Understanding the vertical distribution of soil moisture is crucial for making informed decisions in various applications, ranging from precision agriculture to hydrological modeling. Four machine learning algorithms, including random forest, extreme gradient boosting, deep learning, and support vector regression were employed to estimate the soil moisture profile from collected tower-based L-band and P-band brightness temperature observations in Victoria, Australia. The results showed that random forest outperformed the other algorithms, with root mean square error (RMSE) values of 0.03, 0.04, and 0.06 m3/m3for depths of 0-5 cm, 0-30 cm, and 0-60 cm, respectively

Satellite and airborne radars currently monitor agricultural regions on Earth. Corn is a globally important crop that may benefit from radar observations for estimating soil moisture (SM) and other quantities. Estimates of SM could be used to enhance crop yield and aid in weather prediction. A scattering model is needed, however, to accurately estimate these quantities. Historically, corn is a difficult crop to model at microwave frequencies, and only approximate models for it exist. Novel models based on full-wave electromagnetic solvers can be more accurate by accounting for multiple scattering among plant constituents, other adjacent plants, and the underlying soil surface. Such a model is computationally expensive, but the increased availability of computing resources may make it more feasible. This article presents a model for corn at L-band based on finite element method (FEM) simulations in conjunction with Monte Carlo methods to estimate polarimetric backscattering coefficients. The FEM simulation uses periodic boundary conditions to limit its size. The physical representation of the corn plants comes from data-based 3-D plant models. The results of simulations are validated with synthetic aperture radar (SAR) data obtained during the SM active passive validation experiment of 2012 (SMAPVEX12) experimental campaign. The estimated backscattering coefficients of the SAR data are within ±2 dB for all polarization channels. Validation is performed for two days within the experimental campaign. Good agreement is observed between the simulated and measured values. This result indicates that the model can give novel insights into the scattering characteristics of corn. Future work remains to build an invertible model for estimating SM from backscatter measurements.

Soil moisture is a key state variable in environmental monitoring and in the water, energy and carbon cycles [1] - [3] . Farm management decisions, including the timing of planting, application of fertilizers, pesticides, herbicides, and irrigation scheduling, are influenced by soil moisture status [4] , [5] . Moreover, it is highly variable both in space and time and the estimation of this variable is challenging because the amount of moisture in the surficial soil layer is influenced by soil texture [6] , [7] . Passive microwave remote sensing is a well-accepted technique for estimating soil moisture due to the large contrast between the dielectric properties of liquid water (~80) and that of dry soil matter (~3.5) [8] , and reduced sensitivity to surface roughness and vegetation as compared to active microwave [9] . Current missions, including the Soil Moisture and Ocean Salinity (SMOS; [10] ) and Soil Moisture Active Passive (SMAP; [11] ) operating at L-band radiometer (~21 cm) are only able to detect shallow soil moisture (up to 5 cm in depth; [12] ). Compared with L-band, P-band (~ 40 cm) is even less sensitive to vegetation water content [13] and surface roughness [14] , and is able to penetrate deeper into the soil providing information about moisture over deeper depths (~10 cm; [12] ).

This study aims to discuss the use of L-band radar backscatter and phase information to analyze soil moisture (SM) and crop conditions, focusing on corn vegetation. While previous research has primarily utilized total backscatter magnitude to assess SM and crop conditions, this work explores the potential of phase information, which may be more sensitive to crop structure and suitable for monitoring crop dynamics. The methodology involves radar measurements using the University of Florida L-band Automated Radar System (UF-LARS), destructive vegetation sampling, and calibration techniques. Preliminary results demonstrate significant differences in CPD between bare soil and vegetated conditions, with phase variations associated with different growth stages of corn. This ongoing project indicates the potential of phase information to characterize vegetation growth stages for corn and similar crops.

Recently, the efforts to obtain Soil Moisture (SM) global measurements, at high spatial resolution, have been increasing several years ago. As a result, there are many techniques to downscale SM from remote observations of different sensors. However, we have focused on the algorithm which was developed in 2018 by Chakrabarti, using spatio-temporal correlations of high-resolution remote sensing products, bagged regression trees (BRT), and in-situ SM measurements. We computed the algorithm to downscale SMAP Level 2 SM products (L2_SM_P_E) at 9 km to 1 km over agricultural fields in Mexico using optical observations such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Landcover (LC), and precipitation measurements (PPT). We found that the algorithm correctly downscales soil moisture at 1 km over our study area, especially over the corn fields the RMSD is 0.037 m3/m3. Despite the limitations that we encountered when using optical ancillary products due to adverse weather conditions and the difference of spatial and temporal resolutions between each space-borne mission.

The need to remotely obtain Soil Moisture (SM) measurements has been a challenge in recent years. Worldwide, this generates a scientific interest on remote sensing field. As a result, there are many techniques to retrieve SM from remote observations of different sensors, for example, mission SMOS can retrieve soil moisture from Brightness Temperature (TB) measurements at coarse spatial resolution. However, these resolutions for agricultural purposes are not suitable, for this reason, we are focused on the assessment of SM retrievals from the improvement of TB products over agricultural fields with similar weather conditions and crop field characteristics. Initially, we obtained TB at 9km and 3km in H and V polarization from NSIDC over agricultural fields in Mexico. Later, we computed retrieval model to retrieve SM. Finally, we obtained RMSD equal to 0.1 and low Pearson correlation coefficients, such as 0.068 in 2018 at 3 km (the lowest value) and 0.5711 in 2019 at 9 km (the highest value). In addition, BIAS and ubRMSD are similar to each other. Nevertheless, they showed poor performance in the Huamantla crop fields. But this does not limit the application to other regions of Latin America.

