Soil moisture is central to local climate on land. In situ soil moisture observations are vital for observing vegetation‐relevant root‐zone soil moisture. However, stations included in the ...International Soil Moisture Network are sparse in regions with strong land‐atmosphere coupling. We apply a machine‐learning‐based procedure for informing future station placement using virtual soil moisture stations in future CMIP6 projections. Stations are placed where the climate is currently most under‐represented. This strategy outperforms random station placement and station placement according to geographical distance. Doubling the current number of stations using this method alleviates the uneven global distribution of stations, increases the skill in the estimation of inter‐annual variability and trends in dry‐season soil moisture, and reduces its differences across climates in future projections. Stations are predominantly placed in tropical climates, especially when optimizing for drying trends. The results can inform future station placement to support climate change mitigation efforts.
Plain Language Summary
Water stored in the ground as soil moisture is the main water supply for ecosystems and can influence local weather. Ground observations of soil moisture can measure water in deeper soil layers that roots can access. Many stations are collected in the International Soil Moisture Network database. However, this database mainly contains observation stations from the US and Europe. If we were to put additional stations, we should focus on regions and climates that the current network does not sufficiently cover. Locations in model projections of future climate that currently contain a station (“virtual stations”) are used to build a machine‐learning model that predicts soil moisture at locations without stations. We then place new virtual stations where the prediction error of dry‐season soil moisture variability and dry‐season trends is largest. This strategy outperforms randomly placing new stations or placing stations where they are most distant from existing ones. Stations are placed predominantly in the tropics, but prediction improves in all regions. Furthermore, stations are more evenly distributed across the globe, and differences in skill between regions become smaller. This tool can be used to decide where to place future soil moisture stations, which are important for monitoring climate change.
Key Points
In situ soil moisture observations are distributed unequally across the globe and sparse in regions with strong land‐atmosphere coupling
We present a machine‐learning based procedure for informing future station placement using virtual soil moisture stations
Doubling the number of stations with this strategy reduces error in soil moisture trends by 0.2–1.4 kg m−2 and reduces global differences
This study presents a global, hourly surface soil moisture estimation procedure based on precipitation and temperature data. Information on soil composition further helps to define the local ...characteristics of soil moisture development. An advanced antecedent precipitation index (API) is utilized to generate a global soil moisture product of high temporal resolution with the Global Precipitation Measurement (GPM) Missions Integrated Multi-Satellite Retrievals for GPM (IMERG) as main driver. The resulting global GPM API data set is compared against in situ measurements from the International Soil Moisture Network (ISMN) and is also evaluated against the soil moisture data set from the European Space Agency's Climate Change Initiative (ESA CCI SM). The study shows that with empirically derived dampening factors the GPM API achieves a mean ubRMSD across the utilized in situ stations in different climates and vegetation zones of 4.68
and a bias of 0.88
. The data set clearly represents the local soil moisture schemes with seasonal variations. When comparing with ESA CCI SM, the GPM API does perform better at the measurement sites concerning bias, correlation and error values. The data set is in most parts negatively biased compared to the ESA CCI SM, however better matches the mean soil moisture at ISMN stations. Overall, the GPM API delivers a very promising global, hourly surface soil moisture product at 0.1
0.1
spatial resolution.
Soil moisture (SM) is an important biophysical parameter by which to evaluate water resource potential, especially for agricultural activities under the pressure of global warming. The recent ...advancements in different types of satellite imagery coupled with deep learning-based frameworks have opened the door for large-scale SM estimation. In this research, high spatial resolution Sentinel-1 (S1) backscatter data and high temporal resolution soil moisture active passive (SMAP) SM data were combined to create short-term SM predictions that can accommodate agricultural activities in the field scale. We created a deep learning model to forecast the daily SM values by using time series of climate and radar satellite data along with the soil type and topographic data. The model was trained with static and dynamic features that influence SM retrieval. Although the topography and soil texture data were taken as stationary, SMAP SM data and Sentinel-1 (S1) backscatter coefficients, including their ratios, and climate data were fed to the model as dynamic features. As a target data to train the model, we used in situ measurements acquired from the International Soil Moisture Network (ISMN). We employed a deep learning framework based on long short-term memory (LSTM) architecture with two hidden layers that have 32 unit sizes and a fully connected layer. The accuracy of the optimized LSTM model was found to be effective for SM prediction with the coefficient of determination (R2) of 0.87, root mean square error (RMSE) of 0.046, unbiased root mean square error (ubRMSE) of 0.045, and mean absolute error (MAE) of 0.033. The model’s performance was also evaluated concerning above-ground biomass, land cover classes, soil texture variations, and climate classes. The model prediction ability was lower in areas with high normalized difference vegetation index (NDVI) values. Moreover, the model can better predict in dry climate areas, such as arid and semi-arid climates, where precipitation is relatively low. The daily prediction of SM values based on microwave remote sensing data and geophysical features was successfully achieved by using an LSTM framework to assist various studies, such as hydrology and agriculture.
