Robust estimates of soil moisture and latent heat flux coupling strength obtained from triple collocation

Land surface models (LSMs) are often applied to predict the one-way coupling strength between surface soil moisture (SM) and latent heat (LH) flux. However, the ability of LSMs to accurately represent such coupling has not been adequately established. Likewise, the estimation of SM/LH coupling strength using ground-based observational data is potentially compromised by the impact of independent SM and LH measurements errors. Here we apply a new statistical technique to acquire estimates of one-way SM/LH coupling strength which are nonbiased in the presence of random error using a triple collocation approach based on leveraging the simultaneous availability of independent SM and LH estimates acquired from (1) LSMs, (2) satellite remote sensing, and (3) ground-based observations. Results suggest that LSMs do not generally overestimate the strength of one-way surface SM/LH coupling

Assessment of the Impact of Spatial Heterogeneity on Microwave Satellite Soil Moisture Periodic Error

An accurate temporal and spatial characterization of errors is required for the efficient processing, evaluation, and assimilation of remotely-sensed surface soil moisture retrievals. However, empirical evidence exists that passive microwave soil moisture retrievals are prone to periodic artifacts which may complicate their application in data assimilation systems (which commonly treat observational errors as being temporally white). In this paper, the link between such temporally-periodic errors and spatial land surface heterogeneity is examined. Both the synthetic experiment and site-specified cases reveal that, when combined with strong spatial heterogeneity, temporal periodicity in satellite sampling patterns (associated with exact repeat intervals of the polar-orbiting satellites) can lead to spurious high frequency spectral peaks in soil moisture retrievals. In addition, the global distribution of the most prominent and consistent 8-day spectral peak in the Advanced Microwave Scanning Radiometer - Earth Observing System soil moisture retrievals is revealed via a peak detection method. Three spatial heterogeneity indicators - based on microwave brightness temperature, land cover types, and long-term averaged vegetation index - are proposed to characterize the degree to which the variability of land surface is capable of inducing periodic error into satellite-based soil moisture retrievals. Regions demonstrating 8-day periodic errors are generally consistent with those exhibiting relatively higher heterogeneity indicators. This implies a causal relationship between spatial land surface heterogeneity and temporal periodic error in remotely-sensed surface soil moisture retrievals

The grape remote sensing atmospheric profile and evapotranspiration experiment

Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value crops. Providing this information requires the development of tools applicable across the continuum from subfield scales to improve water management within individual fields up to watershed and regional scales to assess water resources at county and state levels. High-value perennial crops (vineyards and orchards) are major water users, and growers will need better tools to improve water-use efficiency to remain economically viable and sustainable during periods of prolonged drought. To develop these tools, government, university, and industry partners are evaluating a multiscale remote sensing–based modeling system for application over vineyards. During the 2013–17 growing seasons, the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data within adjacent pinot noir vineyards in the Central Valley of California. Additionally, each year ground, airborne, and satellite remote sensing data were collected during intensive observation periods (IOPs) representing different vine phenological stages. An overview of the measurements and some initial results regarding the impact of vine canopy architecture on modeling ET and plant stress are presented here. Refinements to the ET modeling system based on GRAPEX are being implemented initially at the field scale for validation and then will be integrated into the regional modeling toolkit for large area assessment.info:eu-repo/semantics/publishedVersio

The Grape Remote Sensing Atmospheric Profile and Evapotranspiration Experiment

Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability of high-value crops. Providing this information requires the development of tools applicable across the continuum from subfield scales to improve water management within individual fields up to watershed and regional scales to assess water resources at county and state levels. High-value perennial crops (vineyards and orchards) are major water users, and growers will need better tools to improve water-use efficiency to remain economically viable and sustainable during periods of prolonged drought. To develop these tools, government, university, and industry partners are evaluating a multiscale remote sensing–based modeling system for application over vineyards. During the 2013–17 growing seasons, the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data within adjacent pinot noir vineyards in the Central Valley of California. Additionally, each year ground, airborne, and satellite remote sensing data were collected during intensive observation periods (IOPs) representing different vine phenological stages. An overview of the measurements and some initial results regarding the impact of vine canopy architecture on modeling ET and plant stress are presented here. Refinements to the ET modeling system based on GRAPEX are being implemented initially at the field scale for validation and then will be integrated into the regional modeling toolkit for large area assessment

The Impact of Local Acquisition Time on the Accuracy of Microwave Surface Soil Moisture Retrievals over the Contiguous United States

