•Two drought Indices were derived by using 1 km SMAP soil moisture.•The drought indices were used to analyze spatial and temporal drought conditions in Australia.•More severe drought conditions ...occurred during spring and summer in Australia since 2017.•The drought indices were positively correlated to SMAP soil moisture and GPM precipitation.
Drought is one of the major hazards that could have a significant impact on agriculture. In this study, two drought indices at high spatial resolution: Soil Water Deficit Index (SWDI) and Soil Moisture Deficit Index (SMDI) were derived by 1 km downscaled Soil Moisture Active Passive (SMAP) soil moisture (SM), Global Land Data Assimilation System (GLDAS) long-term SM and soil attribute products, and used to analyze the drought conditions in Australia in 2015–2019. The SWDI was calculated from SMAP SM estimates and SM at field capacity/wilting point derived from soil attribute data, while the SMDI was calculated by integrating GLDAS and SMAP SM using a temporally incremental based method. We found that in the eastern and western coastal regions, the droughts occurred during spring and summer and were relieved in fall and winter. The temporal change pattern of drought conditions for the northern coastal regions was opposite of the eastern/western coasts. On the other hand, the inland regions always had more severe drought conditions. Additionally, the validation results for the 1 km SMAP SM using International Soil Moisture Network (ISMN) in situ data showed reliable accuracy and the Root Mean Square Deviation (RMSD) ranged from 0.02 to 0.09 m3/m3. Both SWDI and SMDI showed clear seasonal and interannual variability, and the drought conditions worsened in 2017–2019. From the 1 km SWDI/SMDI maps in the Murray-Darling River Basin, terrain and streamflow were found to be two deterministic factors for the drought conditions.
Root zone soil moisture (RZSM) is a vital variable for vegetation growth, drought monitoring and agricultural water management. Satellite remote sensing measures soil moisture at the surface layer, ...while RZSM is derived usually by model-based simulations. Here, we provide the first comprehensive evaluation of eight RZSM products at a global scale, including GLDAS NOAH, ERA-5, MERRA-2, NCEP R1, NCEP R2, JRA-55, SMAP level 4 and SMOS level 4 datasets. An in-situ validation based on the stations from the International Soil Moisture Network (ISMN) and a triple collocation (TC) evaluation are both conducted to assess the accuracy of these RZSM products. SMAP exhibits the median highest correlation and the median lowest RMSE with in-situ stations over North America. In the TC analysis, MERRA-2 shows the highest median correlation and the median lowest error standard deviation with the unknown truth, followed by GLDAS, SMAP, JRA-55 and ERA-5. A temporal pattern analysis indicates that SMOS has a dry bias relative to other datasets and NCEP R1 has larger seasonal variations relative to other datasets over Asia and North America. The TC analysis indicates that MERRA-2, SMAP, GLDAS, JRA-55, and ERA-5 have better performance relative to other datasets. SMAP is not as good as GLDAS, MERRA-2 and JRA-55 in RZSM estimation over forest areas. The possible factors influencing RZSM performance are discussed, including precipitation forcing, assimilated observations, radio frequency interference issue and validation methods. These results and conclusions may provide new insights for the improvement of model-based RZSM estimation.
•Evaluation of eight RZSM products by ISMN stations and triple collocation globally.•SMAP has highest correlation and lowest RMSE with in-situ stations in North America.•MERRA-2 performs better than other products in TC, followed by GLDAS and SMAP.•Triple collocation and in-situ validation could complement each other.
Although detailed spatial and temporal distribution of soil moisture is crucial for numerous applications, current global soil moisture products generally have low spatial resolutions (25–50 km), ...which largely limit their application at local scales. In this study, we developed a high-resolution soil moisture retrieval framework based on ensemble learning by integrating Landsat 8 optical and thermal observations with multi-source datasets, including in-situ measurements from 1,154 stations in the International Soil Moisture Network, the Soil Moisture Active Passive (SMAP) soil moisture product, the ERA5-Land reanalysis dataset, and auxiliary datasets (terrain, soil texture, and precipitation). Two widely used ensemble learning models were explored and compared using ten-fold cross-validation. The extreme gradient boosting (XGBoost) model performed slightly better than the random forest (RF) model, with a root mean square error (RMSE) of 0.047 m3/m3 and correlation coefficient (R) of 0.952, respectively. Further validation using data from four independent soil moisture networks demonstrated that the prediction accuracy of the XGBoost model was comparable to the SMAP soil moisture product, but with a much higher spatial resolution. The model was finally used to map soil moisture over the high-altitude Tibetan Plateau, which is especially sensitive to climate change, from May to September of 2015. The comparison between our fine-scale soil moisture map at 30 m resolution and the coarse-scale SMAP soil moisture product (36 km) revealed high spatial consistency. These results suggest that there is potential to generate accurate soil moisture products globally at 30 m spatial resolution from the long-term Landsat archive. This finding has practical implications in scenarios requiring fine-scale soil moisture maps, such as climate change and permafrost modeling, hydrological and land surface modeling, and agriculture monitoring.
