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Two-year community college (CC) students face many barriers for recruitment and retention into Science, Technology, Engineering, and Math (STEM) fields and vertical transfer to 4-year universities (4YUs). Experiential learning, mentoring, and cohort building are effective mechanisms for increasing STEM recruitment and retention, and close collaborations between CCs and 4YUs leverage complementary opportunities, supporting vertical transfer. We present a case study incorporating these concepts for a year-long Geoscience Education and Outreach Program (GEOP), a collaboration among a CC, a 4YU, and a non-profit science center, where 20 CC students participated in integrated academic, research, and internship components over three years. We present program design, implementation, revision, and outcomes for both students and institutions. Cohort-building activities encouraged professional conversations and built peer connections that addressed imposter syndrome, cultural divides, and other personal barriers to vertical transfer. The academic component had the highest completion rate, and a majority of respondents in exit interviews reported the internship as the most valuable experience, with half naming research or aspects thereof as most valuable. The vertical transfer exceeded typical CC rates, with 70% of GEOP students transferring to a 4YU, all in STEM disciplines. Successful implementation of GEOP required multi-institutional coordination, effective mentor-mentor and mentor-student communication, and program flexibility. Based upon our experiences, we provide several recommendations for implementation of similar programs.

This study details the mentored research component of a program intended to recruit, retain, and transfer students attending a two-year college (2YC) to four-year geosciences programs. Eighteen of 20 students who started the program were from minoritized backgrounds: 12 women, six racial/ethnic minorities, 12 low-income, and 13 first-generation college attendees. During a calendar year, students engaged in faculty-mentored research at a 4-year university (4YU), coursework at the 2YC, and a paid six-week internship in geoscience education. Students were to spend at least five hours weekly on research February-June and make a public presentation of results in December. Of 11 students who completed their research projects, 10 were minoritized students. Eight of 11 transferred into a science major. Students progressed the most in research when working together on a project designed for them and regularly meeting in-person with their mentors. Student exit interviews indicated that they valued the research experience and the skills gained. However, less progress occurred in the summer than planned, and students cited challenges in commuting to the 4YU due to jobs and personal commitments. Mentor-student matching produced mixed success. Based on the findings, we recommend incorporating a mini-internship with each mentor into the spring course, then pairing the students with one project and mentor for the summer and fall. Funding the research hours in addition to the internship would help alleviate financial burdens on students. Finally, all mentors would benefit from training together to better understand the mindsets of 2YC students and effectively accommodate individual needs.

Objective: Despite the availability of high-paying, high-demand careers, few women and students from underrepresented racial and ethnic minorities enter undergraduate programs understanding what the geosciences are and associated available career opportunities. This problem is compounded for students from backgrounds underrepresented in the United States. High-impact practices, such as mentoring, internships, undergraduate research experiences, and cohort-building, increase recruitment and retention of underrepresented students in science, technology, engineering, and math at 4-year institutions. What is not yet clear is the impact these interventions have on underrepresented students at two-year colleges, where the STEM pathway has become a main postsecondary school entry point for these students due to the affordability, flexibility, and academic support provided. Therefore, we designed, implemented, and researched a year-long program providing underrepresented students at a two-year college exposure to several of these experiences. Methods: We interviewed program participants about their perceptions and experiences in the program. Analysis proceeded using constant comparison. Results: Participants reported benefits from networking opportunities, gains in confidence, and gains in job skills, while some reported challenges for participation such as communication and time expectation conflicts; participants also struggled to balance the program with employment needs on top of school requirements. Different aspects of the program benefited different students, suggesting that all of these experiences could support recruitment and foster interest in geoscience for underrepresented students at two-year colleges. Conclusion: We conclude with implications for future research, program enhancements, and time constraint and mentoring needs related to characteristics of two-year college students.