Satellite-derived soil moisture products have become an important data source for the study of land surface processes and related applications. For satellites with sun-synchronous orbits, these products are typically derived separately for ascending and descending overpasses with different local acquisition times. Moreover, diurnal variations in land surface conditions, and the extent to which they are accurately characterized in retrieval algorithms, lead to distinct systematic and random error characteristics in ascending versus descending soil moisture products. Here, we apply two independent evaluation techniques (triple collocation and direct comparison against sparse ground-based observations) to quantify (correlation-based) accuracy differences in satellite-derived surface soil moisture acquired at different local acquisition times. The orbits from different satellites are separated into two overpass categories: AM (12:00 a.m. to 11:59 a.m. Local Solar Time) and PM (12:00 p.m. to 11:59 p.m. Local Solar Time). Results demonstrate how patterns in the accuracy of AM versus PM retrieval products obtained from a variety of active and passive microwave satellite sensors vary according to land cover and across satellite products with different local acquisition times

Robust estimates of soil moisture and latent heat flux coupling strength obtained from triple collocation

Land surface models (LSMs) are often applied to predict the one-way coupling strength between surface soil moisture (SM) and latent heat (LH) flux. However, the ability of LSMs to accurately represent such coupling has not been adequately established. Likewise, the estimation of SM/LH coupling strength using ground-based observational data is potentially compromised by the impact of independent SM and LH measurements errors. Here we apply a new statistical technique to acquire estimates of one-way SM/LH coupling strength which are nonbiased in the presence of random error using a triple collocation approach based on leveraging the simultaneous availability of independent SM and LH estimates acquired from (1) LSMs, (2) satellite remote sensing, and (3) ground-based observations. Results suggest that LSMs do not generally overestimate the strength of one-way surface SM/LH coupling

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets

Evaluation of Multiple Downscaled Microwave Soil Moisture Products over the Central Tibetan Plateau

Downscaled microwave soil moisture (SM) products with a fine resolution are of great importance for both local and regional studies. However, few studies have explored the merits of multiple downscaled microwave SM products. An evaluation of the different products could help to advance knowledge of the downscaled microwave SM products and help researchers to choose the appropriate downscaled SM products for use in further studies. In this research, five microwave SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), AMSR2, and Soil Moisture and Ocean Salinity (SMOS) data were downscaled via the back-propagation neural network (BPNN). The BPNN was chosen because it can effectively simulate the nonlinear relationship between SM and the land surface temperature (LST)/vegetation index (VI). The different downscaled SM products were evaluated with in-situ SM data from the central Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN) during the period from 1 August 2010 to 31 December 2012. Compared with the regression technique, the downscaled correlation coefficient (r) is significantly improved by the BPNN. The downscaled root-mean-square error (RMSE) and bias are comparable for the two techniques. As expected, LST and enhanced VI (EVI) are physically related to SM, and this is the most suitable combination for SM downscaling. Except for the ascending node of SMOS and AMSR2, the downscaled r is closely related to the original RMSE, and a lower original RMSE for the SM product results in a higher downscaled r. The BPNN-downscaled SMOS product in descending node is the closest to the in-situ SM among the different downscaled microwave SM products. The temporal variations and ranges of the microwave SM products are well maintained by the BPNN downscaling. Furthermore, the evaluations against in-situ SM reveal that the overall accuracies of the BPNN-downscaled SM products are very close to the original microwave SM products

An Intelligent Epileptic Prediction System Based on Synchrosqueezed Wavelet Transform and Multi-Level Feature CNN for Smart Healthcare IoT

Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients’ quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively

Comparison of microplastic pollution in different water bodies from urban creeks to coastal waters

Although freshwater and estuary systems are recognized as origins and transport pathways of plastics to the oceans, there is a lack of comparison of microplastics in different water bodies or river networks. In the present study, the spatial distribution of microplastics was compared across different water bodies, including city creeks (Shanghai), rivers (Suzhou River and Huangpu River), an estuary (Yangtze Estuary) and coastal waters (East China Sea) in the Yangtze Delta area. Significant spatial differences of micro plastic abundances were revealed across the sampling areas. The results showed that the abundance of microplastics was higher (1.8-2.4 items/L) in freshwater bodies than that in estuarine and coastal water (0.9 items/L). In the Suzhou River and the Huangpu River, microplastics showed trends of increasing abundance downstream, where the peak of microplastic pollution is closer to the city center and the estuary. In respect of abundance, microplastics are likely to be transported from pollution sources to sink areas via river networks. The proportion of fibers was the highest in city creeks (88%), followed by the Suzhou River (85%), the Huangpu River (81%), the Yangtze Estuary (66%) and the East China Sea (37%). Similarly, polyesters dominated in city creeks and rivers. The results suggest that both the abundance and properties of microplastic pollution varies across different water bodies. Microplastic pollution in small freshwater bodies is more serious than in estuarine and coastal waters. Therefore, we support prioritization of water monitoring for microplastics within entire river networks, instead of single water body surveys. (C) 2018 Elsevier Ltd. All rights reserved