Selective PP2A Enhancement through Biased Heterotrimer Stabilization.

Impairment of protein phosphatases, including the family of serine/threonine phosphatases designated PP2A, is essential for the pathogenesis of many diseases, including cancer. The ability of PP2A to dephosphorylate hundreds of proteins is regulated by over 40 specificity-determining regulatory "B" subunits that compete for assembly and activation of heterogeneous PP2A heterotrimers. Here, we reveal how a small molecule, DT-061, specifically stabilizes the B56α-PP2A holoenzyme in a fully assembled, active state to dephosphorylate selective substrates, such as its well-known oncogenic target, c-Myc. Our 3.6 Å structure identifies molecular interactions between DT-061 and all three PP2A subunits that prevent dissociation of the active enzyme and highlight inherent mechanisms of PP2A complex assembly. Thus, our findings provide fundamental insights into PP2A complex assembly and regulation, identify a unique interfacial stabilizing mode of action for therapeutic targeting, and aid in the development of phosphatase-based therapeutics tailored against disease specific phospho-protein targets.

The association between embedded catheter implantation and hospitalization costs for peritoneal dialysis initiation: a retrospective cohort study.

BACKGROUND: Compared with the conventional peritoneal dialysis (PD) catheter insertion, embedding PD catheter implantation is one of the procedures for planned PD initiation. However, facilities where embedded PD catheter implantation is available are limited, and the impact of embedded PD catheter implantation on hospitalization cost and length of hospitalization is unknown. METHODS: This retrospective single-center cohort study included 132 patients with PD initiation between 2005 and 2020. The patients were divided into two groups: 64 patients in the embedding group and 68 patients in the conventional insertion group. We created a multivariable generalized linear model (GLM) with the gamma family and log-link function to evaluate the association among catheter embedding, the duration and medical costs of hospitalization for PD initiation. We also evaluated the effect modification between age and catheter embedding. RESULTS: Catheter embedding (ß coefficient - 0.13 [95% confidence interval - 0.21, - 0.05]) and age (per 10 years 0.08 [0.03, 0.14]) were significantly associated with hospitalization costs. Catheter embedding (- 0.21 [- 0.32, - 0.10]) and age (0.11 [0.03, 0.19]) were also identified as factors significantly associated with length of hospitalization. The difference between the embedding group and the conventional insertion group in hospitalization costs for PD initiation (P for interaction = 0.060) and the length of hospitalization (P for interaction = 0.027) was larger in young-to-middle-aged patients than in elderly patients. CONCLUSIONS: Catheter embedding was associated with lower hospitalization cost and shorter length of hospitalization for PD initiation than conventional PD catheter insertion, especially in young-to-middle-aged patients.

Integration of Sentinel-1A Radar and SMAP Radiometer for Soil Moisture Retrieval over Vegetated Areas.

NASA's Soil Moisture Active Passive (SMAP) was originally designed to combine high-resolution active (radar) and coarse-resolution but highly sensitive passive (radiometer) L-band observations to achieve unprecedented spatial resolution and accuracy for soil moisture retrievals. However, shortly after SMAP was put into orbit, the radar component failed, and the high-resolution capability was lost. In this paper, the integration of an alternative radar sensor with the SMAP radiometer is proposed to enhance soil moisture retrieval capabilities over vegetated areas in the absence of the original high-resolution radar in the SMAP mission. ESA's Sentinel-1A C-band radar was used in this study to enhance the spatial resolution of the SMAP L-band radiometer and to improve soil moisture retrieval accuracy. To achieve this purpose, we downscaled the 9 km radiometer data of the SMAP to 1 km utilizing the Smoothing Filter-based Intensity Modulation (SFIM) method. An Artificial Neural Network (ANN) was then trained to exploit the synergy between the Sentinel-1A radar, SMAP radiometer, and the in situ-measured soil moisture. An analysis of the data obtained for a plant growing season over the Mississippi Delta showed that the VH-polarized Sentinel-1A radar data can yield a coefficient of correlation of 0.81 and serve as a complimentary source to the SMAP radiometer for more accurate and enhanced soil moisture prediction over agricultural fields.

Intermittent instability is widespread in plankton communities.

Chaotic dynamics appear to be prevalent in short-lived organisms including plankton and may limit long-term predictability. However, few studies have explored how dynamical stability varies through time, across space and at different taxonomic resolutions. Using plankton time series data from 17 lakes and 4 marine sites, we found seasonal patterns of local instability in many species, that short-term predictability was related to local instability, and that local instability occurred most often in the spring, associated with periods of high growth. Taxonomic aggregates were more stable and more predictable than finer groupings. Across sites, higher latitude locations had higher Lyapunov exponents and greater seasonality in local instability, but only at coarser taxonomic resolution. Overall, these results suggest that prediction accuracy, sensitivity to change and management efficacy may be greater at certain times of year and that prediction will be more feasible for taxonomic aggregates.

