterometer mounted on a crane. In order to measure internal vegetation water distribution and dry biomass, pre-dawn destructive sampling was conducted three times a week for a full growing season. In addition, soil moisture, meteorological, dew, and interception data were measured every 15 minutes for the entire growing season. Results demonstrate that the presence of surface canopy water and diurnal internal water dynamics can each affect the radar backscatter up to 3–4 dB. The surface canopy water also affects the relationship between radar and crop biophysical variables. In corn, the spearman rank correlation between backscatter and biophysical variables is, on average, about 0.2 higher for dry vegetation compared to wet vegetation. The results highlight the possible influence of overpass time on the interpretation of radar data for vegetation monitoring.@en

Soil moisture (SM) retrieval in agricultural regions during the growing seasons is particularly challenging due to high spatial variability and dynamic vegetation conditions. The retrievals have been problematic even when the passive signatures at different spatial scales match well since they depend upon the accuracy of vegetation information such as the vegetation water content (VWC). The VWC used in the Soil Moisture Active Passive (SMAP) single channel retrieval algorithm (SCA) is derived from remotely sensed, climatologically-based Normalized Difference Vegetation Index (NDVI), which does not respond to real-time vegetation dynamics and is prone to saturation. This study explored the differences and seasonal trend in passive signatures and SM at satellite- and field-scales and investigated uncertainties in retrievals arising from different approaches used to estimate VWC from optical and radar indices. It used high temporal resolution, ground-based data collected during the SMAP Validation-Microwave Water, Energy Balance Experiment in 2016 (SMAPVEX16-MicroWEX) during a growing season of corn and soybean. Overall, the brightness temperatures (T_B) from SMAP matched well with the upscaled, ground-based T_B, with a root mean square differences (RMSDs) of about 5 K. In contrast, the SMAP SM retrievals were worse during rapid vegetation growth in the mid-season, with higher RMSDs compared to the upscaled in situ SM, than those in the late-season. In addition, the ground-based T_B from corn and soybean were similar in the early and the late seasons, while their emission differences were > 40 K in the mid season. This indicates the importance of accurate VWC information, particularly during the early and late growing seasons, to account for sub-pixel heterogeneities in agricultural regions. VWC obtained from five optical and radar indices were used in the SMAP SCA for soil moisture retrieval for the entire growing season of corn. The NDVI-based VWC provided SM retrievals that were consistently lower compared to those using in situ VWC, with a higher RMSD of 0.030 m³/m³ and a negative bias of 0.020 m³/m³ for VWC > 4 kg/m². The Normalized Difference Water Index (NDWI)-derived VWC resulted in lower SM retrieval RMSD of 0.022 m³/m³ when compared with in situ SM. Among the three radar indices, vertically polarized cross-pol ratio (CR_vv)-derived VWC provided similar RMSDs in retrieved SM as the NDWI-derived VWC during the growing season. The radar vegetation index (RVI)-derived VWC improved in the late season compared to the in situ VWC and resulted in SM retrievals with RMSDs similar to the CR_vv-derived retrievals. Results presented here suggest that SMAP SCA SM retrievals could be improved through the use of near-real time NDWI and CR_vv-derived vegetation information. Microwave data are available regardless of cloud cover, so the guaranteed availability of CR_vv to capture seasonal and interannual variability is advantageous.

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The latest developments in radar mission concepts suggest that subdaily synthetic aperture radar will become available in the next decades. The goal of this study was to demonstrate the potential value of subdaily spaceborne radar for monitoring vegetation water dynamics, which is essential to understand the role of vegetation in the climate system. In particular, we aimed to quantify fluctuations of internal and surface canopy water (SCW) and understand their effect on subdaily patterns of L-band backscatter. An intensive field campaign was conducted in north-central Florida, USA, in 2018. A truck-mounted polarimetric L-band scatterometer was used to scan a sweet corn field multiple times per day, from sowing to harvest. SCW (dew, interception), soil moisture, and plant and soil hydraulics were monitored every 15 min. In addition, regular destructive sampling was conducted to measure seasonal and diurnal variations of internal vegetation water content. The results showed that backscatter was sensitive to both transient rainfall interception events, and slower daily cycles of internal canopy water and dew. On late-season days without rainfall, maximum diurnal backscatter variations of >2 dB due to internal and SCW were observed in all polarizations. These results demonstrate a potentially valuable application for the next generation of spaceborne radar missions.@en