•A climate-adaptive transfer learning framework for soil moisture estimation is proposed.•The framework mainly uses ERA5-Land data, ISMN data, and global Köppen climate classification data.•The ...framework is designed for data-scarce region and performed well on the Qinghai-Tibet Plateau.•The framework can contribute to historical soil moisture data reconstruction.•A long-term (1960–2019) soil moisture dataset with accuracy improvement is produced.
Soil moisture (SM) plays essential roles in revealing complex interaction mechanisms among air–soil-water-plant processes. In the Qinghai-Tibet Plateau (QTP), the in-situ SM data is sparse and limited, satellite-based SM data has short period, while reanalysis SM data has advantages on long-term and high spatiotemporal resolution but has relatively high error. In this study, to improve soil moisture estimation in the QTP, we aim to propose a Climate-Adaptive Transfer Learning (CATL) framework for data scarce region based on reanalysis data (ERA5-LAND dataset) and the in-situ data (International Soil Moisture Network (ISMN) data). Specifically, regarding the QTP as the target region, selecting the areas with similar climate types with QTP as the source region, we train the CNN-LSTM fusion model in the source region and then transfer it to the target region via fine-tuning strategy. Results indicate that the produced soil moisture data based on CATL framework achieves CC of 0.755 and ubRMSE of 0.042, which has better quality than SMAPL3 during 2015–2019. Additionally, the CATL framework also produced the historical SM data reconstruction during 1960–2010, with CC increased by 11.3 % and ubRMSE reduced by 1.5 % compared with the original ERA5-Land reanalysis data. Furthermore, compared to the direct fine-tuning strategy (without climate adaptive), the CATL framework showed an increase of CC with 2.6 %, and decreases in RMSE, MAE, and ubRMSE of 5.3 %, 4.2 %, and 7.5 %, respectively. Finally, an improved soil moisture dataset (daily, 0.05°) ranging from 1960 to 2019 is produced for the QTP. This study provides a new tool for soil moisture estimation improvement in data-scarce region which will also benefit basin hydrology and water resources management.
Since 2010, SMOS (Soil Moisture and Ocean Salinity) retrievals of surface soil moisture (SM) and vegetation optical depth (VOD) have been produced through the inversion of the so-called Tau-Omega ...(TO) vegetation emission model. Tau-Omega is a 0th-order solution of the radiative transfer equations that neglects multiple scattering, conversely to 1st-order solutions as Two-Stream (2S). To date, very little is known about the compared retrieval performances of these emission models. Here, we inter-compared (SM, VOD) retrievals using the SMOS-IC algorithm running with the TO and 2S emission models. Retrieval performances obtained from TO and 2S were found to be relatively similar, except that a larger dry bias and a slightly lower SM unbiased RMSD were obtained with 2S and the VOD values of the two models vary over dense vegetation areas, both in terms of magnitude and seasonal variations. Considering this and the enhanced physical background of 2S that allows its implementation as a unified emission model for different applications, our study reveals the high interest of using Two-Stream in global retrieval algorithms at L-band.
•Comparing 0th- (Tau-Omega) and 1st-order (Two-Stream) radiative transfer modelling.•Similar performances of Tau-Omega and 2S in retrieving SM and VOD at global scale.•Slightly improved performances of 2S in retrieving SM in terms of ubRMSD.•Drier SM values (by ~0.015 m3/m3) with 2S at global scale.•Large Bias (>0.1) between Tau-Omega and 2S VOD retrievals over the tropical forests.
Soil moisture (SM) derived from satellite-based remote sensing measurements plays a vital role for understanding Earth’s land and near-surface atmosphere interactions. Bistatic Global Navigation ...Satellite System (GNSS) Reflectometry (GNSS-R) has emerged in recent years as a new domain of microwave remote sensing with great potential for SM retrievals, particularly at high spatio-temporal resolutions. In this work, a machine learning (ML)-based framework is presented for obtaining SM data products over the International Soil Moisture Network (ISMN) sites in the Continental United States (CONUS) by leveraging spaceborne GNSS-R observations provided by NASA’s Cyclone GNSS (CYGNSS) constellation alongside remotely sensed geophysical data products. Three widely-used ML approaches—artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—are compared and analyzed for the SM retrieval through utilizing multiple validation strategies. Specifically, using a 5-fold cross-validation method, overall RMSE values of 0.052, 0.061, and 0.065 cm3/cm3 are achieved for the RF, ANN, and SVM techniques, respectively. In addition, both a site-independent and a year-based validation techniques demonstrate satisfactory accuracy of the proposed ML model, suggesting that this SM approach can be generalized in space and time domains. Moreover, the achieved accuracy can be further improved when the model is trained and tested over individual SM networks as opposed to combining all available SM networks. Additionally, factors including soil type and land cover are analyzed with respect to their impacts on the accuracy of SM retrievals. Overall, the results demonstrated here indicate that the proposed technique can confidently provide SM estimates over lightly-vegetated areas with vegetation water content (VWC) less than 5 kg/m2 and relatively low spatial heterogeneity.