The goal of this study was to inform land use planning decisions by incorporating remote sensing time series data and land cover classifications into a suitability analysis. Trends extracted from land cover classifications between the years 2000 and 2020 informed on general land cover trends occurring in the study area, the THLD District of Ghana. Remotely sensed environmental and anthropogenic variables were combined with road and soil data to form the foundations of a suitability analysis. These classifications were then integrated along with time-series remote sensing variables and road and soil data into the suitability analysis, which analyses the validity of land areas for predetermined uses. The preliminary results of the classification show that urban areas have largely expanded throughout the THLD District, while there have been losses in forested areas. The baseline results from the suitability analysis effectively determined transportation accessibility and soil quality for areas across the THLD District. These results provide a strong foundation for the impacts of land use decisions on land cover and environmental trends.

Monitoring corn fields with radar to measure soil moisture and biomass may yield benefits in corn cultivation. To do this, accurate forward models of corn backscatter are needed. Current models approximate the plant geometry with canonical shapes like cylinders and disks. Plant geometry and scattering may be accurately represented with 3D models and an appropriate electromagnetics solver. Using a finite-element method solver with periodic boundaries, qualitative agreement for corn backscatter is shown.

In this study a method for near-real time (nRT) crop progress estimation (CPE) for data-poor regions - those without large scale crop surveys - is proposed. The method utilizes Long Short-Term Memory and is pre-trained on USDA corn crop progress data for the US Midwest using weather and MODIS-derived vegetation index products. Performance of the method is evaluated in different growing zones of Argentina, a major corn exporter, using Bolsa de Cereales corn crop progress data. To establish how the proposed nRT CPE method would perform in regions any ground survey data, evaluation is conducted without prior access to or fine-tuning on Argentinian ground truth crop progress. Initial results from a single growing zone in Argentina indicate that pre-training an LSTM-based nRT CPE method using data from regions with high ground truth data availability may translate to effective nRT CPE in regions where ground survey data are unavailable.

Remote sensing based river extent mapping can be used for detecting inundation extents in retrospective and near-realtime applications. Synthetic aperture radar (SAR) has been proven useful for this purpose due to its high-spatial resolution, low atmospheric attenuation, and self-illumination but suffers from radiometric scattering and unwanted reflections from vegetation, terrain, and anthropogenic features. A novel four step procedure is proposed that augments a standard unsupervised classification of the SAR backscatter values with a terrain index to extract stages. The extracted stages are filtered, exploiting the dendritic nature of river networks, utilizing graph signal processing then remapping the filtered stages using the hydrologically relevant terrain model. Using a reference map derived from in-situ observations, the final inundation product significantly enhances the mapping skill when compared to the segmented SAR only method.

Agricultural drought periods are a phenomenon that affect crops production and health of ecosystems thereby economies suffer continuous imbalances. For this reason, scientific communities have been focused on accuracy global prediction and mitigation risk models. These models are fed with increasingly enhanced inputs such as TB, therefore the need arises to evaluate inputs. This work presents the relationship between Microwave Polarization Difference Index (MPDI) and Soil Water Deficit Index (SWDI), using TB at 9km of SMAP mission to identify drought periods over a rainfed agricultural area in Huamantla (Tlaxcala State in Central Mexico).

In this study, a data assimilation framework based on the Ensemble Kalman Filter was implemented including a soil-vegetation-atmosphere energy transfer (SVAT) model. The SVAT model has been calibrated with in-situ data in the central region of Mexico, with temperate subhumid climate. The soil moisture information from ten locations was scaled within a 36km satellite pixel. Both synthetic observations and SMAP SM retrieval were assimilated and they improved by 29% compared to open-loop simulations. Particularly, the assimilated soil moisture allows us to have a better characterization of periods of water stress for corn cultivation.

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.

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.

The proper management of resources allows better yields from crops. Within the field of remote sensing, one of the sectors benefited is the agricultural sector because changes in biodiversity can be quantified through the observation and temporary evaluation of satellite images. For example, different studies have shown the potential of using information from the ESA Sentinel-1 satellite to monitor the growing crops. The backscatter observations obtained from Sentinel 1A and Sentinel 1B satellites showed greater sensitivity to the change in vegetation according to the correlation study, it is shown that the correlation between vegetation parameters and Sentinel-1 observations is greater than 0.8. The application of this methodology allows the understanding of the temporal variability over corn fields in the central zone of Mexico and allows seeing the potential of the Sentinel-1 constellation for the monitoring of natural resources. The application of this methodology allows the understanding of the temporal variability over corn fields in the central zone of Mexico and allows seeing the potential of the Sentinel-1 constellation for the monitoring of natural resources.

Since 2018, the Terrestrial Hydrology Experiment in Mexico (THExMEX-18) is being part of SMAP algorithm validation through soil moisture (SM), crop measurements, and biomass samples that were collected over a rainfed agricultural region of Huamantla, Tlaxcala. As a second part of this experiment, THExMEX-19 was carried out in whole crop season from March to December 2019. In order to help to validate to SMAP algorithm applying NASA's protocols, both joint efforts of National Polytechnic Institute and Center for Remote Sensing of the University of Florida have obtained these results in 2018 and 2019. In-Situ soil moisture measurements are compared with the enhanced soil moisture SMAP level 2 passive observation (SMAP L2_SM_P & SMAP L2_SM_P_E) and soil moisture SMAP level 2 and Sentinel-1 passive observation (SMAP L2_SM_SP). SMAP mission meets the requirement of SM retrievals with an unbiased root-mean-square difference (ubRMSD)3m-3) compared to in-situ measurements over agricultural fields. The L-band passive SM retrievals and in-situ measurements were evaluated in terms of four statistical metrics: root-mean-square difference (RMSD), bias, ubRMSD, and correlation coefficient (r).

