Multi-temporal Dual-channel Algorithm Research Articles

Assessment of five SMAP soil moisture products using ISMN ground-based measurements over varied environmental conditions

Since the launch of Soil Moisture Active Passive (SMAP) mission in 2015, several advanced global surface soil moisture (SM) products derived from different retrieval schemes have been released. However, the factors contributing to the performance differences of these SMAP products have not been fully investigated. This study tries to fill the gap by assessing and intercomparing five SMAP products, namely, the single channel algorithm V/H-pol (SCA-V/H), dual channel algorithm (DCA), multi-temporal dual channel algorithm (MT-DCA) and the new SMAP-INRAE-BORDEAUX (SMAP-IB), using the International Soil Moisture Network (ISMN) ground measurements worldwide during 2016–2020. After reviewing the retrieval algorithms, the impact of five potential perturbing factors on the skills of all products is reported; these factors including mean surface soil temperature (MSST), surface roughness (log(SR)), vegetation density (characterized by vegetation optical depth (VOD)), mean surface soil wetness (MSSM) and soil organic matter (SOM). Results shows that the SMAP-IB product has the highest overall R value of 0.75 with ISMN ground SM, and the smallest unbiased root mean square difference (ubRMSD) values same as SCA-V and DCA (∼0.058 m3/m3). The performance of all five products in terms of R and ubRMSD rapidly degrades over the regions with high VOD, rough surface and low MSST. In contradiction with the fact that product performance degrades with increasing roughness, the ubRMSD of all five products increases with decreasing log(SR) on a relatively smooth surface (log(SR) < 6) with larger growth rates for SMAP-IB, SCA-H and MT-DCA. Regarding characterizing the temporal variations of in situ SM anomalies (Ranom), Ranom increases as the ground becomes wet for all products within a certain range (MSSM < 0.3 m3/m3). In particular, SMAP-IB and MT-DCA present higher Ranom values than the other three products under wet soil surfaces (MSSM > 0.3 m3/m3). The two metrics R and Ranom decrease significantly with increasing SOM for SMAP-IB compared to the other products, and SMAP-IB obtains the lowest values for both metrics when SOM > 20%. These findings are expected to provide useful information for appropriate product selection and improvement.

Evaluation of satellite and reanalysis estimates of surface and root-zone soil moisture in croplands of Jiangsu Province, China

High-quality and long-term surface soil moisture (SSM) and root-zone soil moisture (RZSM) data are critical for agricultural water management of Jiangsu province, which is a major agricultural province in China. However, few studies assessed the accuracy of SSM and RZSM datasets in croplands of Jiangsu province. The study addressed this gap by firstly using observations from ninety-one sites to assess thirteen satellite and model-based SSM products (Advanced Scatterometer (ASCAT), European Space Agency Climate Change Initiative (ESA CCI) Combined/Passive/Active, Soil Moisture and Ocean Salinity in version IC (SMOS-IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP)-Multi-Temporal Dual-channel Algorithm (MTDCA)/Level 3 (L3)/Level 4 (L4)/SMAP-INRAE-BORDEAUX (IB)/Multi-channel Collaborative Algorithm (MCCA), the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land), and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah)), and four RZSM products (ERA5-Land, GLDAS-Noah, SMAP-L4 and ESA CCI (retrieved using ESA CCI Combined SSM coupled with an exponential filter)). We also inter-compared time-invariant and time-variant Triple Collocation Analysis (TCA)-based R with in situ-based R calculated using SSM anomalies. Various evaluation strategies were compared using different groups of available sites and temporal samplings. Our results showed that the model-based and combined SSM products (i.e., ERA5-Land, SMAP-L4, ESA CCI Combined/Passive/Active, GLDAS-Noah, ASCAT) performed better than the other SSM products and ERA5-Land, SMAP-L4 and ESA CCI RZSM generally performed better than the GLDAS-Noah RZSM product with higher R. Similar performance rankings were observed among time-invariant and time-variant TCA-R and in situ-based R, in which the TCA-R values for all SSM datasets were higher than the in situ-based R as the representativeness errors of the in situ measurements may bias in situ-based R. The accuracy of the ESA CCI, GLDAS-Noah and ERA5-Land SSM products was expected to be enhanced by considering the water effect and high uncertainties were observed for MTDCA and SMAP-MCCA SSM over dense vegetation periods and regions. Also, it is important to select appropriate evaluation strategies to conduct the SSM and RZSM evaluations according to the situation as the available sites and temporal samplings may bias the evaluation results.