The archaeal Lsm protein from Pyrococcus furiosus binds co-transcriptionally to poly(U)-rich target RNAs.

Posttranscriptional processes in Bacteria include the association of small regulatory RNAs (sRNA) with a target mRNA. The sRNA/mRNA annealing process is often mediated by an RNA chaperone called Hfq. The functional role of bacterial and eukaryotic Lsm proteins is partially understood, whereas knowledge about archaeal Lsm proteins is scarce. Here, we used the genetically tractable archaeal hyperthermophile Pyrococcus furiosus to identify the protein interaction partners of the archaeal Sm-like proteins (PfuSmAP1) using mass spectrometry and performed a transcriptome-wide binding site analysis of PfuSmAP1. Most of the protein interaction partners we found are part of the RNA homoeostasis network in Archaea including ribosomal proteins, the exosome, RNA-modifying enzymes, but also RNA polymerase subunits, and transcription factors. We show that PfuSmAP1 preferentially binds messenger RNAs and antisense RNAs recognizing a gapped poly(U) sequence with high affinity. Furthermore, we found that SmAP1 co-transcriptionally associates with target RNAs. Our study reveals that in contrast to bacterial Hfq, PfuSmAP1 does not affect the transcriptional activity or the pausing behaviour of archaeal RNA polymerases. We propose that PfuSmAP1 recruits antisense RNAs to target mRNAs and thereby executes its putative regulatory function on the posttranscriptional level.

Investigating Correlations and the Validation of SMAP-Sentinel L2 and In Situ Soil Moisture in Thailand.

Soil moisture plays a crucial role in various hydrological processes and energy partitioning of the global surface. The Soil Moisture Active Passive-Sentinel (SMAP-Sentinel) remote-sensing technology has demonstrated great potential for monitoring soil moisture with a maximum spatial resolution of 1 km. This capability can be applied to improve the weather forecast accuracy, enhance water management for agriculture, and managing climate-related disasters. Despite the techniques being increasingly used worldwide, their accuracy still requires field validation in specific regions like Thailand. In this paper, we report on the extensive in situ monitoring of soil moisture (from surface up to 1 m depth) at 10 stations across Thailand, spanning the years 2021 to 2023. The aim was to validate the SMAP surface-soil moisture (SSM) Level 2 product over a period of two years. Using a one-month averaging approach, the study revealed linear relationships between the two measurement types, with the coefficient of determination (R-squared) varying from 0.13 to 0.58. Notably, areas with more uniform land use and topography such as croplands tended to have a better coefficient of determination. We also conducted detailed soil core characterization, including soil-water retention curves, permeability, porosity, and other physical properties. The basic soil properties were used for estimating the correlation constants between SMAP and in situ soil moistures using multiple linear regression. The results produced R-squared values between 0.933 and 0.847. An upscaling approach to SMAP was proposed that showed promising results when a 3-month average of all measurements in cropland was used together. The finding also suggests that the SMAP-Sentinel remote-sensing technology exhibits significant potential for soil-moisture monitoring in certain applications. Further validation efforts and research, particularly in terms of root-zone depths and area-based assessments, especially in the agricultural sector, can greatly improve the technology's effectiveness and usefulness in the region.

An Illustration of FY-3E GNOS-R for Global Soil Moisture Monitoring.

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm's development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm3/cm3. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model.

This paper coupled a unified passive and active microwave observation operator-namely, an enhanced, physically-based, discrete emission-scattering model-with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature TBp (p = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of TBH lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of TBV reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.

Improving SMAP soil moisture spatial resolution in different climatic conditions using remote sensing data.