Integrate information of soil moisture obtained through available satellite observations with low implementation cost can help to guarantee food security and sovereignty in Mexican production. With few reliable databases of the behavior of soil moisture during the growth of corn and other crops, the validation of satellite retrievals with Mexican field campaigns it is necessary. In this study, we present the calibration of a SVAT-LSP model during a complete growing season over a corn region in Central Mexico. In-situ soil moisture values and SVAT-LSP estimates of soil moisture were also compared with the SMAP L2SM product. The calibration of the SVAT-LSP model was carried out using the Monte Carlo methodology. The surface soil moisture from the calibrated SVAT-LSP model show an RMSE of 0.0258 m3/m3when compared with in-situ values. In contrast, an RMSE of 0.1331 m3/m3was obtained between in-situ values and SMAP L2SM retrievals.

Accurate knowledge of soil and crop conditions such as water content, biomass, and phenology is crucial in agriculture for estimating growth and productivity. Remote sensing observations at microwave frequencies are sensitive to various soil and crop characteristics. For example, both the active (radar) and passive (radiometer) microwave sensors measure radiation quantities that are functions of soil and vegetation dielectric constant and exhibit sensitivities to soil moisture (SM) and vegetation water content (VWC). In addition to SM, the quantities are also influenced by other land surface parameters such temperature, soil surface roughness, vegetation geometry. Brightness temperature (TB) is more sensitive to SM and less sensitive to surface roughness and vegetation geometry compared to radar backscattering ( σ0). Satellite passive observations have been widely used because of their high temporal resolutions (frequency of every 3 days), but these observations are available at coarse resolutions of about 25-40km. In contrast, satellite active observations from synthetic aperture radar (SAR) provide finer resolution (lower than 3 km), but low temporal resolution. However, with the recent availabilities of both Radarsat-2 and Sentinel-1, we have almost weekly observations combining both observations. In addition, upcoming launch of NISAR mission in 2021 will provide unprecedented opportunities to harness multifrequency observations at L-, S-, and C-bands. In this study, we investigate sensitivities of such multifrequency observations to soil and vegetation water content using current C-band SAR data and ground-based L-band data (in preparation for NISAR) obtained from field experiments. Active L-band observations are the most sensitive to SM variations even when the biomass in agriculture fields such as corn is high; in contrast, active C-band observations are more sensitive to vegetation.

In this study, soil moisture (SM) products from the NASA Soil Moisture Active Passive (SMAP) mission are assessed for their validity in heterogeneous, irrigated agricultural regions. SMAP products at 36 km and at enhanced 9 km resolution are compared with in situ SM data from an operational farm in Florida during the 2018 and 2019 growing seasons for field corn and peanuts. SMAP has a strong positive SM bias which may be caused by unrealistic vegetation water content (VWC) values being used in the SM retrieval algorithm. To determine whether higher spatial resolution VWC information can improve the SM retrieval algorithm, Sentinel-1 derived VWC will be used to form a SM regression model. The model will be calibrated on a Florida research site and tested on the study farm.

A method of physical integration of electromagnetic (EM) interaction models is presented here to estimate the backscattering coefficient (BSC) for L-band, brightness temperature (TB) for L- and C-Band and the reflectance for visible (VIS) and near-infrared (NIR) region for dynamic vegetated terrain. The SPIN (Spectrum Invariant Interaction) model is obtained by solving vector radiative transfer (VRT) equations kernel-based and therefore for different wave interaction mechanisms. To demonstrate its application for the microwave region, the measurements during the 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 PROSAIL model. By applying the SPIN model in the radar regime, it could be 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 horizontal measurements (Pearson 0.45, R20.20). In addition, the modeled brightness temperatures (L- and C-band) in both polarization states are also correlated with the MicroWEX-11 measurements (L-band: Pearson 0.755, R20.57; C-band: Pearson 0.73, R20.53). Finally, the optical results are consistent with the results of other standard optical models (Pearson 0.99, R20.98), like PROSAIL.

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For agricultural applications, SM information is needed at higher resolutions (about 1km). In this study, coarse-scale remotely sensed SM at 36 km from NASA-SMAP was disaggregated to 1 km using high resolution auxiliary information such as land cover, precipitation, land surface temperature, NDVI for a growing season of corn in 2018 in Central Mexico (CM). The main objective is to evaluate a machine-learning based downscaling algorithm over an agricultural area with very limited in-situ observations of SM obtained during THExMEX-18. We found that overall, the downscaled moisture captured the dynamics during the growing season observed by the in-situ measurements.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite was launched in January 2015. In order to validate the soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, the algorithms need to be tested over different conditions worldwide. In response to this need, the Terrestrial Hydrology Experiment 2018 in Mexico (THExMEX-18) was conducted in a rainfed agricultural region of Huamantla, Tlaxcala, Mexico over a six-month growing season. During the experiment, soil moisture, crop measurements, and biomass samples were collected. The objective of THExMEX-18 was to create a soil moisture network over an agricultural area in Mexico, understand the spatial distribution of soil moisture, and compare the SMAP soil moisture retrievals with in-situ measurements. This work details the field data collection as well as data calibration and analysis. A first comparison between in-situ data and SMAP retrievals of soil moisture is presented. It is demonstrated that absolute soil moisture values can be retrieved by satellite observations. SMAP soil moisture estimates closely follow dry down and wetting events observed during the field experiment.

Satellite earth observations provide critical information regarding land, ocean, and atmosphere but are vulnerable to radio frequency interference. It is essential to continue to protect scientific uses of the radio spectrum. Differences in organizational structures and mechanisms of spectrum management among various nations make it challenging for earth scientists to engage in the process. We present several examples and mechanisms that highlight the importance and avenues for engagement of earth scientists in spectrum management.

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For SM studies, observations at microwave frequencies <; 10 GHz are more desirable due to larger penetration depths. The NASA Soil Moisture Active/Passive (SMAP) mission provides global observations of SM using passive sensor at L-band. Current active C-band systems such as Sentinel 1 have shown potentialities to estimate SM and improve spatial resolution of SM data from passive sensors. In addition to the SM sensitivity, radar backscatter is highly sensitive to roughness of soil surface and scattering within the vegetation. Despite much progress in the development of backscattering models, there is still a gap in validating such models under dynamic vegetation conditions such agricultural crops. The goal of this study is to calibrate coherent and incoherent backscattering models for corn and soybean using Sentinel 1 and Radarsat-2 observations.

