The Real-Time In-Situ Soil Monitoring for Agriculture (RISMA) dataset is a collection of publicly available high-quality soil volumetric water content (VWC), soil temperature, and meteorological data for agricultural regions in Manitoba, Saskatchewan, and Ontario, Canada. The RISMA network was established beginning in 2011, and data collection continues at the time of publication. Currently, datasets are available for 36 VWC monitoring stations, where sensors are located within annually cropped and pasture sites. Available data varies depending on location but include soil VWC and soil temperature from surface to as deep as 1.5 m, rainfall, air temperature, relative humidity, wind speed, wind direction, and solar radiation. The RISMA stations cover a wide variety of soil types, from clay and clay loams to sandy loams and sand. The data are processed using an automated script which includes a quality control process. This dataset is valuable for researchers working in agriculture, soil science, meteorology, and remote sensing. Data are used to calibrate and validate remote sensing products as well as hydrological, meteorological, and agricultural models. Sites within Manitoba were extensively detailed as core validation sites for NASA’s Soil Moisture Active Passive (SMAP) satellite.

The RADARSAT Constellation Mission (RCM) performance evaluation is currently in progress for core Synthetic Aperture Radar (SAR) applications. This study aims to investigate the retrieval of Soil Moisture Content (SMC) in bare soil with RCM compact polarimetry and Random Forest Regression (RFR). The focus is on RH (right circular transmit and linear horizontal receive signal) and RV (right circular transmit and linear vertical receive signal) backscattering, which are the primary RCM Compact Polarimetric (CP) products. SMC retrieval is pursued over a wide range of radar incidence angles. Then, an attempt is made to retrieve SMC at higher radar incidence angles only. Furthermore, soil moisture maps are produced and used for analyzing the captured soil moisture variability. CP SAR images acquired with the RCM SC30MCP mode over three Canadian experimental sites are considered in our study. The sites are equipped with calibrated Real-Time In-Situ Soil Monitoring for Agriculture (RISMA) stations. A RFR retrieval algorithm was able to predict SMC with a correlation of 0.75 when compared to in-situ soil moisture measurements. A Root Mean Square Error (RMSE) = 5.9%, a bias = -1.5%, and an unbiased RMSE (ubRMSE) = 5.7% are achieved. A degradation in performance is reported for SMC retrieval under higher radar incidence angles. Results of our study indicate promising performance for capturing near-surface soil moisture variability under bare soil conditions. L'& eacute;valuation du rendement de la mission de la Constellation RADARSAT (MCR) est actuellement en cours pour les principales applications des radars & agrave; synth & egrave;se d'ouverture (ROS). Cette & eacute;tude vise & agrave; & eacute;tudier l'estimation de la teneur en eau du sol de sols nus avec la polarim & eacute;trie compacte MCR et la r & eacute;gression Random Forest. L'accent est mis sur la r & eacute;trodiffusion DH (polarisation circulaire dextrorsum et signal de r & eacute;ception horizontale lin & eacute;aire) et DV (polarisation circulaire dextrorsum droite et signal de r & eacute;ception verticale lin & eacute;aire), qui sont les principaux produits polarim & eacute;triques compacts. L'estimation de la teneur en eau du sol consid & egrave;re une large gamme d'angles d'incidence radar. Ensuite, une tentative est faite pour estimer la teneur en eau du sol avec seulement les angles d'incidence radar les plus & eacute;lev & eacute;s. De plus, des cartes de l'humidit & eacute; du sol ont & eacute;t & eacute; produites et utilis & eacute;es pour analyser la variabilit & eacute; de l'humidit & eacute; du sol estim & eacute;e. Les images polarim & eacute;triques compactes acquises avec le mode SC30MCP de la MRC sur trois sites exp & eacute;rimentaux canadiens ont & eacute;t & eacute; prises en compte dans notre & eacute;tude. Les sites & eacute;taient & eacute;quip & eacute;s de stations & eacute;talonn & eacute;es de surveillance in situ des sols agricoles. Le mod & egrave;le de r & eacute;gression Random Forests a & eacute;t & eacute; en mesure de pr & eacute;dire la teneur en eau du sol avec une corr & eacute;lation de 0,75 par rapport aux mesures in situ de de teneur en eau du sol. On a obtenu une erreur quadratique moyenne de 5,9 %, avec un biais de -1,5 % et une erreur quadratique moyenne non biais & eacute;e de 5,7 %. Le mod & egrave;le a & eacute;t & eacute; moins performant quand seulement les angles d'incidence radar & eacute;lev & eacute;s sont utilis & eacute;s. Les r & eacute;sultats de notre & eacute;tude indiquent des performances prometteuses pour l'estimation de la variabilit & eacute; de la teneur en eau du sol pr & egrave;s de la surface dans des conditions de sol nu.