Soil moisture (SM) at the interface between the land surface and atmosphere is one of the major environmental parameters which plays an important role in hydrological applications. In this article, the SM measured by Soil Moisture Active Passive (SMAP) is downscaled from 3- to 1-km spatial resolution. The main purpose is to evaluate the performance of two different downscaling methods over a variety of climatic conditions and land cover types. These two methods, based on regression and artificial neural network (ANN), are used for enabling us to cross-validate and reliably interpret the obtained results since the number of ground measurements is not sufficient for accuracy assessment. These methods are applied over four different case studies; one is located in the USA, i.e., state of Utah (semi-arid), and the remaining three are located in Iran, i.e., Fars (arid and semi-arid), Yazd (hyper-arid), and Golestan (humid). In both methods, different combinations of input features correlated with SM including land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperatures in horizontal and vertical polarizations (TBH and TBV), shortwave infrared (SWIR), and digital elevation model (DEM) are used. It is found that the DEM does not add extra information in downscaling. The reason is due to high correlation between topography and LST. Moreover, SWIR is most likely able to model only large-scale variations of SM. The downscaled SM products are then compared to 1-km resolution SMAP SM extracted from Sentinel-1 for the study areas in Iran and in situ measurements in Utah. Both methods produce results which are considerably consistent except that the regression method adds more spatial details in the downscaled SM. The achievements illustrate that the performance of both downscaling methods is higher in areas with more homogeneous climatic conditions, i.e., Yazd and Golestan. The best evaluation metrics including correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error (MAPE) for Yazd and Golestan are R = 0.89, RMSE = 0.025 m3/m3, and MAPE = 21.13% and R = 0.93, RMSE = 0.044 m3/m3, and MAPE = 21.95%, respectively. Moreover, large model biases are associated with dense vegetated areas and high altitudes. The best downscaling accuracy in both methods over all study areas belongs to bare soil and flat regions.

Physics-Based Forward Modeling of Ocean Surface Swell Effects on SMAP L1-C NRCS Observations.

This paper examines the impact of ocean surface swell waves on near-coastal L-band high-resolution synthetic aperture radar (SAR) data collected using the National Aeronautics and Space Administration's (NASA) Soil Moisture Active/Passive (SMAP) radar at 40° incidence angle. The two-scale model and a more efficient off-nadir approximation of the second-order small-slope-approximation are used for co- and cross-polarized backscatter normalized radar cross-section (NRCS) predictions of the ocean surface, respectively. Backscatter NRCS predictions are modeled using a combined wind and swell model where wind-driven surface roughness is characterized using the Durden-Vesecky directional spectrum, while swell effects are represented through their contribution to the long wave slope variance (mean-square slopes, or MSS). The swell-only MSS is numerically computed based on a model defined using the JONSWAP spectrum with parameters calculated using the National Data Buoy Center and Wave Watch III data. The backscatter NRCS model is further refined to include fetch-limited and low-wind corrections. The results show an improved agreement between modeled and observed HH-polarized backscatter NRCS when swell effects are included and indicate a relatively larger swell impact on L-band compared to higher radar frequencies. Preliminary investigations into the potential swell retrieval capabilities in the form of excess MSS are encouraging, however further refinements are required to make broadly applicable conclusions.

Protein phosphatase 2A activation as a therapeutic strategy for managing MYC-driven cancers.

The tumor suppressor protein phosphatase 2A (PP2A) is a serine/threonine phosphatase whose activity is inhibited in most human cancers. One of the best-characterized PP2A substrates is MYC proto-oncogene basic helix-loop-helix transcription factor (MYC), whose overexpression is commonly associated with aggressive forms of this disease. PP2A directly dephosphorylates MYC, resulting in its degradation. To explore the therapeutic potential of direct PP2A activation in a diverse set of MYC-driven cancers, here we used biochemical assays, recombinant cell lines, gene expression analyses, and immunohistochemistry to evaluate a series of first-in-class small-molecule activators of PP2A (SMAPs) in Burkitt lymphoma, KRAS-driven non-small cell lung cancer, and triple-negative breast cancer. In all tested models of MYC-driven cancer, the SMAP treatment rapidly and persistently inhibited MYC expression through proteasome-mediated degradation, inhibition of MYC transcriptional activity, decreased cancer cell proliferation, and tumor growth inhibition. Importantly, we generated a series of cell lines expressing PP2A-dependent phosphodegron variants of MYC and demonstrated that the antitumorigenic activity of SMAPs depends on MYC degradation. Collectively, the findings presented here indicate a pharmacologically tractable approach to drive MYC degradation by using SMAPs for the management of a broad range of MYC-driven cancers.

Diversity of LSM Family Proteins: Similarities and Differences.