A novel algorithm is developed to downscale soil moisture (SM), obtained at satellite scales of 10-40 km to 1 km by utilizing its temporal correlations to historical auxiliary data at finer scales. Including such correlations drastically minimizes the size of the training set needed, accounts for time-lagged relationships, and enables downscaling even in the presence of short gaps in the auxiliary data. The algorithm is based upon bagged regression trees (BRT) and uses correlations between high-resolution remote sensing products and SM observations. The algorithm trains multiple RTs and automatically chooses the trees that generate the best downscaled estimates. The algorithm was evaluated using a multiscale synthetic data set in north central Florida for two years, including two growing seasons of corn and one growing season of cotton per year. The time-averaged error across the region was found to be 0.01 m3/m3, with a standard deviation of 0.012 m3/m3when 0.02% of the data were used for training in addition to temporal correlations from the past seven days, and all available data from the past year. The maximum spatially averaged errors obtained using this algorithm in downscaled SM were 0.005 m3/m3, for pixels with cotton land cover. When land surface temperature (LST) on the day of downscaling was not included in the algorithm to simulate “data gaps,” the spatially averaged error increased minimally by 0.015 m3/m3when LST is unavailable on the day of downscaling. The results indicate that the BRT-based algorithm provides high accuracy for downscaling SM using complex nonlinear spatiotemporal correlations, under heterogeneous micrometeorological conditions.

In this paper, we developed and evaluated a phenology-based coherent scattering model to estimate terrain backscatter at the L-band for growing corn. The scattering model accounted for combined effects from periodicity in soil and vegetation, and changes in plant structure and phenology. The model estimates were compared with observations during the two growing seasons in North Central Florida. The unbiased average root-mean-square (rms) differences between the model and observations decreased from 5 to 1.31 dB when these combined effects were included. During the early stage, direct scattering from soil was the primary scattering mechanism, and as the vegetation increased, the interactions between stems and soil became the dominant scattering mechanism. The most sensitive soil parameters were moisture content and rms height, and vegetation parameters were the widths of stems, leaves, and ears, and the stem water content. This paper demonstrates that it is necessary to consider periodicity and plant structural effects in algorithms to retrieve realistic soil moisture in agricultural terrain.

In this study, a data-fusion algorithm is developed for estimation of high-resolution brightness temperatures (TB) at 1km from Soil Moisture Active Passive (SMAP) fine-grid TBproduct at 9km. It uses image segmentation to spatio-temporally cluster the study region based on meteorological and land cover similarity, followed by a support vector machine based regression that computes the value of the high-resolution TBat all pixels. High resolution remote sensing products such as land surface temperature, normalized difference vegetation index, enhanced vegetation index, precipitation, soil texture, and land-cover were used for disaggregation. The algorithm was implemented in Iowa, United States, from May to September 2016, and compared with the field observations of TBfrom Microwave Water and Energy Balance Experiment conducted as a part of the Soil Moisture Active Passive Validation Experiment (SMAPVEX16-MicroWEX). Additionally, they were also compared with the Sentinel downscaled SMAP TBat 1km. High resolution soil moisture is subsequently derived from high resolution TBusing inverse models.

The next World Radio Conference (WRC) will be held in November 2019 in Geneva, Switzerland. This paper discusses WRC-19 agenda items that could impact scientific uses in Earth satellite remote sensing and radio astronomy.

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 (TB) 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 TBfor corn and soybean using ground-based TBobservations. The modeled TBwill be upscaled based upon the USDA crop layer map to compare with the TBobserved in the coarse scales.

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For SM studies, observations at L-band frequencies are more desirable due to larger penetration depths. The NASA Soil Moisture Active/Passive (SMAP) mission includes active and passive sensors at L-band to provide global observations of SM. The active observations are available from April-July 2015. In addition to the SM sensitivity, radar backscatter is highly sensitive to roughness of soil surface and scattering within the vegetation. Despite much progress in the development of backscattering models, there is still a gap in validating such models under dynamic vegetation conditions such agricultural crops. The goal of this study is to validate an incoherent model using season-long active observations over a soybean field at high temporal resolution during the SMAPVEX16-MicroWEX experiment.

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For SM studies, observations at L-band frequencies are more desirable due to larger penetration depths. The NASA Soil Moisture Active/Passive (SMAP) mission includes active and passive sensors at L-band to provide global observations of SM. The active observations are available from April-July 2015. In addition to the SM sensitivity, radar backscatter is highly sensitive to roughness of soil surface and scattering within the vegetation. Despite much progress in the development of backscattering models, there is still a gap in validating such models under dynamic vegetation conditions such agricultural crops. The goal of this study is to improve a coherent model and evaluate it using season-long active observations at high temporal resolution during the SMAPVEX16-MicroWEX experiment.

Radar backscatter from a vegetated surface is sensitive to direct backscatter from the canopy and two-way attenuation of the signal as it travels through the canopy. Both mechanisms are affected by the dielectric properties of the individual elements of the canopy, which are primarily a function of water content. Leaf water content of corn can change considerably during the day and in response to water stress, and model simulations suggested that this significantly affects radar backscatter. Understanding the influence of water stress on leaf dielectric properties will give insight into how the plant water status changes in response to water stress and how radar can be used to detect vegetation water stress. We used a microstrip line resonator to monitor the changes in its resonant frequency at corn leaves, due to variations in dielectric properties. This letter presents the in vivo resonant frequency measurements during field experiments with and without water stress, to understand the dielectric response due to stress. The resonant frequency of the leaf around the main leaf of the stressed plant showed increasing diurnal differences. The dielectric response of the unstressed plant remained stable. This letter shows the clear statistically significant effect of water stress on variations in resonant frequency at individual leaves.