To take full advantage of cloud-free optical remote sensing data for crop leaf area index (LAI) retrieval throughout the growing season, integration of multi-sensor satellite data is increasingly resorted. However, the consistencies of LAI products derived from different satellites using different retrieval approaches should be assessed. This study aimed at understanding the spatiotemporal consistency of crop LAI derived from S2/MSI (Multi Spectral Instrument, Sentinel-2) and L8/OLI (Operational Land Imager, Landsat 8) data using two retrieving approaches, a field-data-driven (FDD) approach driven by field measured LAI and a hybrid approach based on simulations of the PROSAIL radiative transfer model. Results from the two approaches were assessed using field measured LAI for six types of crops in 2016 over Manitoba, one of the major agricultural provinces in the Canadian Prairies. LAI estimates from S2/MSI data obtained unbiased root-mean-square-error (ubRMSE) of 0.39 and 0.51 cm(2) cm(-2) for the FDD and hybrid approaches respectively. For the L8/OLI, ubRMSE of LAI estimates were 0.82 and 0.92 cm(2)cm(-2) for the FDD and hybrid approaches, respectively. To evaluate the temporal consistency of crop LAI derived from the two satellites, daily crop LAI was reconstructed using the Parametric Double-Hyperbolic Tangent model (PDHT) with the aid of Bayesian statistical approach. Time series crop LAI of the hybrid approach had lower variability of estimated parameters in the PDHT and reconstructed daily LAI compared with that of the FDD approach. Based on the Spatial Efficiency Metric (SPAEF) that is a spatial performance metric for assessing spatial pattern similarity, the hybrid approach obtained relatively strong spatial consistency of LAI derived from the two satellites compared with the FDD approach. The study revealed that hybrid approach can achieve good spatiotemporal consistencies in seasonal LAI estimates from the two satellites combined when large numbers of field measurements are not available.

Ongoing evaluation of the soil moisture active passive (SMAP) soil moisture products has utilized validation networks distributed in several regions around the world. The in situ reference used for validation of the soil moisture retrieval algorithm is associated with measurements from soil moisture probes typically located at 5 cm beneath the soil surface; however, some networks also consider a vertically oriented probe that measures from 0 to 5 cm. In this study, we compare the correlation and unbiased root mean square error (ubRMSE) from the SMAP L2 radiometer soil moisture product when compared to in situ measurements taken at 5 cm (approximately 3.5–6.5 cm) below the surface and measurements taken as an integrated measure from 0 to 5.7 cm. The data were obtained from two SMAP validation networks in Canada: the Kenaston network in Saskatchewan and Carman network situated in Manitoba. At both sites, correlations between the in situ and the SMAP L2 product were consistently higher with vertically oriented probes following rain events. With respect to the ubRMSE, the vertically oriented probes at the Carman site had lower ubRMSE with the SMAP product than the horizontal probes that are currently used for validation activities. In some cases, vertical probe information should be considered in validation approaches when this data is available and could be considered in the design of in situ calibration/validation networks. These results may be useful in design considerations of networks for upcoming soil moisture product validation.

This study investigates soil moisture retrievals using airborne passive microwave data at two different resolutions collected during the Soil Moisture Active Passive Validation Experiments in 2012 and 2016 (SMAPVEX12 and SMAPVEX16-MB). Based on the fine-resolution passive data (500 m), we integrate the surface roughness parameters which are traditionally used in the radar backscatter models into the passive emission models. To parameterize the effective roughness Hr in L-band Microwave Emission of the Biosphere (L-MEB) model, we analyze two different functions which include only surface Root Mean Square height (s), as well as both s and the autocorrelation length l (zs = s2/l). For each of the two roughness functions, the b vegetation parameters are optimized for canola, soybean and wheat. The transferability of the b parameters between 2012 and 2016 is also evaluated by comparing the L-MEB model simulated and the measured brightness temperature. Then, the calibrated L-MEB model is applied to the subpixels of the coarse-resolution passive data (1500 m) given the vegetation heterogeneity, to map the soil moisture over the SMAPVEX entire experimental site. The results indicated the Hr model with the zs parameter outperformed that with the s parameter. This suggests that the inclusion of the roughness autocorrelation length in the L-MEB model improved the accuracy of modeling the brightness temperature. The vegetation attenuation on the brightness temperature at V-polarization was stronger than that at H-polarization, due to the dominant vertical structure of the crop canopy. Since the airborne passive observations exhibited remarkable consistency between the 2012 and 2016 measurements, the b parameters obtained in 2012 can be transferred to the 2016. Based on the obtained Hr and b parameters, the soil moisture maps were retrieved using the calibrated L-MEB model applied to the sub-pixel of the coarse-resolution passive data, implying a Root Mean Square Errors (RMSEs) of 0.049–0.058 m3/m3 and correlation coefficients of 0.82–0.87. This paper suggests that the physical roughness zs in the radar domain can be coupled into the L-MEB model to refine the soil moisture retrievals from passive brightness temperature.