Members of the Lsm protein family are found in all three domains of life: bacteria, archaea, and eukarya. They are involved in numerous processes associated with RNA processing and gene expression regulation. A common structural feature of all Lsm family proteins is the presence of the Sm fold consisting of a five-stranded ß-sheet and an α-helix at the N-terminus. Heteroheptameric eukaryotic Sm and Lsm proteins participate in the formation of spliceosomes and mRNA decapping. Homohexameric bacterial Lsm protein, Hfq, is involved in the regulation of transcription of different mRNAs by facilitating their interactions with small regulatory RNAs. Furthermore, recently obtained data indicate a new role of Hfq as a ribosome biogenesis factor, as it mediates formation of the productive structure of the 17S rRNA 3'- and 5'-sequences, facilitating their further processing by RNases. Lsm archaeal proteins (SmAPs) form homoheptamers and likely interact with single-stranded uridine-rich RNA elements, although the role of these proteins in archaea is still poorly understood. In this review, we discuss the structural features of the Lsm family proteins from different life domains and their structure-function relationships.

Structure and RNA-Binding Properties of Lsm Protein from Halobacterium salinarum.

The structure and the RNA-binding properties of the Lsm protein from Halobacterium salinarum have been determined. A distinctive feature of this protein is the presence of a short L4 loop connecting the ß3 and ß4 strands. Since bacterial Lsm proteins (also called Hfq proteins) have a short L4 loop and form hexamers, whereas archaeal Lsm proteins (SmAP) have a long L4 loop and form heptamers, it has been suggested that the length of the L4 loop may affect the quaternary structure of Lsm proteins. Moreover, the L4 loop covers the region of SmAP corresponding to one of the RNA-binding sites in Hfq, and thus can affect the RNA-binding properties of the protein. Our results show that the SmAP from H. salinarum forms heptamers and possesses the same RNA-binding properties as homologous proteins with the long L4 loop. Therefore, the length of the L4 does not govern the number of monomers in the protein particles and does not affect the RNA-binding properties of Lsm proteins.

A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data.

Soil moisture (SM) data are required at high spatio-temporal resolution-typically the crop field scale every 3-6 days-for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66-0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

Bioinformatic Prediction of Gene Ontology Terms of Uncharacterized Proteins from Chromosome 11.

In chromosome 11, 71 out of its 1254 proteins remain functionally uncharacterized on the basis of their existence evidence (uPE1s) following the latest version of neXtProt (release 2020-01-17). Because in vivo and in vitro experimental strategies are often time-consuming and labor-intensive, there is a need for a bioinformatics tool to predict the function annotation. Here, we used I-TASSER/COFACTOR provided on the neXtProt web site, which predicts gene ontology (GO) terms based on the 3D structure of the protein. I-TASSER/COFACTOR predicted 2413 GO terms with a benchmark dataset of the 22 proteins belonging to PE1 of chromosome 11. In this study, we developed a filtering algorithm in order to select specific GO terms using the GO map generated by I-TASSER/COFACTOR. As a result, 187 specific GO terms showed a higher average precision-recall score at the least cellular component term compared to 2413 predicted GO terms. Next, we applied 65 proteins belonging to uPE1s of chromosome 11, and then 409 out of 6684 GO terms survived, where 103 and 142 GO terms of molecular function and biological process, respectively, were included. Representatively, the cellular component GO terms of CCDC90B, C11orf52, and the SMAP were predicted and validated using the overexpression system into 293T cells and immunofluorescence staining. We will further study their biological and molecular functions toward the goal of the neXt-CP50 project as a part of C-HPP. We shared all results and programs in Github (https://github.com/heeyounh/I-TASSER-COFACTOR-filtering.git).

Protein phosphatase 2A controls ongoing DNA replication by binding to and regulating cell division cycle 45 (CDC45).

Genomic replication is a highly regulated process and represents both a potential benefit and liability to rapidly dividing cells; however, the precise post-translational mechanisms regulating genomic replication are incompletely understood. Protein phosphatase 2A (PP2A) is a serine/threonine phosphatase that regulates a diverse array of cellular processes. Here, utilizing both a gain-of-function chemical biology approach and loss-of-function genetic approaches to modulate PP2A activity, we found that PP2A regulates DNA replication. We demonstrate that increased PP2A activity can interrupt ongoing DNA replication, resulting in a prolonged S phase. The impaired replication resulted in a collapse of replication forks, inducing dsDNA breaks, homologous recombination, and a PP2A-dependent replication stress response. Additionally, we show that during replication, PP2A exists in complex with cell division cycle 45 (CDC45) and that increased PP2A activity caused dissociation of CDC45 and polymerase α from the replisome. Furthermore, we found that individuals harboring mutations in the PP2A Aα gene have a higher fraction of genomic alterations, suggesting that PP2A regulates ongoing replication as a mechanism for maintaining genomic integrity. These results reveal a new function for PP2A in regulating ongoing DNA replication and a potential role for PP2A in the intra-S-phase checkpoint.