The process of evaporation interacts with the soil, which has various comprehensive mechanisms. Multiphase flow models solve air, vapour, water, and heat transport equations to simulate non-isothermal soil moisture transport of both liquid water and vapor flow, but are only applied in non-vegetated soils. For (sparsely) vegetated soils often energy balance models are used, however these lack the detailed information on non-isothermal soil moisture transport. In this study we coupled a multiphase flow model with a two-layer energy balance model to study the impact of non-isothermal soil moisture transport on evaporation fluxes (i.e., interception, transpiration, and soil evaporation) for vegetated soils. The proposed model was implemented at an experimental agricultural site in Florida, US, covering an entire maize-growing season (67 days). As the crops grew, transpiration and interception became gradually dominated, while the fraction of soil evaporation dropped from 100% to less than 20%. The mechanisms of soil evaporation vary depending on the soil moisture content. After precipitation the soil moisture content increased, exfiltration of the liquid water flow could transport sufficient water to sustain evaporation from soil, and the soil vapor transport was not significant. However, after a sufficient dry-down period, the soil moisture content significantly reduced, and the soil vapour flow significantly contributed to the upward moisture transport in topmost soil. A sensitivity analysis found that the simulations of moisture content and temperature at the soil surface varied substantially when including the advective (i.e., advection and mechanical dispersion) vapour transport in simulation, including the mechanism of advective vapour transport decreased soil evaporation rate under wet condition, while vice versa under dry condition. The results showed that the formulation of advective soil vapor transport in a soil-vegetation-atmosphere transfer continuum can affect the simulated evaporation fluxes, especially under dry condition.

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Observations from spaceborne radar contain considerable information about vegetation dynamics.The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data.The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain.Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring.Hence, SAR applications, theory, and applications are considered here too.An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications.A description of the current vegetation modeling approaches will be given.A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization.Finally, emerging applications, opportunities and challenges will be identified and discussed.Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications.

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In this study, the impact of spatial variability due to the heterogeneity of vegetation in the agricultural region on passive microwave signatures available at various scales are explored using the brightness temperature (T_B) observed from ground, air, and space. These observations were conducted during a growing season of corn and soybean in South Fork watershed, Iowa, as part of the NASA-Soil Moisture Active Passive Validation Experiment (SMAPVEX16). Both empirical and physically-based microwave emission models are used to understand the effects of vegetation on T_B for corn and soybean using ground-based T_B observations. The modeled T_B will be upscaled based upon the USDA crop layer map to compare with the T_B observed in the coarse scales.

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The NASA Soil Moisture Active/Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions include microwave radiometers at L-band that provides global observations of SM at 36 and 25km, respectively, with a repeat coverage of every 2-3 days. Agricultural regions, with their highly dynamic vegetation and spatial heterogeneity are particularly challenging for soil moisture retrieval algorithms. The Microwave Water and Energy Balance Experiment was conducted as part of the SMAP Validation Experiment (SMAPVEX16-MicroWEX) during the summer of 2016 in a predominantly agricultural region in Iowa, USA. During SMAPVEX16-MicroWEX, ground-based observations of active and passive signatures were obtained every 15-30 minutes during a growing season of corn and soybean from May 23 through September 2, 2016. The field site was within the South Fork Watershed at the Sweeney Farms, near the city of Buckeye. The University of Florida L-band Automated Radar System (UF-LARS) observed the backscatter from corn. The brightness temperatures (TB) at the corn site were observed by the University of Michigan L-Band Radiometer (UMLMR), while those at the soybean site were observed by the University of Florida L-band Microwave Radiometer (UFLMR). Concurrent and colocated observations of soil, vegetation, and micro-meteorological conditions were also conducted at both the sites. The passive signatures from both the corn and the soybean sites were found to be similar during the early season, as both the fields were nearly bare terrains. As expected, the TB diverge during the mid-season, when the vegetation water content (VWC) of the corn is about 2 kg/m2. Interestingly, the TB of the two crops are similar again toward the end of the season, when VWC of the soybean crop reaches about 2 kg/m2. Preliminary modeling results show that physically-based emission models significantly underestimate vegetation opacity for a mature soybean canopy. These findings provide insights into retrieval algorithms for soil moisture in agricultural terrains.@en

An ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorporated to the framework, where the active observations were used to optimize surface roughness and update vegetation biomass, while passive observations were used to update SM. The framework was implemented in a rain-fed agricultural region of the southern La-Plata Basin during the 2011-2012 growing season, through a synthetic experiment and AP observations from the Aquarius mission. The synthetic experiment was conducted at a temporal resolution of 3 and 7 days to match the current AP missions. The assimilated estimates of SM in the root zone and dry biomass were improved compared to those from the cases without assimilation, during both 3-and 7-day assimilation scenarios. Particularly, the 3-day assimilation provided the best estimates of SM in the near surface and dry biomass with reductions in RMSEs of 41% and 42%, respectively. The absolute differences of assimilated LAI from Aquarius were <0.29 compared to the MODIS LAI indicating that the performance of assimilation was similar to the MODIS product at a regional scale. This study demonstrates the potential of assimilation using AP observations at high temporal resolution such as those from soil moisture active passive (SMAP) for improved estimates of SM and vegetation parameters.

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