We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types.

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

In order to validate its soil moisture products, the NASA Soil Moisture Active Passive (SMAP) mission utilizes sites with permanent networks of in situ soil moisture sensors maintained by independent calibration and validation partners in a variety of ecosystems around the world. Measurements from each core validation site (CVS) are combined in a weighted average to produce an estimate of soil moisture at a 33-km scale that represents the SMAP's radiometer-based retrievals. Since upscaled estimates produced in this manner are dependent on the weighting scheme applied, an independent method of quantifying their biases is needed. Here, we present one such method that uses soil moisture measurements taken from a dense, but temporary, network of soil moisture sensors deployed at each CVS to train a random forests regression expressing soil moisture in terms of a set of spatial variables. The regression then serves as an independent source of upscaled estimates against which permanent network upscaled estimates can be compared in order to calculate bias statistics. This method, which offers a systematic and unified approach to estimate bias across a variety of validation sites, was applied to estimate biases at four CVSs. The results showed that the magnitude of the uncertainty in the permanent network upscaling bias can sometimes exceed 80% of the upper limit on SMAP's entire allowable unbiased root-mean-square error (ubRMSE). Such large CVS bias uncertainties could make it more difficult to assess biases in soil moisture estimates from SMAP.

The availability of Landsat 8 and Sentinel-2 has led to a steady increase in both temporal and spatial resolution of satellite data, offering new opportunities for large-scale crop condition monitoring and crop yield mapping. This study investigated the potential of using Landsat 8 and Sentinel-2 data from the harmonized Landsat 8 and Sentinel-2 (HLS) products for crop biomass estimation for six crops in Manitoba, Canada. Crop biomass was estimated using remotely sensed leaf area index (LAI) to reparametrize a simple crop growth model. The results showed that the LAI of six different crops can be estimated using a generic relationship between LAI and red-edge based vegetation indices (VIs, e.g., modified simple ratio red-edge (MSRRE) and red-edge normalized difference VI (NDVIRE)) for the Multispectral Instrument (MSI) of Sentinel-2. For the Operational Land Imager of Landsat 8 without the red-edge band, LAI can be best estimated using a VI derived from Near-infrared (NIR) and short-wave infrared (SWIR) bands (Normalized Difference Water Index, NDWI1). Above-ground dry biomass of these six crops was more accurately estimated from the assimilation of LAI derived from both satellites (R-2 (the coefficient of determination) = 0.81, RMSE (the root-mean-square-error) = 135.4 g/m(2), nRMSE (the normalized RMSE) 37.9%, RPD (the ratio of percent deviation) = 2.26) than that of LAI derived from MSI-data (R-2 = 0.80, RMSE = 136.7 g/m(2) , nRMSE = 38.3%, RPD = 2.23) or that from LAI derived from OLI-data (R-2 = 0.68, RMSE = 191.0 g/m(2), nRMSE = 53.5%, RPD = 1.16). Further analysis showed that these three assimilation cases (MSI and OLI; MSI alone; OLI alone) with a different number of LAI observations resulted in differences in parameter optimization, particularly the parameters relevant to crop phenology and biomass partitioning. Both crop growth stage (e.g., the emergence date for crop growth) and leaf dry biomass estimated from the assimilation of LAI derived from MSI and OLI, or MSI alone, produced the most accurate estimates. These results are likely attributed to the improved temporal coverage associated with Sentinel-2 and the availability of a red-edge band on this sensor.