Regularized S-Map Reveals Varying Bacterial Interactions.

There is a growing awareness that bacterial interactions follow a highly nonlinear pattern in reality. However, it is challenging to track the varying bacterial interactions using pairwise correlation analysis, which fails to explore their potential effects on the behavior of microbes. Here, we utilized a regularized sequential locally weighted global linear map (S-map) to capture the varying interspecific interactions from the time series data of a bacterial community under exposure to nitrite. Our results show that bacterial interactions are highly variable and that asymmetric interactions dominate the interaction pattern in a community. Furthermore, we propose a Jacobian coefficient-based statistical method to predict the harmony level of a bacterial community at each successive ecosystem state. The results show that the bacterial community exhibits a higher harmony level in nitrite-treated samples than in the control group. We show that the community harmony level is positively associated with the specific endogenous respiration rates and biofilm formation of the culture. In addition, the community tends to process lower diversity and structural stability under zero- and high-nitrite stresses. We demonstrate that the harmony level, rather than structural stability, is a useful index for unveiling the underlying mechanism of bacterial performance. Overall, the regularized S-map can help us to understand bacterial interactions in ecosystems more accurately than previous approaches.IMPORTANCE It has long been acknowledged that bacterial interactions play important roles in community structure and function. Revealing the interaction variability can allow an understanding of how bacteria respond to perturbation and why bacterial community performance changes. Such information should improve our skills in engineering bacterial communities (e.g., in a wastewater treatment plant) and achieve better removal performance and lower energy consumption.

Increased high-latitude photosynthetic carbon gain offset by respiration carbon loss during an anomalous warm winter to spring transition.

Arctic and boreal ecosystems play an important role in the global carbon (C) budget, and whether they act as a future net C sink or source depends on climate and environmental change. Here, we used complementary in situ measurements, model simulations, and satellite observations to investigate the net carbon dioxide (CO2 ) seasonal cycle and its climatic and environmental controls across Alaska and northwestern Canada during the anomalously warm winter to spring conditions of 2015 and 2016 (relative to 2010-2014). In the warm spring, we found that photosynthesis was enhanced more than respiration, leading to greater CO2 uptake. However, photosynthetic enhancement from spring warming was partially offset by greater ecosystem respiration during the preceding anomalously warm winter, resulting in nearly neutral effects on the annual net CO2 balance. Eddy covariance CO2 flux measurements showed that air temperature has a primary influence on net CO2 exchange in winter and spring, while soil moisture has a primary control on net CO2 exchange in the fall. The net CO2 exchange was generally more moisture limited in the boreal region than in the Arctic tundra. Our analysis indicates complex seasonal interactions of underlying C cycle processes in response to changing climate and hydrology that may not manifest in changes in net annual CO2 exchange. Therefore, a better understanding of the seasonal response of C cycle processes may provide important insights for predicting future carbon-climate feedbacks and their consequences on atmospheric CO2 dynamics in the northern high latitudes.

Evaluation of SMAP downscaled brightness temperature using SMAPEx-4/5 airborne observations.

The Soil Moisture Active and Passive (SMAP) mission, launched by the National Aeronautics and Space Administration (NASA) on 31st January 2015, was designed to provide global soil moisture every 2 to 3 days at 9 km resolution by downscaling SMAP passive microwave observations obtained at 36 km resolution using active microwave observations at 3 km resolution, and then retrieving soil moisture from the resulting 9 km brightness temperature product. This study evaluated the SMAP Active/Passive (AP) downscaling algorithm together with other resolution enhancement techniques. Airborne passive microwave observations acquired at 1 km resolution over the Murrumbidgee River catchment in south-eastern Australia during the fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4/5) were used as reference data. The SMAPEx-4/5 data were collected in May and September 2015, respectively, and aggregated to 9 km for direct comparison with a number of available resolution-enhanced brightness temperature estimates. The results show that the SMAP AP downscaled brightness temperature had a correlation coefficient (R) of 0.84 and Root-Mean-Squared Error (RMSE) of ~10 K, while SMAP Enhanced, Nearest Neighbour, Weighted Average, and the Smoothing Filter-based Modulation (SFIM) brightness temperature estimates had somewhat better performance (RMSEs of ~7 K and an R exceeding 0.9). Although the SFIM had the lowest unbiased RMSE of ~6 K, the effect of cloud cover on Ka-band observations limits data availability.

Synergistic integration of optical and microwave satellite data for crop yield estimation.

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R ≥ 2 0.76 ; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation.