The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals

Knowledge of the temporal error structure for remotely sensed surface soil moisture retrievals can improve our ability to exploit them for hydrologic and climate studies. This study employs a triple collocation analysis to investigate both the total variance and temporal auto-correlation of errors in Soil Moisture Active and Passive (SMAP) products generated from two separate soil moisture retrieval algorithms, the vertically-polarized bright ness temperature based Single Channel Algorithm (SCA-V, the current baseline SMAP algorithm) and the Dual Channel Algorithm (DCA). A key assumption made in SCA V is that real-time vegetation opacity can be accurately captured using only a climatology for vegetation opacity. Results demonstrate that, while SCA-V generally outperforms DCA, SCA-V can produce larger total errors when this assumption is significantly violated by inter-annual variability in vegetation health and biomass. Furthermore, larger auto-correlated errors in SCA-V retrievals are found in areas with relatively large vegetation opacity deviations from climatological expectations. This implies that a significant portion of the auto-correlated error in SCA-V is attributable to the violation of its vegetation opacity climatology assumption and suggests that utilizing a real (as opposed to climatological) vegetation opacity time series in the SCA-V algorithm would reduce the magnitude of auto-correlated soil moisture retrieval errors

A double instrumental variable method for geophysical product error estimation

The global validation of remotely sensed and/or modeled geophysical products is often complicated by a lack of suitable ground observations for comparison. By cross-comparing three independent collocated observations, triple collocation (TC) can solve for geophysical product errors in error-prone systems. However, acquiring three independent products for a geophysical variable of interest can be challenging. Here, a double instrumental variable based algorithm (IVd) is proposed as an extension of the existing single instrumental variable (IVs) approach to estimate product error standard deviation (σ) and product-truth correlation (R) using only two independent products - an easier requirement to meet in practice. An analytical examination of the IVd method suggests that it is less prone to bias and has reduced sampling errors relative to IVs. Results from an example application of the IVd method to precipitation product error estimation show that IVd-based σ and R are good approximations of reference values obtained from TC at the global extent. In addition to their spatial consistency, IVd estimated error metrics also have only marginal (less than 5%) relative biases versus a TC baseline. Consistent with our earlier analytical analysis, these empirical results are shown to be superior to those obtained by IVs. However, several caveats for the IVd approach should be acknowledged. As with TC and IVs, IVd estimates are less robust when the signal-to-noise ratio of geophysical products is very low. Additionally, IVd may be significantly biased when geophysical products have strongly contrasting error auto-correlations

Genomic and Genetic Evidence for the Loss of Umami Taste in Bats

Umami taste is responsible for sensing monosodium glutamate, nucleotide enhancers, and other amino acids that are appetitive to vertebrates and is one of the five basic tastes that also include sour, salty, sweet, and bitter. To study how ecological factors, especially diets, impact the evolution of the umami taste, we examined the umami taste receptor gene Tas1r1 in a phylogenetically diverse group of bats including fruit eaters, insect eaters, and blood feeders. We found that Tas1r1 is absent, unamplifiable, or pseudogenized in each of the 31 species examined, including the genome sequences of two species, suggesting the loss of the umami taste in most, if not all, bats regardless of their food preferences. Most strikingly, vampire bats have also lost the sweet taste receptor gene Tas1r2 and the gene required for both umami and sweet tastes (Tas1r3), being the first known mammalian group to lack two of the five tastes. The puzzling absence of the umami taste in bats calls for a better understanding of the roles that this taste plays in the daily life of vertebrates

A Young Drosophila Duplicate Gene Plays Essential Roles in Spermatogenesis by Regulating Several Y-Linked Male Fertility Genes

Gene duplication is supposed to be the major source for genetic innovations. However, how a new duplicate gene acquires functions by integrating into a pathway and results in adaptively important phenotypes has remained largely unknown. Here, we investigated the biological roles and the underlying molecular mechanism of the young kep1 gene family in the Drosophila melanogaster species subgroup to understand the origin and evolution of new genes with new functions. Sequence and expression analysis demonstrates that one of the new duplicates, nsr (novel spermatogenesis regulator), exhibits positive selection signals and novel subcellular localization pattern. Targeted mutagenesis and whole-transcriptome sequencing analysis provide evidence that nsr is required for male reproduction associated with sperm individualization, coiling, and structural integrity of the sperm axoneme via regulation of several Y chromosome fertility genes post-transcriptionally. The absence of nsr-like expression pattern and the presence of the corresponding cis-regulatory elements of the parental gene kep1 in the pre-duplication species Drosophila yakuba indicate that kep1 might not be ancestrally required for male functions and that nsr possibly has experienced the neofunctionalization process, facilitated by changes of trans-regulatory repertories. These findings not only present a comprehensive picture about the evolution of a new duplicate gene but also show that recently originated duplicate genes can acquire multiple biological roles and establish novel functional pathways by regulating essential genes

Assessing Performances of Multivariate Data Assimilation Algorithms with SMOS, SMAP, and GRACE Observations for Improved Soil Moisture and Groundwater Analyses