Monitoring irrigation is crucial for sustainable water management in freshwater-limited regions. Even though soil moisture (SM)-based inversion algorithms have been widely used to estimate irrigation, scarcity of irrigation records has prevented a thorough understanding of their uncertainties, especially in humid regions. This study assesses the suitability of the SM2RAIN algorithm for estimating irrigation at field scale using high-temporal-resolution data from four corn growing experiments conducted in north-central Florida. Daily irrigation estimates were compared with observations, revealing root mean squared differences of 1.26 to 3.84 mm/day and Nash–Sutcliffe Efficiencies of 0.33 to 0.89. The estimates were more sensitive to uncertainties in static inputs of porosity, saturation moisture and soil thickness than they were to noise in time series inputs. Defining the saturation moisture as porosity made the algorithm insensitive to both parameters, while increasing soil thickness from 40 to 200 mm improved detection accuracies by 34–46%. In addition, the impact of SM on the estimations was investigated based on satellite overpass times. The analysis showed that morning passes produced more accurate estimates for the study site, while evening passes doubled the uncertainty. This study enhances the understanding of the SM2RAIN algorithm for irrigation estimation in subtropical humid conditions, guiding future high-resolution applications. [ABSTRACT FROM AUTHOR]

Given the availability of high-quality and high-spatial-resolution digital elevation maps (DEMs) from the United States Geological Survey's 3D Elevation Program (3DEP), derived mostly from light detection and ranging (lidar) sensors, we examined the effects of these DEMs at various spatial resolutions on the quality of flood inundation map (FIM) extents derived from a terrain index known as Height Above Nearest Drainage (HAND). We found that using these DEMs improved the quality of resulting FIM extents at around 80 % of the catchments analyzed when compared to using DEMs from the National Hydrography Dataset Plus High Resolution (NHDPlusHR) program. Additionally, we varied the spatial resolution of the 3DEP DEMs at 3, 5, 10, 15, and 20 m (meters), and the results showed no significant overall effect on FIM extent quality across resolutions. However, further analysis at coarser resolutions of 60 and 90 m revealed a significant degradation in FIM skill, highlighting the limitations of using extremely coarse-resolution DEMs. Our experiments demonstrated a significant burden in terms of the computational time required to produce HAND and related data at finer resolutions. We fit a multiple linear regression model to help explain catchment-scale variations in the four metrics employed and found that the lack of reservoir flooding or inundation upstream of river retention systems was a significant factor in our analysis. For validation, we used Interagency Flood Risk Management (InFRM) Base Level Engineering (BLE)-produced FIM extents and streamflows at the 100- and 500-year event magnitudes in a sub-region in eastern Texas. [ABSTRACT FROM AUTHOR]

P-band radiometry has been demonstrated to have a deeper sensing depth than L-band, making the consideration of multilayer microwave interactions necessary. In addition, the scattering and phase interference effects are different at the P-band, requiring a reconsideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L- and P-bands is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multilayer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L- and P-bands radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band and 2 K at L-band), correlating strongly with soil moisture variations at 0–10 cm. Findings suggest that coherent and incoherent models performed similarly; thus, incoherent models may be preferable for estimating TB at L- and P-bands due to reduced computational complexity. [ABSTRACT FROM AUTHOR]

Accurate in‐season crop phenology estimation (CPE) using remote sensing (RS)‐based machine‐learning methods is challenging because of limited ground‐truth data. In this study, a biophysical crop model was used to guide neural network (NN)‐based, in‐season CPE. Using the Decision Support System for Agrotechnology Transfer (DSSAT), we conducted uncalibrated simulations for corn (Zea mays L.) across Iowa and Illinois in the U.S. Midwest with in‐season weather and historical information for planting and harvest. We investigated guiding the NN CPE method with growth stage (GSTD) and water stress factor (WSF) outputs from these simulations. Results show that guided NNs are able to estimate onset and progression of phenological stages more accurately than an unguided baseline and a crop model‐only method. GSTD guidance improved CPE during seasons when progress deviated from a regional average because of temperature but was detrimental during seasons of delayed planting and harvest. WSF guidance improved CPE during seasons when planting and harvest were delayed by heavy rainfall but performed less well during grainfill and mature stages. Neural network‐based CPE guided by both GSTD and WSF provided the most accurate estimates for pre‐emergence, emerged, silking, and grainfill stages as well as lower RMSE for the median stage transition date than reported in three full‐season CPE studies. An accurate RS method for estimating planting could link DSSAT simulations to the current planting window and improve upon these results. This model‐guided approach can be extended to other crops and regions to unlock in‐season crop risk assessments that are directly linked to crop phenology. Core Ideas: Remote sensing‐based CPE with machine learning is challenged by limited training data.DSSAT crop model outputs provide additional guidance to NN‐based CPE methods.An NN guided with GSTD and WSF from DSSAT outperforms unguided methods.GSTD improves CPE during seasons with abnormal temperatures; WSF improves CPE during rainfall‐driven delays.More accurate planting date estimates will improve crop model‐guided CPE during delayed seasons. [ABSTRACT FROM AUTHOR]