Global Navigation Satellite Systems-Reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multi-static radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from a ground-based and airborne experiments, but studies using space-borne data are still preliminary. This work presents a sensitivity study of Using TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e. vegetation covers). Despite the scattering in the data, which can be attributed to the temporal and spatial (footprint size) collocation mismatch with the SMOS and MODIS NDVI data, and errors in the land use data preliminary results show a good correlation with soil moisture.

In this paper, a novel machine learning algorithm is presented for disaggregation of satellite soil moisture (SM) based on self-regularized regressive models (SRRMs) using high-resolution correlated information from auxiliary sources. It includes regularized clustering that assigns soft memberships to each pixel at a fine scale followed by a kernel regression that computes the value of the desired variable at all pixels. Coarse-scale remotely sensed SM was disaggregated from 10 to 1 km using land cover (LC), precipitation, land surface temperature, leaf area index, and in situ observations of SM. This algorithm was evaluated using multiscale synthetic observations in NC Florida for heterogeneous agricultural LCs. It was found that the rmse for 96% of the pixels was less than 0.02 m3/m3. The clusters generated represented the data well and reduced the rmse by up to 40% during periods of high heterogeneity in LC and meteorological conditions. The Kullback-Leibler divergence (KLD) between the true SM and the disaggregated estimates is close to zero, for both vegetated and bare-soil LCs. The disaggregated estimates were compared with those generated by the principle of relevant information (PRI) method. The rmse for the PRI disaggregated estimates is higher than the rmse for the SRRM on each day of the season. The KLD of the disaggregated estimates generated by the SRRM is at least four orders of magnitude lower than those for the PRI disaggregated estimates, whereas the computational time needed was reduced by three times. The results indicate that the SRRM can be used for disaggregating SM with complex nonlinear correlations on a grid with high accuracy.

In this paper, bias correction approaches are investigated to understand their impact on assimilating active and/or passive microwave observations on near-surface soil moisture (SM) estimates. Synthetic and field observations were assimilated in a soil-vegetation-atmosphere transfer model linked with an integrated active-passive model at L-band for bare soil. The two bias correction methods included in this study are the online bias correction with feedback (BCWF) with extended implementation with nonlinear observation operators and the simultaneous state parameter (SSP) update. New equations for BCWF were derived for the case of nonlinear observation operators because current versions of this approach were not applicable for improving SM by assimilating microwave observations. In SSP, the bias is compensated by tunning the values of the parameters. The two approaches resulted in similar accuracy for improving SM estimates compared with the uncorrected estimates. SSP showed the highest certainty for both synthetic and field observations. Using the bias correction methods, the mean estimates of SM improved by up to 88%, 87%, and 94%, when passive, active, and active-passive synthetic observations were assimilated, respectively, compared with the open-loop estimates. In contrast, when assimilating field observations from the Eleventh Microwave Water Energy Balance Experiment, the mean estimates of SM improved by up to 44%, 18%, and 48%, when passive, active, and active-passive observations were assimilated, respectively, compared with open-loop estimates. The decrement in improving the SM estimates suggests sources of uncertainty other than those from model parameters and forcings.

An novel algorithm is proposed to downscale microwave brightness temperatures (TB), at scales of 10-40 km such as those from the Soil Moisture Active Passive mission to a resolution meaningful for hydrological and agricultural applications. This algorithm, called Self-Regularized Regressive Models (SRRM), uses auxiliary variables correlated to TBalong-with a limited set of in-situ SM observations, which are converted to high resolution TBobservations using biophysical models. It includes an information-theoretic clustering step based on all auxiliary variables to identify areas of similarity, followed by a kernel regression step that produces downscaled TB. This was implemented on a multi-scale synthetic data-set over NC-Florida for one year. An RMSE of 5.76 K with standard deviation of 2.8 K was achieved during the vegetated season and an RMSE of 1.2 K with a standard deviation of 0.9 K during periods of no vegetation.

Leaf dielectric properties influence microwave scattering from a vegetation canopy. The dielectric properties of leaves are primarily a function of leaf water content. Understanding the effect of water stress on leaf dielectric properties will give insight in how plant dynamics change as a result of water stress, and how radar can be used for early water stress detection over agricultural canopies. This paper presents in-vivo measurements of leaf dielectric properties. Different relationships between leaf water content and leaf dielectric properties were found tomato leaves at various heights. The dielectric properties of live stressed and unstressed tomato plants were measured during a controlled, two-week experiment. A clear difference was found between the leaf dielectric properties of stressed and unstressed leaves, which can be attributed to increase in water stress. This results of this study show changes in plant dynamics due to water stress lead to a difference in leaf dielectric properties between stressed and unstressed plants.

In this study, a novel methodology based upon the information-theoretic measures of entropy and mutual information was implemented to downscale soil moisture (SM) observations from 10 km to 1 km. It included a transformation function that related auxiliary remotely sensed (RS) products at high resolution to in situ SM observations to obtain first estimates of SM at 1 km and merging this estimate with SM at coarse resolutions through Principle of Relevant Information (PRI). The PRI-based estimates were evaluated using synthetic observations in NC Florida for heterogeneous agricultural land covers (LC), with two growing seasons of sweet corn and one of cotton, annually. The cumulative density function showed an overall error in SM of <; 0.03 cubic meter/cubic meter in the region, with a confidence interval of 95% during the simulation period. The PRI estimates at 1 km were also compared with those from the method based upon Universal Triangle (UT). The spatially averaged root mean square error (RMSE) aggregated over the vegetative LC were 0.01 cubic meter/cubic meter and 0.15 cubic meter/cubic meter using the PRI and UT methods, respectively. The RMSE for downscaled estimates using the UT method increased to 0.28 cubic meter/cubic meter when Laplacian errors are used, while the corresponding RMSE for the PRI remains the same for both Laplacian or Gaussian errors. The Kullback-Liebler divergence (KLD) for estimates using PRI is about 50% lower than those using the method based upon UT indicating that the probability density function (PDF) of the PRI estimate is closer to PDF of the true SM, than the UT method.

Microwave backscatter from vegetated surfaces is influenced by vegetation structure and vegetation water content (VWC), which varies with meteorological conditions and moisture in the root zone. Radar backscatter observations are used for many vegetation and soil moisture monitoring applications under the assumption that VWC is constant on short timescales. This research aims to understand how backscatter over agricultural canopies changes in response to diurnal differences in VWC due to water stress. A standard water-cloud model and a two-layer water-cloud model for maize were used to simulate the influence of the observed variations in bulk/leaf/stalk VWC and soil moisture on the various contributions to total backscatter at a range of frequencies, polarizations, and incidence angles. The bulk VWC and leaf VWC were found to change up to 30% and 40%, respectively, on a diurnal basis during water stress and may have a significant effect on radar backscatter. Total backscatter time series are presented to illustrate the simulated diurnal difference in backscatter for different radar frequencies, polarizations, and incidence angles. Results show that backscatter is very sensitive to variations in VWC during water stress, particularly at large incidence angles and higher frequencies. The diurnal variation in total backscatter was dominated by variations in leaf water content, with simulated diurnal differences of up to 4 dB in X- through Ku-bands (8.6-35 GHz) . This study highlights a potential source of error in current vegetation and soil monitoring applications and provides insights into the potential use for radar to detect variations in VWC due to water stress.

The ground-based University of Florida L-band Automated Radar System (UF-LARS) was developed to obtain observations of normalized radar backscatter (\mmbσ0) at high temporal resolution for soil moisture applications. The system was mounted on a 25 m manlift with capabilities of antenna positioning for multi-angle data acquisition and ranging. The RF subsystem of UF-LARS was based upon the established designs for ground-based scatterometers employing a vector network analyzer with simultaneous acquisition of V- and H-polarized returns. System integration and automated data acquisition were enabled using a software control system. Fifteen-minute observations of \mmb σ0collected over a growing season of sweet-corn and bare soil conditions in North Central Florida, were used to study the sensitivity of \mmbσ0to growing vegetation and near-surface (0-5 cm) soil moisture (\mmbSM0 - 5). On average, \mmb σ\mmbVV0were observed to be 23% higher than \mmbσ\mmbHH0during the mid- and late-stages of crop growth due to the vertical structure of stems. The correlation between 3-day observations of \mmbSM0 - 5 and \mmbσ\mmbVV0reduced by 55% compared to those obtained for ≤ 30-min observations. These findings suggested that data set at high temporal frequencies can be used to develop more realistic and robust forward backscattering models.

In this study, sensitivity of active and passive (AP) observations at L-band to near-surface SM was analyzed for bare sandy soils. The complementarity of AP microwave observations was used to obtain realistic SM profile that matched well with both AP observations during dynamic moisture conditions. Active observations exhibit less sensitivity to SM changes and higher sensitivity to surface roughness than passive observations. Based upon these findings, the observed brightness temperatures (TBs) were used to estimate a SM profile using an emission model. The backscatter (σ°) observations were used to estimate root mean square height (s) and correlation length (cl) using a backscatter model. The estimated SM profile, s, and cl resulted in RMSDs of 4.55K and 0.81dB between the observed and modeled TBand σ° values, respectively, for the rough surface. This study demonstrates the integrated use of AP to improve SM estimates.

In this letter, near-surface and root-zone soil moisture (RZSM), land surface temperature (LST), leaf area index, and vegetation water content were simulated at different spatial scales for three land cover types in North Central Florida under dynamic vegetation conditions. Insights into expected retrieval errors in soil moisture (SM) due to assumptions of static landscape were obtained from differences in the estimates using the static and dynamic land covers. Maximum differences of about 0.04 m3/m3in near-surface SM and RZSM, and 5.1 K in LST were observed between estimates obtained over the vegetated and bare-soil regions during dry-soil conditions. During wet conditions, the maximum differences in near-surface SM and RZSM increased to about 0.05 m3/m3, while those in LST decreased to 3.6 K. The RZSM simulations generated at the two resolutions of 200 m and 10 km were used to implement an upscaling algorithm based on averaging, to illustrate the use of the synthetic data set for upscaling studies. This letter highlights the importance of simulating land surface states at multiple scales for heterogeneous landscapes under dynamic vegetation conditions and for developing accurate SM retrieval and scaling algorithms.

The electromagnetic spectrum is a valued shared resource. Its scientific use allows us to learn about our universe, measure and monitor our planet, and communicate scientific data. The use of the spectrum is managed by national, regional, and global regulatory frameworks. There are increasing demands for new or extended allocations because of vast technological advances in the past few years. Understanding spectrum management is important in the successful planning and execution of missions and instruments, as well as in determining the potential source of radio frequency interference in existing data and instruments, and in working to ameliorate its impact. This paper provides a summary of this framework for radio scientists and engineers.

