Core Ideas Upscaling methods compared in situ measures with soil moisture from the SMAP satellite. The accuracy of SMAP soil moisture products in annual cropland was assessed. The spatial representativeness of sparse in situ networks was determined. In 2015, NASA launched the Soil Moisture Active Passive (SMAP) satellite. Data from this satellite are being exploited to improve forecasting of extreme weather events and delivery of disaster response. International core validation sites (CVSs) have been contributing in situ soil moisture data to validate and calibrate SMAP soil moisture products. Overall the soil moisture retrieval errors have exceeded SMAP's mission requirement (errors below 0.04 m3 m−3), with the exception of some sites of annual cropland as present at the Carman (Canada) CVS. In 2016, a SMAP validation experiment was conducted at the Canadian site in Manitoba (SMAPVEX16‐MB) in an attempt to understand the differences between the SMAP soil moisture retrievals and the permanent in situ network observations. The research presented here analyzed the performance of this network in representing soil moisture within a SMAP pixel and tested five upscaling approaches. Comparisons between the permanent network and SMAPVEX16‐MB measurements (from temporary stations and field measures) confirmed agreement among these three sources of soil moisture measures. The SMAP soil moisture values were compared with in situ soil moisture upscaled from the four tested approaches as well as soil moisture estimated by the NOAH Land Surface Model (LSM). There were similar discrepancies when analyzing all methods (RMSE 0.072–0.074 m3 m−3 for the four upscaling methods; 0.076 m3 m−3 for the LSM approach), yielding no reduction in the soil moisture RMSE for this site. The SMAP team will continue to investigate other factors that may be contributing to errors above 0.04 m3 m−3 at these annually cropped CVSs.

Prolonged periods of wet soil conditions, when present during critical crop development stages, can significantly elevate the risk of some crop diseases. Wet soils in fields of flowering canola are a concern with respect to the development of sclerotinia as this pathogen feeds on the petals of the canola flower. As such, determining if canola is in bloom during periods of high moisture is important in deciding whether to take action to mitigate this disease. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization Synthetic Aperture Radar (SAR) data were used with a novel dynamic filtering framework to estimate canola growth stages. In this process, a new crop growth stage indicator was developed and SAR polarimetric parameters sensitive to changes in phenology were identified. Model development used multi-year SAR satellite and field data for one site in Manitoba, Canada. The crop growth estimator was then tested on unseen data from three sites, one in each of Canada's Prairie provinces. This independent validation established that the growth estimator was able to accurately determine canola growth stage and date of flowering with high accuracy. Correlation coefficients (r-values) between observed and estimated phenology ranged from 0.91 to 0.96. Given that this method performed well on test data from other sites and years, this approach could be widely adopted for monitoring the development of canola over extended regions.

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m3m-3, 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m3m-3, 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Jackson, T., P. O’Neill, , S, Chan, R. Bindlish, A. Colliander, F. Chen, S. Dunbar, J. Piepmeier, M. Cosh, T. Caldwell, J. Walker, X. Wu, A. Berg, T. Rowlandson, A. Pacheco, H. McNairn, M. Thibeault, J. Martínez-Fernández, Á. González-Zamora, E. Lopez-Baeza, F. Uldall, M. Seyfried, D. Bosch, P. Starks, C. Holifield, J. Prueger, Z. Su, R. van der Velde, J. Asanuma, M. Palecki, E. Small, M. Zreda, J. Calvet, W. Crow, Y. Kerr, S. Yueh, and D. Entekhabi, December 15, 2016. Calibration and Validation for the L2/3_SM_P Version 4 and L2/3_SM_P_E Version 1 Data Products, SMAP Project, JPL D-56297, Jet Propulsion Laboratory, Pasadena, CA.