In order to calibrate and validate the SMAP soil moisture products, networks of ground-based soil moisture sensors have been deployed. Measurements collected from the networks must be upscaled to the radiometer footprint scale (30-40 km) for comparison with the SMAP radiometer-based retrievals. The upscaling is typically performed as a weighted average of individual sensor measurements within the SMAP grid. Since different weighting schemes have been found to result in different upscaled soil moisture estimates, an independent method of assessing soil moisture estimation biases is needed. We therefore present a method for calculating estimation biases at each SMAP Core Validation Site (CVS). The estimation was enabled by networks of enhanced soil moisture sampling that were deployed at four CVSs for a limited time. Based on Random Forests, our method offers a straightforward, systematic, and unified approach to bias estimation across a variety of sites. The method was applied to estimate biases at the four SMAP CVSs.

Soil moisture is a key variable in Earth systems, controlling the exchange of water andenergy between land and atmosphere. Thus, understanding its spatiotemporal distribution andvariability is important. Environment and Climate Change Canada (ECCC) has developed a newland surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS landsurface scheme features sophisticated parameterizations of hydrological processes, including watertransport through the soil. It has been shown to provide more accurate simulations of the temporaland spatial distribution of soil moisture compared to the current operational land surface scheme.Simulation of high resolution soil moisture at the field scale remains a challenge. In this study, wesimulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme overan experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps wereproduced between May and November 2015. Simulated soil moisture values were compared withestimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture andAgri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic ApertureRadar (SAR) imagery. Statistical analysis of the results showed an overall promising performanceof the SVS land surface scheme in simulating soil moisture values at high resolution scale.Investigation of the SVS output was conducted both independently of the soil texture, and as afunction of the soil texture. The SVS model tends to perform slightly better over coarser texturedsoils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulatedSVS soil moisture and the retrieved SAR soil moisture lie between 0.753–0.860 over sand and 0.676-0.865 over clay, with goodness of fit values between 0.567–0.739 and 0.457–0.748, respectively. TheRoot Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. Theunbiased RMSD values lie between 0.038–0.057 over sand and 0.039–0.064 over clay. Furthermore,results show an Index of Agreement (IA) between the simulated and the derived soil moisturealways higher than 0.90.

Knowledge of crop phenology assists in making agricultural decisions such as appropriate irrigation and fertilization applications in order to optimize crop yield. The objective of this study is to monitor crop phenology using Synthetic Aperture Radar (SAR) polarimetric decompositions and a random forest algorithm applied to a multi-temporal RADARSAT-2 dataset, acquired during the Soil Moisture Active Passive (SMAP) Validation Experiment 2016 in Manitoba (SMAPVEX16-MB). The model-based and eigen-based polarimetric parameters are used to separate the vegetation and soil scattering contributions in the total radar signal. As the crop morphological shape and structure vary with phenological growth, our study assumes that the polarimetric parameters related to the volume scattering mechanism have the potential to track the crop phenology. The sensitivity of the polarimetric parameters to the ground identified crop phenology is analyzed for different crop types. For canola, a single polarimetric parameter is sufficient to characterize the crop phenology, due to the high volume scattering power and large temporal dynamic. For corn, soybean and wheat, combinations of multiple polarimetric parameters are required. For each crop type, the Random Forest algorithm trained using 60% of the data is used to retrieve the, crop phenology. Performances are compared to Artificial Neural Network, Support Vector Machine Regression and k-Nearest neighborhood algorithms. The Random Forest algorithm provides the best phenology retrieval with significant (p-value < 0.01) spearman correlation coefficients (between the retrieved and ground identified phenology) of 0.93, 0.90, 0.85 and 0.91 for canola, corn, soybean and wheat, respectively. While a single polarimetric parameter demonstrates limited sensitivity to corn phenology, the retrieved phenology from the Random Forest algorithm using multiple polarimetric parameters agrees well with the ground measurements. Furthermore, the importance of different polarimetric parameters for phenology retrieval using the Random Forest algorithm is quantified for different crop types. These findings will be of interest in developing future analytical retrieval models.

