Anna Pacheco

Assessment of the SMAP Passive Soil Moisture 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 m3/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

Mapping Soil Moisture Using RADARSAT-2 Data and Local Autocorrelation Statistics

The purpose of this study is to evaluate the capability of surface radar backscatter models to estimate soil moisture over agricultural fields from fully polarimetric RADARSAT-2 C-band synthetic aperture radar (SAR) responses. For validation purposes, ground measurements over 44 sampling sites in eastern Ontario, Canada were carried out in the spring of 2008 simultaneously with satellite data acquisitions. Soil moisture retrieval was accomplished using two semi-empirical scattering models (Dubois and Oh) and the SAR image backscatter. Discrepancies between measured radar backscatter coefficients and those predicted by the models were previously reported, requiring correction factors to reduce biases associated with these semi-empirical approaches. Soil moisture was estimated by explicitly solving the two backscatter equations of the Dubois model, and using a look-up table (LUT) approach applied to the Oh model. Results showed that the Oh model in a cross-polarization (HH-HV) and Dubois in a co-polarization (HH-VV) inversion scheme provide the best estimates. These model configurations were implemented to produce multi-date soil moisture maps for the eastern Ontario site. To expand the range of validity of these soil moisture estimates, the maps produced by the Dubois and Oh models were uniquely combined. These estimates of absolute soil moisture were then used to derive spatial patterns of near-surface moisture content using the Getis statistic. The Getis statistic maps provide meaningful spatial information, demonstrating the potential of combining the Getis statistic and RADARSAT-2 data in predicting soil moisture conditions.

Monitoring Soil Moisture to Support Risk Reduction for the Agriculture Sector Using RADARSAT-2

Monitoring the amount of moisture held in the soil is critical in the management of risk for the agriculture sector. Extremes in soil moisture can lead to devastating consequences. Early assessment of soil moisture reserves, and monitoring of changes in available soil moisture, could assist in risk reduction strategies for the agriculture sector and effective delivery of government programs. Agriculture and Agri-Food Canada has been acquiring RADARSAT-2 data since 2008 to evaluate the accuracy with which this sensor can provide soil moisture to assist with implementing risk reduction strategies for the Canadian agriculture sector. The calibrated Integral Equation Model (IEM) was used to estimate soil moisture for 15 RADARSAT-2 data sets acquired over an eastern and western Canadian test site. Using this approach, field level soil moisture was estimated to a mean average error of 7.95%, although considerable scatter in the results was observed. Removing fields which had significant residue cover improved site specific soil moisture errors, but only for the fall campaign prior to spring tillage and seed bed preparation. Higher errors were also observed for data sets where angles between the RADARSAT-2 look direction and field tillage structures were largest. When soil moisture estimates were evaluated at a regional scale, mean errors fell to 3.14%. The IEM was also able to detect increases and decreases in soil moisture which followed periods of rainfall and drying.

Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

AbstractThe 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 requiremen...

Potential of mapping soil moisture by combining radar backscatter modeling and PolSAR decomposition

The purpose of this study is to evaluate the capability of the Oh backscattering model in combination with the Freeman Durden decomposition to estimate soil moisture over agricultural fields from fully polarimetric RADARSAT-2 C-band SAR responses. Initially, soil moisture multi-polarization retrieval was accomplished by using a look-up table (LUT) approach applied to the Oh model. Two methods were considered: the multi-polarization method and the one-unknown configuration. Of the two methods, results showed that the HH-HV inversion provided the best estimates. In the second phase, the Freeman Durden decomposition was applied to the polarimetric data. The conceptual approach for retrieving soil moisture using the surface scattering component of the total power was implemented in a LUT inversion. The algorithm attempts to minimize the difference between measured single scattering power obtained by applying the Freeman Durden decomposition and simulated total power using Oh model. When compared with the multi-polarization approach, this polarimetry-based method improves the accuracy of soil moisture estimates.

Evaluation of the validated Soil Moisture product from the SMAP radiometer

NASAs 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. SMAPs 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.

Estimating surface soil moisture from SMAP observations using a Neural Network technique

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.

Using RADARSAT-2 and TerraSAR-X Satellite Data for the Identification of Canola Crop Phenology

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.

The Impact of National Land Cover and Soils Data on SMOS Soil Moisture Retrieval Over Canadian Agricultural Landscapes

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 (R2 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.

Evaluating TerraSAR-X for the identification of tillage occurrence over an agricultural area in Canada

Tillage practices can affect the long term sustainability of agricultural soils as well as a variety of soil processes that impact the environment. The benefits of reduced tillage and no-till practices over agriculture fields are well documented and include: (1) significant reductions in wind and water erosion mitigating nutrient and pesticide runoff into waterways; (2) increasing and/or maintaining soil organic matter; (3) increasing biological activity and improving soil structure; and (4) increasing soil carbon and its sequestration. Information on tillage activities assists in implementing policies and programs to promote beneficial management practices (BMPs), and in monitoring the success of these initiatives. Agriculture and Agri-Food Canada supports environmentally responsible agriculture and has identified this as one of their priorities. Thus, tillage information requirements have become increasingly important to a number of programs and policies within the department. Rapid, accurate and objective methods are required to map and monitor tillage activities. Earth observing satellites can assist with targeting and monitoring land management activities. For the last decade, research has clearly demonstrated that complementary information provided by both optical and radar satellite sensors are fundamental in developing an agricultural land management monitoring system. Launched in June 2007, the TerraSAR-X is a radar satellite acquiring data at the X-band frequency (9.6 GHz). The application of TerraSAR-X data for conservation tillage mapping has been somewhat limited, and thus this study investigates its use in determining tillage occurrence. An HH-HV TerraSAR-X image was acquired on November 4, 2009 and ground data were also collected characterizing tillage conditions at the time of acquisition. Backscatter responses were analyzed to identify tillage occurrence and to differentiate between untilled, chiseled and moldboard ploughed fields. Preliminary analysis showed that HH polarization can better contribute to tillage discrimination than compared to HV polarization and that the backscatter response can be used to discriminate untilled fields from ones that are moldboard ploughed. However, chiseled fields were often confused with highroughness (rms height~1.30 cm) untilled fields and moldboard ploughed fields. Fully polarimetric X-band radar datasets could potentially contribute more information to mapping tillage conditions.