In this paper, L-band microwave observations were assimilated using the ensemble Kalman filter to improve root-zone soil moisture (RZSM) estimates from a coupled soil vegetation atmosphere transfer (SVAT)-vegetation model linked to a forward microwave model. Simultaneous state-parameter updates were performed by assimilating both synthetic and field observations during a growing season of sweet corn every three days, matching the temporal coverage of observations from the Soil Moisture and Ocean Salinity and Soil Moisture Active Passive missions. The sensitivities of parameters to the states were investigated using the information-theoretic measure of conditional entropy. Among the soil parameters, the pore-size index (λ) was the most sensitive to brightness temperatures (TB) during the early and midgrowth stages, while porosity (φ) was the most sensitive toTBduring the reproductive stage. In the microwave model, the soil roughness parameters, root mean square (RMS) height (r), and correlation length (l) were the most sensitive during the early and mid stages, while the vegetation regression parameter (b) was the most sensitive during the reproductive stage. In the synthetic experiment, assimilation ofTBprovided RMS error reductions in RZSM estimates of 70% compared to open loop estimates. Minimal variations in performance were observed across different stages of the season during the synthetic experiment. However, when field observations ofTBwere assimilated, significant differences in RZSM estimates were observed during different growth stages. Maximum RMS difference (RMSD) reductions in RZSM estimates of 33.3% were observed compared to open loop estimates during the early stages, while improvements of 4.8% and 16.7% were observed in the mid- and reproductive stages, respectively. Further analyses of assimilation with field observations also suggest some improvements in the SVAT model are needed for moisture transport immediately following the precipitation/irrigation events. In the microwave model, the linear vegetation formulation for estimating canopy opacity, parameterized byb, was inadequate in capturing the complexities inTBduring stages of high vegetative and reproductive growth rates.

The impact of incoherent and coherent formulations on estimates of terrain backscatter (σterrain0) at L-band for a growing season of corn is examined. The average root mean square difference (RMSD) between the two formulations over the growing season ranged between 3-4 dB, with higher RMSDs at HH polarization (pol), indicating the presence of coherent effects. In the incoherent model, the direct scattering from stems was the primary mechanism, while in the coherent formulation, the interactions between the stems and soil were the primary mechanisms due to the coherent effects. Both incoherent and coherent formulations estimated equally high sensitivities of σterrain0to soil moisture (SM) during early stage under low vegetation conditions. During the early and mid stages, the σterrain0estimated by both formulations exhibited higher sensitivities during dry conditions than wet conditions. In contrast, during the reproductive stage, the σterrain0by the incoherent formulation was more sensitive to the SM at wet conditions than at dry conditions. Based upon the ALOS/SMAP accuracy for σterrain0, the incoherent formulation exhibited the highest sensitivity during the early stage with detection of SM changes as low as 2 vol% for dry condition, whereas the coherent formulation exhibited the highest sensitivity during the mid stage with detection of SM changes as low as 2.5 vol%. The results of this study suggest that the coherent effects should be considered for defining accuracy of SM estimation algorithms for corn at L-band.

Microwave emission and scattering models require the parametrization of surface roughness. Traditionally this has been achieved by sampling the surface in transects. In this work, roughness is characterized from 3D surface models derived from ground-based LiDAR. The dataset consist 18 surfaces with varying roughness characteristics. 2D profiles extracted from the surface model constitute the baseline to compare to traditional profiling methods. It was found that sampling using profiles produces an underestimation of the RMSh by 25–63% and an even more severe underestimation in the correlation length that can reach up to an order of magnitude difference. From the 17,178 2D extracted profiles it was determined a significant sensitivity of the roughness parameters to the detrending methods, as well as a poor fit between the experimental ACF and the exponential and Gaussian models. Finally, methodologies to detrend quasi-periodic surfaces and the decomposition of surface at different scales are proposed and illustrate the advantage of having a 3D representation.

In this paper, we develop a microwave brightness model for a growing season of cotton (MB-Cotton) and evaluate it by comparing modeled brightness temperatures at C-band with ground-based observations during an extensive field experiment. Overall, the phases of the modeled TBmatched well with those observed but the diurnal amplitudes were underestimated when observed soil and vegetation conditions were used as inputs. The model estimates matched better with the observations when it was linked with a Land Surface Process model. The non- scattering canopy assumption provided reasonable estimates even during the late season.

We quantify the performance of three commonly used techniques to calibrate ground-based microwave radiometers for soil moisture studies, external (EC), tipping-curve (TC), and internal (IC). We describe two ground-based C-band radiometer systems with similar design and the calibration experiments conducted in Florida and Alaska using these two systems. We compare the consistency of the calibration curves during the experiments among the three techniques and evaluate our calibration by comparing the measured brightness temperatures (TBs) to those estimated from a lake emission model (LEM). The mean absolute difference among the TBs calibrated using the three techniques over the observed range of output voltages during the experiments was 1.14 K. Even though IC produced the most consistent calibration curves, the differences among the three calibration techniques were not significant. The mean absolute errors (MAEs) between the observed and LEM TBs were about 2-4 K. As expected, the utility of TC at C-band was significantly reduced due to transparency of the atmosphere at these frequencies. Because IC was found to have a MAE of about 2 K that is suitable for soil moisture applications and was consistent during our experiments under different environmental conditions, it could augment less frequent calibrations obtained using the EC or TC techniques

In this paper, we develop a model for estimating canopy opacity tau for sweet corn. We estimate the refractive index based upon moisture distribution in the corn during different stages of growth. The moisture distribution was observed during two season-long field experiments. We found that the moisture content decreased linearly as the height of the corn increased, with the distribution closer to Gaussian in the fruit region during reproductive stages. The tau obtained from our model was compared to that estimated using a widely used Jackson model. In general, our tau estimates were higher than those obtained using the Jackson model, with a root mean-square difference (rmsd) of up to 0.23 Np between the two models. The tau values were used in a microwave emission model at C-band, and the model estimates of brightness were compared with field observations. We found that the model brightness temperatures matched well with observations, with rmsd values of 5.13 and 4.88 K, using our model and the Jackson model for tau, respectively.

In this paper, we develop a canopy opacity model for sweet corn. The model, calculated using a simple refractive emission model, is based upon observed moisture distribution in the corn canopy for vegetative and reproductive stages during our Fourth Microwave Water and Energy Balance Experiment (MicroWEX-4). The moisture content decreases linearly with height of the corn height during the vegetative stages. During the reproductive stage, the moisture distribution in the fruit region can be estimated as Gaussian, with the spread dependent on grain biomass. The optical depth increased during the growing season to a maximum of 1.34 on DoY 153, corresponding to the canopy biomass of 3.48 kg/m2and canopy height of 1.72 m.