The objective of this study is to investigate the retrieval of crop phenology using polarimetric decompositions and Random Forest (RF) algorithms. To realize this objective, we used multi-temporal RADARSAT-2 data and ground measured vegetation characteristics acquired during the SMAPVEX16-MB (Soil Moisture Active Passive Validation Experiment 2016 in Manitoba) campaign in Canada. Polarimetric parameters with the potential to quantify the volume scattering mechanism were extracted, and then analyzed with respect to ground identified phenology for different crop types. The RF algorithm was subsequently trained based on 60% of the data, and validated using the remaining data. Results show that the crop phenology can be monitored, through the combination of multiple polarimetric parameters to build different decision trees in the RF algorithm. By averaging the estimates from multiple decision trees, the complex relative patterns between the polarimetric parameters and crop phenology were recognized, leading to appropriate estimations on crop phenology. The obtained spearman correlation coefficients between the retrieved and ground identified crop phenology were 0.94, 0.91, 0.81 and 0.89 for canola, corn, soybean and wheat, respectively. This study also suggests suitable polarimetric parameters for a timely monitoring of crop phenology.

Soil moisture is a factor for risk analysis in the agricultural sector, yet access to temporally and spatially detailed data is challenging for much of the world's agricultural extend. Significant effort has been focused on developing methodologies to estimate soil moisture from microwave satellite sensors. Canada's RADARSAT Constellation Mission (RCM) is capable of acquiring imagery in a number of modes with a Compact Polarimetry (CP) configuration at different spatial resolutions (1 to 100 m). RCM offers greater polarization diversity, wide swaths and improved temporal frequency (4-day exact revisit time); all important considerations for large area monitoring of agricultural resources. The major goal of this study was to examine whether CP could accurately estimate surface soil moisture over bare fields. A methodology was developed using the calibrated Integral Equation Model (IEM) multi-polarization inversion approach. RADARSAT-2 data was acquired between 2012 and 2017 over a test site in eastern Canada. CP backscatter for two RCM modes (medium resolution 30 m and 50 m (MR30 and MR50)) was simulated using 63 RADARSAT-2 fully polarimetric images. A simple transfer function was developed between RH (right circular-horizontal) and HH (horizontal-horizontal) intensity, as well as RV (right circular-vertical) and VV (vertical-vertical). These HH- and VV-like intensities were then used in the multi-polarization inversion scheme to retrieve soil moisture. CP soil moisture retrievals were validated against soil moisture measurements from a long term in-situ network instrumented with five soil moisture stations. Retrieved and measured soil moisture were well correlated (R > 0.70) with an unbiased root mean square error (ubRMSE) less than 0.06 m(3)/m(3). Overall, the developed method clearly captured the dry down and wetting trends observed through the five years study period. However, results demonstrated that the inversion method introduced a consistent bias (0.10 m(3)/m(3)). Comparison of CP soil moisture estimates to those from the Soil Moisture Active Passive (SMAP) passive microwave satellite confirmed this bias. This study demonstrates the potential of C-band CP data to deliver accurate soil moisture products over wide swaths for regional and national soil moisture monitoring.

Vegetation water content (VWC) is an important land surface parameter that is used in retrieving surface soil moisture from microwave satellite platforms. Operational approaches utilize relationships between VWC and satellite vegetation indices for broad categories of vegetation, i.e., "agricultural crops," based on climatological databases. Determining crop type-specific equations for water content could lead to improvements in the soil moisture retrievals. Data to address this issue are lacking, and as a part of the calibration and validation program for NASA's Soil Moisture Active Passive (SMAP) Mission, field experiments are conducted in northern central Iowa and southern Manitoba to investigate the performance of the SMAP soil moisture products for these intensive agricultural regions. Both sites are monitored for soil moisture, and the calibration and validation assessments had indicated performance issues in both domains. One possible source could be the characterization of the vegetation. In this investigation, Landsat 8 data are used to compute a normalized difference water index for the entire summer of 2016 that is then integrated with extensive VWC sampling to determine how to best characterize daily estimates of VWC for improved algorithm implementation. In Iowa, regression equations for corn and soybean are developed that provided VWC with root mean square error (RMSE) values of 1.37 and 1.10 kg/m(2), respectively. In Manitoba, corn and soybean equations are developed with RMSE values of 0.55 and 0.25 kg/m(2). Additional crop-specific equations are developed for winter wheat (RMSE of 0.07 kg/m(2)), canola (RMSE of 0.90 kg/m(2)), oats (RMSE of 0.74 kg/m(2)), and black beans (RMSE of 0.31 kg/m(2)). Overall, the conditions are judged to be typical with the exception of soybeans, which had an exceptionally high biomass as a result of significant rainfall as compared to previous studies in this region. Future implementation of these equations into algorithm development for satellite and airborne radiative transfer modeling will improve the overall performance in agricultural domains. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