Characterization of higher-order scattering from vegetation with SMAP measurements

Vegetation cover absorbs and scatters L-band microwave emission measured by SMOS and SMAP satellites. Misrepresentation of this phenomena results in uncertainties when inferring, for instance, surface soil moisture in retrieval algorithms that commonly utilize the tau-omega model which is most applicable for a weakly scattering medium. In this study, we investigate the degree to which multiple-scattering is prevalent over a range of land cover classifications (from lightly vegetated grasslands to dense forests) at the satellite scale by explicitly accounting for multiple-scattering in a first-order radiative transfer model, developed here. Even though the tau-omega model with effective parameters can possibly capture higher-order scattering contributions, deliberately partitioning scattering into different components is required to estimate multiple-scattering properties. Specifically, we aim to determine how one can partition between zeroth and first-order radiative transfer terms within a retrieval algorithm without ancillary information, determine whether this method can detect first-order scattering at the SMAP measurement scale without ancillary information, and quantify the magnitude of detected scattering. A simplified first-order radiative transfer model which characterizes single interactions of microwaves with a scattering medium is developed for implementation within retrieval algorithms. This new emission model is implemented within a recently developed retrieval algorithm, the multi-temporal dual channel algorithm (MT-DCA), which does not require ancillary land use information. Scattering parameters as well as SM and vegetation optical depth (τ) are retrieved simultaneously in Africa and South America using the first year of SMAP brightness temperature measurements on a 36 km grid. Specifically, an introduced time invariant first-order scattering coefficient (ω1) is retrieved representing microwave emission interaction with the canopy. We find that ω1 is typically zero in lightly vegetated biomes and non-zero (~0.06) in 74% of the forest pixels. In forest-dominated pixels, the median first-order emissivity is 0.04, or about 4.3% of a given SMAP radiometer brightness temperature measurement. Additionally, explicitly accounting for first-order scattering terms in the radiative transfer model tends to increase SM and τ retrievals by a median of 0.02 m3/m3 and 0.1, respectively, only in forested regions. This study demonstrates the first attempt to explicitly partition higher-order scattering terms in a retrieval algorithm at a satellite scale and ultimately provides a fundamental understanding and quantification of multiple-scattering from grasslands to forests.

L-band vegetation optical depth seasonal metrics for crop yield assessment

Attenuation of surface microwave emission due to the overlying vegetation is proportional to the density of the canopy and to its water content. The vegetation optical depth (VOD) parameter measures this attenuation. VOD could be a valuable source of information on agroecosystems, especially at lower frequencies for which greater portion of the vegetation canopy contributes to the observed brightness temperature. In the past, visible-infrared indices have been used to provide yield estimates based on measuring the photosynthetic activity from the surface canopy layer. These indices are affected by clouds and apply only in the presence of solar illumination. In this study we instead use the L-band microwave radiometer on board of the SMAP mission that provides VOD estimates in all weather and regardless of illumination. This study proposes a series of L-band VOD metrics for crop yield assessment using the first annual cycle of SMAP data (April 2015 to March 2016) over north-central United States. Maps of yield and crop proportion from the US Department of Agriculture are compared to VOD retrieved from SMAP with the Multi-Temporal Dual Channel Algorithm (MT-DCA). The yield-VOD relationship is explored using principal components regressions. Results show that 66% of yield variance is explained over the whole region by the first principal component (PC1). In corn-soy crops, PC1 explains 78% of yield amount, and maximum, standard deviation, and range of VOD capture the yield spatial patterns. Mixture of crops and scene heterogeneity reduced the unique relationships between VOD metrics and yield for specific crops. Hence, in wheat and mixed crops, PC1 explains 43% of yield variance. Results suggest that complementary information on maximum biomass, growth rate, and VOD amplitude can provide robust yield estimates, and that the uncertainty of these estimates depends on crop composition and heterogeneity. This study provides evidence that L-band VOD metrics can potentially be used to enhance crop yield forecasts.

Vegetation optical depth and scattering albedo retrieval using time series of dual-polarized L-band radiometer observations

Abstract Passive microwave measurements have the potential to estimate vegetation optical depth (VOD), an indicator of aboveground vegetation water content. They are also sensitive to the vegetation scattering albedo and soil moisture. In this work, we propose a novel algorithm to retrieve VOD and soil moisture from time series of dual-polarized L-band radiometric observations along with time-invariant scattering albedo. The method takes advantage of the relatively slow temporal dynamics of early morning vegetation water content and combines a number of consecutive observations to estimate a single VOD. It is termed the multi-temporal dual channel algorithm (MT-DCA). The soil dielectric constant (directly related to soil moisture) of each observation is also retrieved simultaneously. Additionally, the method retrieves a constant albedo, thereby providing for the first time information on global single-scattering albedo variations. The algorithm is tested using three years of L-band passive observations from the NASA Aquarius sensor. The global VOD distribution follows expected gradients of climate and canopy biomass conditions. Its seasonal dynamics follow expected behavior based on precipitation and land cover. The retrieved VOD is closely related to coincident cross-polarized backscatter coefficients. The VOD and dielectric retrievals from MT-DCA are compared to those obtained from implementing the commonly used Land Parameter Retrieval Model (LPRM) algorithm and shown to have less high-frequency noise. There is almost as much variation in MT-DCA retrieved albedo between pixels of a given land cover class than between land cover classes, suggesting the common approach of assigning albedo based on land cover class may not capture its spatial variability. Globally, albedo appears to be primarily sensitive to woody biomass. The proposed algorithm allows for a more accurate accounting of the effects of vegetation on radiometric soil moisture retrievals, and generates new observations of L-band VOD and effective single-scattering albedo. These new datasets are complementary to existing remotely sensed vegetation measurements such as fluorescence and optical-infrared indices.