This study attempts to derive the uncertainty of the soil moisture estimation from passive microwave satellite mission at global scale. To do so, the approach is based on the sensitivity of the Soil ...Moisture and Ocean Salinity (SMOS) soil moisture retrieval quality to the land surface characteristics within its footprint (presence of forest, topography, open water bodies, sand, clay, bulk density and soil organic carbon content). First, we performed a global assessment of SMOS using in situ measurements from the International Soil Moisture Network (ISMN) as reference, with more than 1900 ISMN stations and 10 years of SMOS data. This assessment shows that the ubRMSD scores vary greatly between locations (with a mean of 0.074 m3m−3 and an interquartile range of 0.030 m3m−3). Second, the scores are analyzed for different surface conditions within the satellite footprint. The best agreement between the ground measurement and SMOS time series are obtained for low forest cover, low topographic complexity, and marginal presence of open water bodies within the SMOS footprint. Soil parameters also have an impact, with better scores for sandier soils with a high bulk-density and low soil organic carbon content. Finally, we propose to extrapolate the obtained relationships, using a multiple linear regression, in order to derive a global map of SMOS uncertainties based on surface conditions. This map of predicted uncertainties show a diverse range of ubRMSD values across the globe (with a mean of 0.076 m3m−3 and an interquartile range of 0.031 m3m−3) depending on the surface characteristics. At the ISMN site location, the predicted ubRMSD shows similar results than the comparison between SMOS and the in situ measurements. The map of predicted SMOS ubRMSD represents an upper bound estimate of the SMOS uncertainty, as it includes the uncertainties of the in situ sensor measurements and the scale mismatch. Further investigations will focus on the different components of this uncertainty budget to obtain a better assessment of the absolute uncertainties of SMOS soil moisture retrievals across the globe.
Display omitted
•SMOS soil moisture uncertainty ubRMSD is in average 0.070 m3m−3 (IQR 0.030 m3m−3) compared to in situ measurement.•SMOS uncertainty is analysed regarding the footprint composition : vegetation, soil texture, topography and open water.•SMOS uncertainty at global scale is predicted based on a sensitivity analysis with surface conditions.
Accurate localization is the foundation of unmanned aerial vehicle (UAV) swarm applications in the global navigation satellite system (GNSS)-denied environment. However, the implementation of UAV ...formation in the real world is costly and time-consuming, which leads to difficulties in developing navigation algorithms. A real-time simulator for navigation in GNSS-denied environments is proposed, which includes world, model, controller, scene matching navigation (SMN), relative navigation and formation controller modules. Each module can be modified, which means that users can test their own algorithms. A novel inertial-aided SMN (ISMN) algorithm is developed and a relative navigation method that does not rely on inter communication is proposed. ISMN and relative navigation based on a camera and ultrawideband (UWB) are tested on the platform. Based on the developed simulation system, the navigation algorithms can be verified easily, which can reduce the time and personnel requirements during flight testing.
This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) ...as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANNRAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future.