The NASA's Soil Moisture Active Passive (SMAP) mission conducted a field experiment with its partners over two 40-km agricultural domains in Iowa and Manitoba in the summer of 2016 to address concerns observed in SMAP soil moisture (SM) retrievals over agricultural areas. The experiment featured airborne Passive Active L-band System (PALS) flights over each domain with intensive ground measurements and dense networks of SM monitoring stations. With two intensive observation periods separated in time (May 28–June 20 and July 14–August 16), the flights captured both early-season/low vegetation and later-season/high-vegetation conditions. The comparison of the PALS brightness temperature (TB) measurements to the SMAP TB observed over the sites resulted in root mean square difference (RMSD) of 2.8 K and 4.0 K for vertical and horizontal polarizations, respectively. The subsequent SM analysis rescaled the PALS TB with the SMAP TB to allow equitable comparisons between the SM retrievals from the two instruments. The PALS SM retrieval algorithm used the SM sampled by the ground teams during the overpass days for tuning, and was parameterized by a high-resolution vegetation water content product calibrated using vegetation samples collected during the experiment. The tuning process was not able to find a satisfactory result with a temporally constant set of parameters in the single channel algorithm for the two intensive observation periods of the experiment. This result indicated that the rapid change in the vegetation structure during the growth stages and likely variation in the surface roughness conditions were not compatible with rigid parameterization over the entire period. However, using seasonally variable parameters we found that it was possible to retrieve soil moisture with satisfactory accuracy. Comparative analysis with the SMAP SM product included aggregation of the PALS SM to the SMAP pixel-scale. The RMSD between the PALS SM and the aggregated manual field samples was <0.04 m3/m3 with Pearson correlation >0.85 for both sites. The comparison between different in situ sources indicated that the soil moisture network measurements were not the source of the large biases observed for SMAP over the sites reported in earlier studies. Therefore, the results suggested the rapidly growing vegetation and the early-season surface condition changes not captured by the SMAP algorithm caused the SMAP retrieval errors. In addition, the significant deviations of the vegetation water content used by the SMAP product from the calibrated vegetation water content obtained during the experiment compounds the problem.

A field campaign was conducted October 30th to November 13th, 2015 with the intention of capturing diurnal soil freeze/thaw state at multiple scales using ground measurements and remote sensing measurements. On four of the five sampling days, we observed a significant difference between morning (frozen scenario) and afternoon (thawed scenario) ground-based measurements of the soil relative permittivity. These results were supported by an in situ soil moisture and temperature network (installed at the scale of a spaceborne passive microwave pixel) which indicated surface soil temperatures fell below 0 degrees C for the same four sampling dates. Ground-based radiometers appeared to be highly sensitive to F/T conditions of the very surface of the soil and indicated normalized polarization index (NPR) values that were below the defined freezing values during the morning sampling period on all sampling dates. The Scanning L-band Active Passive (SLAP) instrumentation, flown over the study region, showed very good agreement with the ground-based radiometers, with freezing states observed on all four days that the airborne observations covered the fields with ground-based radiometers. The Soil Moisture Active Passive (SMAP) satellite had morning overpasses on three of the sampling days, and indicated frozen conditions on two of those days. It was found that > 60% of the in situ network had to indicate surface temperatures below 0 degrees C before SMAP indicated freezing conditions. This was also true of the SLAP radiometer measurements. The SMAP, SLAP and ground-based radiometer measurements all indicated freezing conditions when soil temperature sensors installed at 5 cm depth were not frozen.

Several large-scale field campaigns have been conducted over the last 20 years that require accurate measurements of soil moisture conditions. These measurements are manually conducted using soil moisture probes which require calibration. The calibration process involves the collection of hundreds of soil moisture cores, which is extremely labor intensive. In 2012, a field campaign was conducted in southern Manitoba in which 55 fields were sampled and calibration equations were derived for each field. The Soil Moisture Active Passive Experiment 2016 (SMAPVEX16) was conducted in this same region, and 21 of the same fields were resampled. This study examines the temporal transferability of calibration equations between these two field campaigns. It was found that the larger range in soil moisture over which samples were collected in 2012 (average range 0.11-0.41 m(3) m(-3)) generally resulted in lower errors when used in 2016 (average range 0.24-0.44 m(3) m(-3)) than the equations derived in 2016 when used with data collected in 2012. Combining the data collected in 2012 and 2016 did not improve the errors, overall. These results suggest that the transfer of calibration equations from one year to the next is not recommended. (C) 2017 Elsevier B.V. All rights reserved.