Height Above Nearest Drainage (HAND), a drainage normalizing terrain index, is a means able of producing flood inundation maps (FIMs) from the National Water Model (NWM) at large scales and high resolutions using reach‐averaged synthetic rating curves. We highlight here that HAND is limited to producing inundation only when sourced from its nearest flowpath, thus lacks the ability to source inundation from multiple fluvial sources. A version of HAND, known as Generalized Mainstems (GMS), is proposed that discretizes a target stream network into segments of unit Horton‐Strahler stream order known as level paths (LPs). The FIMs associated with each independent LP are then mosaiced together, effectively turning the stream network into discrete groups of homogeneous unit stream order by removing the influence of neighboring tributaries. Improvement in mapping skill is observed by significantly reducing false negatives at river junctions when the inundation extents are compared to FIMs from that of benchmarks. A more marginal reduction in the false alarm rate is also observed due to a shift introduced in the stage‐discharge relationship by increasing the size of the catchments. We observe that the improvement of this method applied at 4%–5% of the entire stream network to 100% of the network is about the same magnitude improvement as going from no drainage order reduction to 4%–5% of the network. This novel contribution is framed in a new open‐source implementation that utilizes the latest combination of hydro‐conditioning techniques to enforce drainage and counter limitations in the input data. Plain Language Summary: Flooding is one of the most impactful natural disasters on life and property. The United States National Water Model (NWM) provides flood forecasts to adequately warn people for safe evacuations and protective measures across the entire country. To convert streamflow from the NWM to flood inundation maps (FIM), a model, Height Above Nearest Drainage (HAND), is used that translates elevation data from height above mean sea‐level to height above the nearest river. This model suffers from issues in mapping performance because inundation sourced from rivers is only considered from the nearest river. We developed a technique that mitigates these errors by removing consideration for neighboring tributaries in the relative elevation computation process. This is done by splitting the stream network into continuous river segments known as level paths (LPs) which removes the effects of tributaries. HAND is computed independently for each LP and the resulting FIMs are mosaiced together to form one seamless map. By computing HAND and catchments on an LP scale, catchments and FIMs are allowed to overlap which can account for multiple river sources of inundation especially at river confluences. We compared these HAND derived FIMs to maps from physically‐based models and found improvement in mapping performance. Key Points: Flood maps derived from Height Above Nearest Drainage (HAND) are subject to nearest flowpath limitations that affect inundation skillA means of resolving this limitation is provided by reducing HAND processing units to level paths with effective unit stream orderDiscretizing the stream network for HAND computation affects the stage‐discharge relationship and leads to higher skill inundation [ABSTRACT FROM AUTHOR]