L-band satellite remote sensing is one of the most promising techniques for global monitoring of soil moisture (SM). In addition to soil moisture and ocean salinity (SMOS) SM products, another global ...SM product has been developed using Aquarius, which is the first operational active/passive L-band satellite sensor. The spatial resolution of Aquarius SM products is about 100 km, which presents more challenges to the groundbased validation. This study explores approaches to validate and evaluate the Aquarius SM products in terms of their spatial and temporal distributions, through synergistic use of in situ measurements and model products from the global land data assimilation system (GLDAS). A dense soil moisture/temperature monitoring network over the central Tibetan plateau (CTP-SMTMN) and sparse stations from the soil climate analysis network (SCAN) over United States are used for the reliability assessment of Aquarius SM products. Results show that the Aquarius SM captures the spatial-temporal variability of CTP-SMTMN reference dataset with an overall RMSD of 0.078 m 3 · m -3 and correlation coefficient of 0.767. The comparison results with reference to SCAN datasets suggest that the RMSD can reach to the target value of 0.04 m 3 · m -3 over specific stations, but the impacts from different orbits, seasons, and land cover types are also found to be significant. The comparison between Aquarius retrievals and GLDAS/common land model (CLM) simulations presents a general well statistical agreement with correlation coefficients above 0.5 for most terrestrial areas. These results are considered to support the use of Aquarius SM products in future applications.
In this paper we study the space of graphs with weights togetherwith two operations between these graphs. The results found in this spaceare used in the study of stable maps of3-manifolds inR3, in ...particular whenthe3-manifold isMn, where this manifold is the connected sum ofn-copiesofS1S2withM0=S3. In addition, important results are proven for theconstruction of this type of maps.
En el presente artículo se estudia el espacio de grafos con pesos, junto con dos operaciones entre estos grafos. Los resultados que se encuentran en
este espacio son usados en el estudio de aplicaciones estables de 3-variedades en R3, en particular cuando la 3-variedad es Mn, donde esta variedad es la suma conexa de n-copias de S1× S2 con M0 = S3. Además, se prueban resultados importantes para la construcción de este tipo de aplicaciones.
Drug dissolution/absorption simulating system (DDASS) is a novel method to monitor the process of dissolution and permeation of complete oral solid formulations simultaneously. Four commercial dosage ...forms of isosorbide mononitrate (ISMN) were chosen as model formulations. An in vitro-in vivo correlation study was carried out between DDASS methods and beagle dogs, and between the classical method and beagle dogs.
The objective of the present study was to develop a novel
in vitro system to simulate the process of dissolution and permeation of oral solid dosage forms
in vivo, and to establish a correlation between
in vitro permeation and
in vivo absorption that could predict the bioavailability (BA) and bioequivalence (BE) of congeneric products. The
in vitro dissolution and absorption kinetics of four dosage forms of isosorbide mononitrate (ISMN) were evaluated by the USP basket/paddle system and drug dissolution/absorption simulating system (DDASS). The corresponding pharmacokinetic study was performed in beagle dogs. A comparative study was carried out between the classical and the novel method to estimate the effectiveness of the modified DDASS in simulating the course of dissolution and absorption
in vivo. Indeed, the correlation coefficients of
in vitro dissolution and
in vivo absorption obtained from DDASS and dogs were higher. Moreover, a higher level A
in vitro–
in vivo correlation (IVIVC) between DDASS permeation and dog absorption was established, with correlation coefficients of 0.9968, 0.9872, 0.9921, and 0.9728. The DDASS method was more accurate at modeling the process of dissolution and absorption
in vivo for both immediate-release (IR) and sustained-release (SR) dosage forms of ISMN.
We investigated the efficacy of irinotecan/cisplatin (IP) versus irinotecan/capecitabine (IX) with or without isosorbide-5-mononitrate (ISMN) in chemo-naïve advanced non-small-cell lung cancer.
...Initially, 74 patients were randomly assigned to either IP or IX. Given the potential benefits of ISMN on chemotherapy, the protocol was amended during the study. Subsequently, 72 patients were randomly assigned to either IP + ISMN or IX + ISMN. Patients were treated with predefined second-line therapies (docetaxel/capecitabine for IP or IP + ISMN, docetaxel/cisplatin for IX or IX + ISMN) when disease progressed.
A total of 146 received treatment. Response rate (RR), median progression-free survival (PFS) and overall survival (OS) were 49%, 5.5 months, 14.5 months in IP; 33%, 3.3 months, 13.0 months in IP + ISMN; 30%, 4.3 months, 16.1 months in IX; and 25%, 3.4 months, 13.6 months in IX + ISMN, respectively. While IP arm showed a trend toward higher RR and longer PFS than IX arm, IX arm showed a trend toward longer OS than IP arm. No significant differences were observed between IP + ISMN and IX + ISMN.
IP showed better RR and PFS but no OS benefit when compared with IX. The addition of ISMN to IP or IX chemotherapy did not seem to improve the treatment outcome.
PDF