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) "reference pixels'' at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m(3) m(-3) or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m(3) m(-3) (0.030 m(3) m(-3)) at the 9-km scale and 0.035 m(3) m(-3) (0.026 m(3) m(-3)) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m(3) m(-3) (0.032 m(3) m(-3)). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide well-calibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations. The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distribution. SMAP fulfilled these requirements through a collaborative Cal/Val Partner program. This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34 candidate sites 18 sites fulfilled all the requirements at one of the resolution scales (at least). The rest of the sites are used as secondary information in algorithm evaluation. The results indicate that the SMAP radiometer-based soil moisture data product meets its expected performance of 0.04 m3/m3 volumetric soil moisture (unbiased root mean square error); the combined radar-radiometer product is close to its expected performance of 0.04 m3/m3, and the radar-based product meets its target accuracy of 0.06 m3/m3 (the lengths of the combined and radar-based products are truncated to about 10 weeks because of the SMAP radar failure). Upon completing the intensive Cal/Val phase of the mission the SMAP project will continue to enhance the products in the primary and extended geographic domains, in co-operation with the Cal/Val Partners, by continuing the comparisons over the existing core validation sites and inclusion of candidate sites that can address shortcomings.

Although soil moisture is an essential variable within the Earth system and has been extensively investigated, there is still a limited understanding of its spatiotemporal distribution and variability. Thus, the objective of this study is to attempt to reproduce the spatial variability of soil moisture and brightness temperature as measured by point-based and airborne remote sensing measurements. To do so, Environment and Climate Change Canada's Surface Prediction System (SPS) is run at very high resolution (100 m) over a region of Manitoba (Canada) where an extensive soil moisture experiment took place in the summer of 2012 [SMAP Validation Experiment 2012 (SMAPVEX12)]. Results show that realistic finescale soil texture improves the quality of SPS outputs. Soil moisture spatial average evolution in time is well simulated by SPS. Simulated spatial variability is underestimated when compared to point-based measurements, although results are improved when examined domainwide versus comparisons using grid points corresponding to measurement sites. SPS brightness temperature fields compare well with remote sensing data in terms of spatial variability. It is shown that during drier periods, factors other than soil texture become important with respect to soil moisture spatial variability. However, during periods with plenty of precipitation, soil texture seems essential in improving simulated soil moisture spatial variability at high resolutions. These results support the conclusion that SPS could provide very high-resolution soil moisture products for research and operational purposes if high-resolution soil texture and vegetation products are made available on a larger scale.

Knowing the exact growth stage of agricultural crops can be valuable information for crop management and monitoring. In Canada, canola fields are particularly vulnerable for crop disease development during their flowering stage, especially when the fields are under persistent wet conditions. Clubroot and sclerotinia are diseases that can occur in canola when these two factors come together. Remote sensing can provide an interesting tool for the monitoring of crop phenological stages over large agriculture landscapes. Reliable and frequent access to data is needed to determine field-specific growth stages. Given their all-weather capability, radar sensors are optimal for monitoring such a dynamic crop parameter. In 2014, Agriculture and Agri-Food Canada collected crop phenology information over multiple canola fields in the area of Carman, Manitoba. Coincidental to ground data collection, fully polarimetric RADARSAT-2 and dual-polarimetric TerraSAR-X satellite data were acquired over the study site. In collaboration with A. U. G. Signals Ltd., a methodology will be developed and validated for the identification of inflorescence emergence and flowering in canola fields. Analysis of the polarimetric datasets from this study determined that several polarimetric parameters were sensitive to the emergence of flower buds and the flowering stage in canola. The alpha angle and entropy in both the C- and X-band were able to identify these growth stages, in addition to any of the reflectivity ratios and differential reflectivity responses that incorporated an HV response. The RADARSAT-2 scatter diversity, degree of purity and depolarization index also demonstrated great potential at identifying canola flower emergence and flowering. These latter polarimetric parameters along with the reflectivity ratios may be advantageous given their ease in implementation within a larger risk assessment satellite-derived methodology for canola crop disease.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36 km fixed Earth grid using brightness temperature observations from descending (6 am) passes and ancillary data. A beta quality version of L2_SM_P was released to the public in September, 2015, with the fully validated L2_SM_P soil moisture data expected to be released in May, 2016. Additional improvements (including optimization of retrieval algorithm parameters and upscaling approaches) and methodology expansions (including increasing the number of core sites, model-based intercomparisons, and results from several intensive field campaigns) are anticipated in moving from accuracy assessment of the beta quality data to an evaluation of the fully validated L2_SM_P data product.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m 3 /m 3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m 3 /m 3 .