Most crop, hydrology, and water quality models require the simulation of evaporation from the soil surface. A model developed by J.T. Ritchie in 1972 provides useful algorithms for estimating soil evaporation, but it does not calculate the soil water redistribution resulting from evaporation. A physically-based model using diffusion theory, described previously by Suleiman and Ritchie in 2003, provides efficient algorithms for soil water redistribution and soil evaporation. However, the model is appropriate only for second stage drying when the soil in the entire profile being simulated is below the drained upper limit ([[theta].sub.DUL]) and no more drainage occurs due to gravity. This paper extends the Suleiman-Ritchie model for soil water contents higher than [[theta].sub.DUL] where soil evaporation rates are usually higher than second stage drying. New algorithms were developed for these wetter conditions that are functions of soil depth and the wetness of the near-surface soil. New model parameters were calibrated with data measured in laboratory soil column studies. The resulting model was integrated into DSSAT-CSM (Decision Support System for Agrotechnology Transfer Cropping Systems Model). Simulated soil evaporation rates and soil water contents obtained using the Suleiman-Ritchie model with the developed extensions and the previous DSSAT soil evaporation model were compared and evaluated with field measurements of soil water content during several drying cycles for parts of 3 yr in North Central Florida. Computed soil water contents from the model agreed well with the measured soil water contents near the surface, and provided more accurate estimations than the original DSSAT soil evaporation model, especially for the 5-cm surface layer. Abbreviations: [[theta].sub.DUL], Soil water content at drained upper limit ([cm.sup.3] [cm.sup.-3]); CSM, Cropping Systems Model; DSSAT, Decision Support System for Agrotechnology Transfer; ES, evaporation of water from soil surfaces (cm); ESR, Extended Suleiman and Ritchie soil evaporation modal; LAI, Leaf area index ([m.sup.2] [m.sup.-2]); MAE, metal average error; MicroWEX, Microwave, Water, and Energy Balance Experiment; ORD, Original Ritchie DSSAT soil evaporation model; SR, Suleiman and Ritchie method of soil evaporation using diffusion theory.

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. [ABSTRACT FROM AUTHOR]

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's of 5.13 and 4.88 K, using our model and the Jackson model for [tau], respectively. Index Terms--Microwave propagation, remote sensing, vegetation.

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. Index Terms--Microwave, remote sensing, soil moisture.

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. [ABSTRACT FROM AUTHOR]

An Ensemble Kalman Filter (EnKF)-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using a Soil-Vegetation-Atmosphere Transfer (SVAT) model during a complete growing season of corn in Central Mexico. Synthetic and field soil moisture (SM) observations and NASA SMAP SM retrievals were used to understand the effect of vertically spatial updates and uncertainties in meteorological forcings on RZSM estimates. Assimilation of RZSM every 3 days using SM observations at 4 depths lowered the averaged standard deviation (ASD) and the root mean square error (RMSE) by 60 % and 50 %, respectively, compared to the open-loop ASD. The assimilation of synthetic SM at the top 0-5 cm obtained RZSM closer to observations compared to THEXMEX-18 SM measurements and SMAP SM retrievals. Differences between EnKF estimates and SM observations and SMAP SM retrievals are mainly due to misrepresentation of vegetation conditions. The results improved SM estimates up to 10-cm depth using SMAP SM retrievals; however, additional studies are needed to improve SM at deeper layers. The implemented methodology can estimate SM at the top 10 cm of the soil every 3 days to mitigate the impact of the climate change on agricultural production over rainfed areas, particularly in developing countries. [ABSTRACT FROM AUTHOR]

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 VWC and how they affect passive and active 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-day time series of polarimetric L-band backscatter with high temporal resolution. Sub-daily variations of 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 to either internal VWC, surface canopy water or soil moisture variations. Our results showed that internal VWC varied with 10-20 % during the day in non-stressed conditions, and the effect on backscatter was significant. Diurnal variations of internal VWC and nocturnal dew formation affected vertically polarized backscatter most. Moreover, on a typical dry day, backscatter variations were 1.5 (HH-pol) to 3 (VV-pol) times more sensitive to VWC than to soil moisture. 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. [ABSTRACT FROM AUTHOR]

As demands on agriculture increase, food producers will need to employ management strategies that not only increase yields but reduce environmental impacts. Modeling is a powerful tool for informing decision-making about current and future practices. We present a model to evaluate the effects of crop diversification on the robustness of simulated farms under labor shocks. We use an example inspired by the Florida production system of high-value, labor-intensive fruits. We find that crop diversification to high-value crops is a robust strategy when labor shocks are mild, and that crop diversification becomes less valuable as more simulated farms practice it. Based on our results, we suggest that crop diversification is a useful management strategy under specific conditions, but that policies designed to encourage crop diversification must consider broad effects as well as farm-level benefits. [ABSTRACT FROM AUTHOR]