For accurate prediction of weather and near-term climate, root-zone soil moisture is one of the most crucial components driving the surface hydrological processes. The microwave brightness at low frequencies is very sensitive to soil moisture in the top few centimeters in most vegetated surfaces. The first Microwave Water and Energy Balance Experiment (MicroWEX-1) was conducted to gain better understanding of the interactions among microwave brightness, moisture, and energy fluxes at the land surface for a growing season of cotton. During the experiment, we observed the microwave brightness temperatures at 6.7 GHz and micrometeorological parameters. In this paper, we describe the polarization dependence of observed brightness temperatures; explore the relation of the brightness temperatures changes in soil moisture, ET, and vegetation properties during the growing season. We found that vertically polarized (V-pol) brightness temperatures were less sensitive to the changes in biomass and soil moisture than horizontally polarized (H-pol) temperatures. During the early glowing season, the brightness temperatures were polarization dependent and the temperatures became polarization independent as the biomass increased above 1 kg/m/sup 2/.

In this paper we compare three techniques typically used for calibrating a microwave radiometer and understanding its design stability. The first calibration technique utilizes cold and hot load measurements to construct calibration curves. For our case, we used sky as the cold load and microwave absorber at ambient temperature as hot load. The second calibration technique utilizes measurements of the brightness temperature of the sky at various zenith angles to determine the atmospheric opacity needed for the calibration curves. The third calibration technique utilizes measurements of internal reference load and micro-controller inside the radiometer at both polarizations to estimate the system gain fluctuations and the receiver noise temperatures. We calibrated our University of Florida C-band Microwave Radiometer (UFCMR) every two weeks during the first Microwave Water and Energy Balance Experiment (MicroWEX-1). We found that, the first and third techniques produced similar calibration results with 1 Kelvin/volt standard deviation. However, the third technique was the most stable during the entire experiment with the smallest standard deviations which were 2.41 Kelvin/volt for the slope of the calibration curves at H-pol and 7.46 Kelvin/volt for the slope of the calibration curves at V-pol during MicroWEX-1

The electronically scanned thin array radiometer (ESTAR) was utilized for soil moisture mapping during the Southern Great Plains Experiment (SGP99). A retrieval algorithm was applied to obtain soil moisture from passive microwave measurements at 1.4 GHz. The algorithm was verified using ground data collected during SGP99. The results indicate a good correlation between observed and predicted soil moisture values and are consistent with results obtained from the same instrument in previous experiments. The present results demonstrate the validity of the retrieval algorithm for moderately to extremely dry soils. The ESTAR measurements along with ancillary data were used to create soil moisture maps of the entire region.

The authors compare ground-based and the special sensor microwave/imager (SSM/I) brightness temperatures at 19 and 37 GHz in the Northern and the Southern Great Plains. The comparison was conducted to examine season-related differences in plot-scale and satellite footprint-scale brightness temperatures at these frequencies. The ground-based observations were from the three Radiobrightness Energy Balance Experiments (REBEXs), viz., REBEX-1, REBEX-4, and REBEX-5. REBEX-1 and REBEX-4 were conducted near Sioux Falls, SD, in fall and winter 1992-93, and in summer 1996, respectively. REBEX-5 was conducted near Lamont, OK, during summer 1997 as part of the Southern Great Plains Hydrology Experiment-1997 (SGP'97). The instantaneous fields of view (FOV) of the ground-based radiometers were only a few meters compared to those of the SSM/I, which were several tens of kilometers. The REBEX and the SSM/I brightness temperatures are moderately correlated at both the 19 and 37 GHz. They match well during winter when there was uniform snow cover over the SSM/I footprint. During spring, summer, and fall, REBEX brightness temperatures at the grass-site were on average 18 K higher than the SSM/I brightness temperatures because the SSM/I footprint included nearby agricultural fields in summer and predominantly bare soil in fall and spring. During summer, REBEX-4 brightness temperatures at the bare soil site were on average 10 K cooler than the SSM/I brightness temperatures. In effect, the REBEX grass and bare soil brightness temperatures bracket the SSM/I observations with the SSM/I brightness temperatures lying closest to those of the bare soil.

The authors' point-scale Land Surface Process/Radiobrightness (LSP/R) model for a prairie grassland in the northern Great Plains was adapted to winter wheat-stubble within the region of the Southern Great Plains 1997 (SGP'97) Hydrology Experiment. The model maintains running estimates of near-surface soil moisture and stored water in soil and vegetation when forced by weather, and predicts the microwave brightness of the terrain. LSP/R model predictions were compared with the field observations recorded during SGP'97. The model captures canopy and soil temperatures very well, with the maximum mean and variance of the difference between the model and field temperatures being 1.06 K and 3.28 K/sup 2/, respectively. It yields reasonable predictions for the moisture in deeper layers of the soil, but its predictions for the moisture in the upper layers are low by /spl sim/2.3% by volume. These underpredictions of near-surface soil moisture result in higher H-pol brightnesses at 19 GHz than those observed.

Data from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) have been used to classify snow-free soils in the northern Great Plains as either frozen or thawed. The technique is based on differing sensitivities among SMMR radiobrightness frequencies to liquid moisture and volume scattering in the upper few millimeters of bare soil. The SMMR is no longer active. A current near-equivalent is the Special Sensor Microwave/Imager (SSM/I). The authors demonstrate that SSM/I radiobrightnesses also exhibit differential sensitivities to liquid water and volume scattering in frozen soil despite their higher frequencies. They find that the best classification discriminants for SSM/I data are a combination of the 37-GHz V-pol radiobrightnesses and the 19-to-37-GHz V-pol spectral gradients. They also examine the sensitivity of the classification to atmospheric emission and absorption and find little effect.