Assessment of the SMAP Passive Soil Moisture Product Chan, Steven K.; Bindlish, Rajat; O'Neill, Peggy E. ...
IEEE transactions on geoscience and remote sensing,
08/2016, Letnik:
54, Številka:
8
Journal Article
Recenzirano
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 NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular ...intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process ~2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP high-resolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m3/m3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of ~0.053 m3/m3. The SMAP L2SMAP product for ~2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined L-band radar-radiometer for proving high spatial resolution and accurate global soil moisture.
•This work highlights the NASA SMAP mission high-resolution soil moisture product.•The algorithm used for this product merges the SMAP radar and radiometer data.•The soil moisture product meets the NASA Level-1 Cal/Val requirement.•The product is available to public from NSIDC for research and applications.
The validation of the soil moisture retrievals from the recently launched National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) satellite is important prior to ...their full public release. Uncertainty in attempts to characterize footprint-scale surface-layer soil moisture using point-scale ground observations has generally limited past validation of remotely sensed soil moisture products to densely instrumented sites covering an area approximating the satellite ground footprint. However, by leveraging independent soil moisture information obtained from land surface modeling and/or alternative remote sensing products, triple collocation (TC) techniques offer a strategy for characterizing upscaling errors in sparser ground measurements and removing the impact of such error on the evaluation of remotely sensed soil moisture products. Here, we propose and validate a TC-based strategy designed to utilize existing sparse soil moisture networks (typically with a single sampling point per satellite footprint) to obtain an unbiased correlation validation metric for satellite surface soil moisture retrieval products. Application of this TC strategy at five SMAP core validation sites suggests that unbiased estimates of correlation between the satellite product and the true footprint average can be obtained - even in cases where ground observations provide only one single reference point within the footprint. An example of preliminary validation results from the application of this TC strategy to the SMAP Level 2 Soil Moisture Passive (beta release version) product is presented.
The concept of temporal stability can be used to identify persistent soil moisture patterns and estimate the large scale average from select representative sensor locations. Accurate and efficient ...estimation of large-scale surface soil moisture is a primary component of soil moisture satellite validation programs. However, monitoring the soil surface at large grid scales is difficult. As part of the aqua satellite advanced microwave scanning radiometer (AMSR) Validation Program, a soil moisture sensor network was installed in the little Washita river watershed in Oklahoma, USA in 2002. Along with data from the soil moisture experiment 2003 (SMEX03), this network will provide a valuable dataset for satellite soil moisture product validation. Analysis shows that most of the network sensors are temporally stable at multiple scales and four sites are identified as representative with negligible bias and small standard deviation to the watershed mean. As part of this analysis, the protocols established for large-scale soil moisture sampling campaigns such as in the soil moisture experiments (SMEX) are validated. This analysis showed that basing grid scale estimates on six sampling points is reasonable and accurate. Temporal stability is shown to be a valuable tool for soil moisture network analysis and can provide an efficient means to large-scale satellite validation.
NASA’s Soil Moisture Active Passive (SMAP) Level 2 soil moisture products are not meeting mission goals in the U.S. Corn Belt according to our seasonal evaluation conducted at a SMAP Core Validation ...Site in central Iowa. The single-channel algorithm (SCA) soil moisture products are too dry in early spring and late fall before and after crops are present, and too noisy in late spring and early summer when crops begin to grow. We investigated likely contributing factors. The climatology of vegetation’s effect on soil moisture retrieval in the SCA can differ by more than 14 days from what is retrieved by SMAP’s dual-channel algorithm (DCA). Soil and vegetation temperatures, assumed to be equal by all retrieval algorithms, are not: vegetation is about 2 K colder at 6:00 a.m. and about 2 K warmer at 6:00 p.m.. The effective temperature in version 2 products is too warm as compared to in situ soil temperatures. We propose a new effective temperature model that is consistent with observations, decreases the unbiased root-mean-square-error (ubRMSE) overall, and increases the coefficient of determination (R2) of the DCA in every month. However, some monthly dry biases increase to more than 0.10 m3 m−3. The single-scattering albedo, ω , has a significant impact on soil moisture retrieval. While the DCA has its lowest ubRMSE and highest R2 when ω is non-zero, the SCA have their lowest ubRMSE and highest R2 when ω = 0 , and the dry bias of all algorithms increases as ω increases. Errors in soil texture are not significant, but soil surface roughness should not be static and have a higher overall value. Our findings make it clear that a new retrieval algorithm that can account for changing soil roughness and vegetation conditions is needed.