Climate change and variability are major threats to crop productivity. Crop models are being used worldwide for decision support system for crop management under changing climatic scenarios. Two-year field experiments were conducted at the Water Management Research Center (WMRC), University of Agriculture Faisalabad, Pakistan, to evaluate the application of CERES-Maize model for climate variability assessment under semi-arid environment. Experimental treatments included four sowing dates (27 January, 16 February, 8 March, and 28 March) with three maize hybrids (Pioneer-1543, Mosanto-DK6103, Syngenta-NK8711), adopted at farmer fields in the region. Model was calibrated with each hybrid independently using data of best sowing date (27 January) during the year 2015 and then evaluated with the data of 2016 and remaining sowing dates. Performance of model was evaluated by statistical indices. Model showed reliable information with phenological stages. Model predicted days to anthesis and maturity with lower RMSE (< 2 days) during both years. Model prediction for biological yield and grain yield were reasonably good with RMSE values of 963 and 451 kg ha−1, respectively. Model was further used to assess climate variability. Historical climate data (1980-2016) were used as input to simulate the yield for each year. Results showed that days to anthesis and maturity were negatively correlated with increase in temperature and coefficient of regression ranged from 0.63 to 0.85, while its values were 0.76 to 0.89 kg ha−1 for grain yield and biological yield, respectively. Sowing of maize hybrids (Pioneer-1543 and Mosanto-DK6103) can be recommended for the sowing on 17 January to 6 February at the farmer field for general cultivation in the region. Early sowing before 17 January should be avoided due to severe reduction in grain yield of all hybrids. A good calibrated CERES-Maize model can be used in decision-making for different management practices and assessment of climate variability in the region. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 timeaveraged error across the region was found to be 0.01 m 3/m3, with a standard deviation of 0.012 m3/m3 when 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/m3 when LST is unavailable on the day of downscaling. The results indicate that the BRT-based algorithm provides high accuracy for downscaling SMusing complex nonlinear spatiotemporal correlations, under heterogeneous micrometeorological conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

A novel algorithm is developed to downscale microwave brightness temperatures ( \mathrmT_B), obtained at satellite scales of 10–40 to $\leq$1 km, meaningful for agricultural applications. Downscaling \mathrmT_B directly bypasses the errors induced by inverse modeling encountered while downscaling satellite-based soil moisture products. This algorithm is based upon self-regularized regressive models (SRRM) and uses higher order correlations between auxiliary variables, such as precipitation (PPT), land cover, leaf area index, and land surface temperature, and horizontally polarized \mathrmT_B observations. It includes information-theoretic clustering based on auxiliary variables to identify areas of similarity, followed by kernel regression that produces downscaled \mathrmT_B. The algorithm was evaluated using a multiscale synthetic dataset over North Central Florida for one year, including two growing seasons of corn and one growing season of cotton. Compared to the true \mathrmT_B, the downscaled \mathrmT_B had a root-mean-square error (RMSE) of 5.76 K with standard deviation (SD) of 2.8 K during the growing seasons and an RMSE of 1.2 K with an SD of 0.9 K during nonvegetated. The SRRM algorithm effectively captured the variability in \mathrmT_B at 1 km through the auxiliary variables. This algorithm was implemented to downscale SMOS observations available for five days during the SMAPVEX-12 experiment. Spatially averaged root-mean-square difference (RMSD) between the downscaled \mathrmT_B and the airborne \mathrmT_B observations from the airborne passive-active L-band sensor was 6.2 K, with Kullback–Leibler divergences of up to 0.91. For the SMAPVEX-12 dataset, better downscaling results are obtained for days when there was no PPT due to regional biases in the remotely sensed PPT from the NASA Tropical Measurement Mission. The RMSDs were lower when in-situ PPT data were used. [ABSTRACT FROM PUBLISHER]