Background: The rate of induction of labour is on rise globally due to various indications. The use of well established methods for induction of labour like prostaglandins is associated with various ...maternal and fetal adverse effects. Objective: Present study was designed to investigate the efficacy and safety of Nitric Oxide (NO) donor, Isosorbide mononitrate (ISMN) for cervical ripening and labour induction. To see the effect of sequential use of ISMN followed by misoprostol compared to misoprostol alone on induction of labour at term. Methods: This prospective randomized controlled trial was conducted from May 2012 to April 2013. Total 100 women, who fulfilled the inclusion criteria were admitted for labor induction during the study period. Study group received 60 mg sustained release tablet of Isosorbide mononitrate (ISMN) and control group received placebo, per vaginally, for cervical ripening. Bishop score was reassessed after 16 hours and participants in each group received 25mcg of misoprostol tablet per vaginally at 4 hrs interval for maximum of 4 doses till 3 contraction in 10 min or if cervix was dilated 3cm or more. Progress of labor was monitored using partograph. Results: There was significant difference between ISMN group and control group with respect to mean Bishop score (5.5±0.54 verses 4.16±0.76, p value <0.001). Vaginal delivery were more in ISMN group (66% vs 32%), so caesarean section were less in ISMN group ( 34% vs 68%, p=0.46). Lesser doses of misoprostol was required in ISMN group and reduced requirement of oxytocin to achieve vaginal delivery in ISMN group as compared to control group (9.1% vs 81.25%, p=0.001). Induction to vaginal delivery interval less than 12 hrs was seen on 54% cases in study group whereas none patient delivered in this interval in control group which was statistically significant ( p=0.0002). Major side effect of ISMN was headache which responded to analgesia. Conclusion: ISMN is an ideal agent for effective cervical ripening, which induces ripening of cervix without causing uterine contraction. It significantly improves mean Bishop score, reduces the number of misoprostol doses required to achieve vaginal delivery and less induction failure.
•Review of evolution of sensors and accuracy of microwave soil moisture retrievals.•Eight passive and two active soil moisture products are validated over the CONUS.•ISMN station data and VIC soil ...moisture simulations are considered as reference.•Coverage, spatio-temporal performance and inter satellite comparisons are discussed.•Observed significant growth in coverage and performance over past four decades.
Soil moisture is widely recognized as an important land surface variable that provides a deeper knowledge of land-atmosphere interactions and climate change. Space-borne passive and active microwave sensors have become valuable and essential sources of soil moisture observations at global scales. Over the past four decades, several active and passive microwave sensors have been deployed, along with the recent launch of two fully dedicated missions (SMOS and SMAP). Signifying the four decades of microwave remote sensing of soil moisture, this Part 2 of the two-part review series aims to present an overview of how our knowledge in this field has improved in terms of the design of sensors and their accuracy for retrieving soil moisture. The first part discusses the developments made in active and passive microwave soil moisture retrieval algorithms. We assess the evolution of the products of various sensors over the last four decades, in terms of daily coverage, temporal performance, and spatial performance, by comparing the products of eight passive sensors (SMMR, SSM/I, TMI, AMSR-E, WindSAT, AMSR2, SMOS and SMAP), two active sensors (ERS-Scatterometer, MetOp-ASCAT), and one active/passive merged soil moisture product (ESA-CCI combined product) with the International Soil Moisture Network (ISMN) in-situ stations and the Variable Infiltration Capacity (VIC) land surface model simulations over the Contiguous United States (CONUS). In the process, the regional impacts of vegetation conditions on the spatial and temporal performance of soil moisture products are investigated. We also carried out inter-satellite comparisons to study the roles of sensor design and algorithms on the retrieval accuracy. We find that substantial improvements have been made over recent years in this field in terms of daily coverage, retrieval accuracy, and temporal dynamics. We conclude that the microwave soil moisture products have significantly evolved in the last four decades and will continue to make key contributions to the progress of hydro-meteorological and climate sciences.
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Retrieving surface soil moisture on a local scale using Synthetic Aperture Radar (SAR) data and Deep Learning (DL) models necessitates a substantial volume of data, which may not be available in all ...scenarios. In this study, the application of transfer learning was introduced as a novel approach to address the scarcity of training samples for DL models in the context of soil moisture retrieval. The proposed DL model was initially trained using International Soil Moisture Network (ISMN) data, followed by a fine-tuned process on a local scale using field trip data from an agricultural area in Karaj, Iran. The proposed DL model was compared against Random Forest Regressor (RFR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R
2
indicators. All models underwent hyperparameter-tunning, and their performance was evaluated using 8-fold cross-validation (CV) and various combinations of inputs. The proposed DL model outperformed other models on a local scale achieving an RMSE, MAE, and R
2
of 2.42 vol%, 1.66 vol%, and 0.90, respectively. The MLP model also exhibited good performance with an RMSE, MAE, and R
2
of 2.84 vol%, 2.04 vol%, and 0.88 compared to the RFR with 2.83 vol%, 2.20 vol%, and 0.86, respectively. Additionally, the SVR yielded an RMSE, MAE, and R
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of 3.71 vol%, 3.05 vol%, and 0.78. However, the RMSE, MAE, and R
2
of the MLP and the proposed DL model without using transfer learning deteriorated by around 18%, 32%, and 34%, respectively.
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.
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