Multivariate data assimilation (DA) of satellite soil moisture (SM) and terrestrial water storage (TWS) observations has recently been used to improve SM and groundwater storage (GWS) simulations. Previous studies employed the ensemble Kalman approach in multivariate DA schemes, which assumes that model and observation errors have a Gaussian distribution. Despite the success of the Kalman approaches, SM and GWS estimates can be suboptimal when the Gaussian assumption is violated. Other DA approaches, such as particle smoother (PS), ensemble Gaussian particle smoother (EnGPS), and evolutionary smoother (EvS), do not rely on the Gaussian assumption and may be better suited to non-Gaussian error systems. The objective of this paper is to evaluate the performance of these four DA approaches (EnKS, PS, EnGPS, and EvS) in multivariate DA systems by assimilating satellite data from the Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery And Climate Experiment (GRACE) missions into the Community Atmosphere and Biosphere Land Exchange (CABLE) land surface model. The analyses are carried out in Australia’s Goulburn River catchment, where in situ SM and groundwater data are available to comprehensively validate the DA performance. Results show that all four DA approaches have outstanding performances and improve correlation coefficients of SM and GWS estimates by ~20% and 100%, respectively. The EvS outperforms the others, but its benefit is relatively marginal compared to Gaussian approaches (e.g., EnKS). This is due to the fact that SM and TWS error distributions in this study are close to Gaussian: a suitable condition for, e.g., EnKS, EnGPS. The robust performance of EvS appears to be the optimal approach for jointly assimilating multi-source hydrological observations to improve regional hydrological analyses

bias-corrected irrigated area dataset

We produce bias-corrected irrigated area (IA) dataset for the period 2000 to 2019, employing agricultural suitability (AS calculated using precipitation and potential evapotranspiration) information through a linear regression correction method, which ensures continuous spatiotemporal IA information at the county-level.The geodetic datum utilized by this dataset adheres to the EPSG:4326 coordinate reference system, commonly known as WGS-1984.</p

An instrument variable based algorithm for estimating cross-correlated hydrological remote sensing errors

Optimally using multi-source remote-sensing (RS) and/or reanalyzed hydrological products requires knowledge of each product's accuracy and inter-product error cross-correlations. Quadruple collocation (QC) analysis can potentially solve for this error information without the reliance of high-quality ground references. However, QC requires at least three independent products for a variable of interest. At the global scale, obtaining three independent products is often a challenge. To address this issue, this study proposes an extended double instrumental variable algorithm (denoted as EIVD), which can accurately estimate product error and inter-product error cross-correlations using only two independent products – a requirement easier to meet in practice. Synthetic numerical experiments demonstrate that EIVD is robust and unbiased – provided product error auto-correlations are not strongly contrasting. The performance of EIVD is further tested via a (real-data) global precipitation error analysis using traditional QC results as a validation reference. The global consistency (i.e., spatial correlation) of QC- and EIVD-estimated product-truth correlation is above 0.86 [–] for all precipitation products being considered, and the relative mean difference of QC- and EIVD-based correlations is, on average, less than 5%. The spatial consistency of QC- and EIVD-based inter-product error cross-correlation is 0.47 [–] with a relative bias of 8%. A quantitative analysis demonstrates that regions with inconsistent EIVD and QC results are likely attributable to the violation of the QC error independency assumptions. Given the robustness of EIVD in fully parameterizing hydrological product error information, it is expected to improve the accuracy and efficiency of multi-source hydrological data merging and data assimilation

Triple collocation-based merging of multi-source gridded evapotranspiration data in the Nordic Region

Accurate evapotranspiration (ET) data are required for many hydro-meteorological applications. Compared with the traditional evaluation that requires in-situ measurements, the triple collocation (TC) technique estimates geophysical product errors without the need for ground truth, which is especially suitable over large areas lacking a dense in-situ network. However, violations of the zero-error cross-correlation (ECC) assumption are found to be the dominant sources of impairing the TC robustness. This study presents the first application of a TC-based merging framework that optimally considers ECC to merge multi-source gridded ET products in the Nordic Region during 2003–2018. The ECC estimates of each ET dataset pair calculated by the quadruple collocation approach are used to select the qualified triplets from four products, including FLUXCOM, Global Land Surface Satellite (GLASS), Global Land Evaporation and Amsterdam Model (GLEAM), and Penman-Monteith-Leuning Version 2 (PML-V2). Then the ET merged datasets are generated by weighting TC-based rescaled error variances of the parent datasets through least square merging. Finally, the accuracy of both the parent and the merged datasets are assessed with the Integrated Carbon Observation System (ICOS) flux data in the Nordic Region based on multiple statistical metrics. Results demonstrate that the ECC values provide intuitive evidence for filtering unqualified TC triplets. Both the absolute and relative error variances (signal-to-noise ratio) are considered for ET dataset evaluation. Overall PML-V2 has the best performance among the evaluated four products. Two merged ET datasets with the reference climatology of FLUXCOM outperform all parent products with the lowest errors by using ICOS data as reference among all sites – indicating the feasibility of TC technique for improving ET accuracy in the Nordic Region. This study also analyses the impacts of reference climatology selection on the TC merged results