The Soil Moisture Active Passive (SMAP) Level-4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, an unbiased ...qualitative estimate of the relative skill of SMAP Level-4 and model-only surface soil moisture (versus true soil moisture) is derived using only one additional noisy (but independent) soil moisture product. The method is applied globally and verified using high-quality, ground-based measurements where available. Results demonstrate that assimilating SMAP brightness temperature has relatively little impact in data-rich areas like the United States and Europe. In contrast, much larger improvement is observed in data-sparse regions, including much of Africa and central Australia, where model-only simulations are disproportionately impacted by low-quality model forcing. Therefore, ground validation conducted in data-rich areas does not adequately sample the added value of SMAP data assimilation for data-sparse regions and substantially underestimates the added skill provided by the SMAP Level-4 system.
Long term surface soil moisture (SSM) data with stable and consistent quality are critical for global environment and climate change monitoring. L band radiometers onboard the recently launched Soil ...Moisture Active Passive (SMAP) Mission can provide the state-of-the-art accuracy SSM, while Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 series provide long term observational records of multi-frequency radiometers (C, X, and K bands). This study transfers the merits of SMAP to AMSR-E/2, and develops a global daily SSM dataset (named as NNsm) with stable and consistent quality at a 36 km resolution (2002-2019). The NNsm can reproduce the SMAP SSM accurately, with a global Root Mean Square Error (RMSE) of 0.029 m
/m
. NNsm also compares well with in situ SSM observations, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observation-driven dataset spans nearly two decades at present, and is extendable through the ongoing AMSR2 and upcoming AMSR3 missions for long-term studies of climate extremes, trends, and decadal variability.
A new soil moisture and soil temperature wireless sensor network (the SMN-SDR) consisting of 34 sites was established within the Shandian River Basin in 2018, located in a semi-arid area of northern ...China. In this study, in situ measurements of the SMN-SDR were used to evaluate 24 different soil moisture datasets grouped according to three categories: (1) single-sensor satellite-based products, (2) multi-sensor merged products, and (3) model-based products. Triple collocation analysis (TCA) was applied to all possible triplets to verify the reliability and robustness of the results. Impacts of different factors on the accuracy of soil moisture products were also investigated, including local acquisition time, physical surface temperature, and vegetation optical depth (VOD). The results reveal that the latest Climate Change Initiative (CCI) -combined product (v06.1, merging extra low-frequency passive microwave data) had the best agreement with in situ measurements from the SMN-SDR, with the lowest ubRMSE (< 0.04 m3/m3) and highest R (> 0.6). Among all single-sensor retrieved soil moisture products, the Soil Moisture Active Passive (SMAP) products performed best in terms of R (> 0.6) and ubRMSE (close to 0.04 m3/m3), with the SMAP-MDCA (Modified Dual Channel Algorithm) being slightly better than the baseline SCA-V (Single Channel Algorithm-Vertical polarization). Importantly, the newly developed SMAP-IB product, which does not use auxiliary data, delivered the best bias statistics and higher VOD values compared with the drier SMAP retrievals, suggesting that the low VOD values (underestimated vegetation effects) may be the major factor causing the dry bias of SMAP products in this study area. It was also found that TCA may systematically overestimate the correlation and underestimate the ubRMSE of soil moisture products as compared with ground-based metrics. TCA-based metrics may vary considerably when using different triplets, due to the TCA assumptions being violated even with the most conservative triplets (in this case an active product, a passive product, and a model-based product). Redundant TCA-based metrics from multiple independent triplets could be averaged to increase the accuracy of final TCA estimates. This study is the first to use in situ measurements from the SMN-SDR to conduct a comprehensive evaluation of commonly used, multi-source soil moisture products. These results are expected to further promote the improvement of satellite- and model-based soil moisture products.
•First use of a new ground network to assess different soil moisture products.•There is an obvious deviation between TCA- and ground-based metrics.•SMAP-MDCA retrieval is more accurate than SCA-V in SMN-SDR.•Underestimation of the vegetation effect is the main factor of SMAP underestimation in SMN-SDR.•First evaluation of the newly developed SMAP-IB product.
This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) ...data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and -0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m 3 /m 3 ubRMSE, -0.015 m 3 /m 3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m 3 /m 3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation.
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.
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