The feasibility of using optical remote sensing for crop monitoring has been widely studied over the past three decades. Considering the advantages offered with longer microwave wavelengths, most importantly an ability to collect data under cloudy conditions, the exploitation of Synthetic Aperture Radar (SAR) for monitoring crop condition is of great interest. In this study, multi-polarization RADARSAT-2 (C-band) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) (L-band) were used to investigate the potential of SAR to estimate Leaf Area Index (LAI), a strong indicator of crop productivity. A new LAI estimation approach based on coupling of two existing models has been developed. The coupled model was calibrated and validated for two economically important crops, corn and soybeans, using the data collected during the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) over a site in the Canadian Prairies. The validation of the model generally showed high correlation coefficients (R) between ground measured and estimated LAI values, when dual like-cross polarizations were used (either HH–HV or VV–HV). At C-band, correlations were similar for both dual polarizations with corn R values of 0.83 and 0.81 for HH–HV and VV–HV, respectively; and with soybean R values of 0.80 for both HH–HV and VV–HV. L-band was less sensitive to corn LAI with R values ranging from 0.79 for HH–HV to 0.59 for VV–HV. Our method was not able to accurately estimate LAI for soybeans using UAVSAR. Errors of estimation (both root mean square error and mean average error) established that C-band for corn and soybeans canopies and L-band for corn canopies can be used to estimate LAI at accuracies similar to those reported in studies using satellite optical sensors. These optical satellite-based LAI measurements are currently used to correct estimates of yield and biomass from crop growth models. This study is important in that it demonstrates the capability of SAR to also estimate LAI but with an all-weather acquisition capability, offering an important advantage for operational crop growth monitoring.

Soil spatial heterogeneity poses a challenge to accurate soil moisture determination. Remote sensing, in particular, using sensors that acquire data at microwave frequencies, is being used to overcome this challenge. In situ soil moisture monitoring can be used to validate remotely sensed surface soil moisture estimates and as inputs for agronomic and hydrologic models. Nine in situ soil moisture stations were established in Manitoba (Canada) and instrumented with Stevens Hydra Probes. The sensors were installed in triplicate with vertical orientation at the surface and with horizontal orientation at the 5‐, 20‐, 50‐, and 100‐cm depths. To ensure accuracy of the measured soil moisture, both laboratory and field calibrations were conducted. These calibrated soil moisture values were compared with the probe default values and those generated using published calibrations. Overall, the results showed that the field calibration was superior (coefficient of determination r2 of 0.95) to the laboratory calibration (r2 of 0.89). In addition, coarse‐textured sites generally performed better than the fine‐textured, high cation exchange capacity (CEC) sites. At the Kelburn site with high clay and CEC, the use of field calibration reduced the root mean square error from 0.188 to 0.026 m3 m−3. However, at the low clay and CEC Treherne site, gains in accuracy were minimal, about 0.005 m3 m−3. The laboratory calibration consistently underestimated soil moisture at all the evaluation sites, whereas both Topp and Logsdon calibrations overestimated soil moisture.

To ensure sustainable agriculture production, the availability of water in the right quantity and at the right time is critical, with extremes in availability resulting in severe impacts on the agricultural sector. Delivery of timely and accurate soil moisture data can play a vital role in monitoring the status of available water reserves for this sector. Passive microwave sensors, such as the Soil Moisture and Ocean Salinity (SMOS), are well suited for monitoring vast landscapes given their all-weather capabilities, large spatial footprint, frequent revisit, and the sensitivity of microwave emissions to the soil dielectric. This study examines the impact of exploiting Canadian soil and land cover datasets in the retrieval of soil moisture from SMOS over an agricultural area in the province of Manitoba (Canada). Results demonstrate that global datasets that are integrated within the current SMOS processor perform adequately when field measured soil moisture is compared to estimates of soil moisture by SMOS (R-2 of 0.70 (p <.01) and root-mean-square error (RMSE) of 7.15% with a negative (dry) bias of -0.05%). Overall, this study showed that ingesting high-quality national datasets into the SMOS soil moisture retrieval algorithm did not fully resolve the underestimation of soil moisture, suggesting that further investigation is required to understand this bias. Also, several approaches were evaluated to improve statistical field-derived soil moisture representation in the validation of SMOS soil moisture retrieval and it is clear that good representation of soil moisture as a function of soil textures is crucial to accurately validate SMOS soil moisture products.