In situ electrokinetic isolation of cadmium from paddy soil through pore water drainage: Effects of voltage gradient and soil moisture

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China

Journal of Meteorological Research ◽

10.1007/s13351-020-0033-7 ◽

2020 ◽

Vol 34 (6) ◽

pp. 1335-1346

Author(s):

Lipeng Jiang ◽

Chunxiang Shi ◽

Shuai Sun ◽

Xiao Liang

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Optimum Interpolation

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Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

Remote Sensing ◽

10.3390/rs13142848 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2848

Author(s):

Hao Sun ◽

Qian Xu

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Water Resource Management ◽

Large Scale ◽

Gap Filling ◽

Spatial Continuity ◽

Study Results ◽

Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

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Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Hydrology and Earth System Sciences ◽

10.5194/hess-19-3845-2015 ◽

2015 ◽

Vol 19 (9) ◽

pp. 3845-3856 ◽

Cited By ~ 9

Author(s):

F. Todisco ◽

L. Brocca ◽

L. F. Termite ◽

W. Wagner

Keyword(s):

Soil Moisture ◽

Soil Loss ◽

Large Scale ◽

Central Italy ◽

Remote Sensing Data ◽

Loss Estimation ◽

Balance Model ◽

Estimation Accuracy ◽

Soil Losses

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha−1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

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NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

Remote Sensing ◽

10.3390/rs12172861 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2861

Author(s):

Jifu Yin ◽

Xiwu Zhan ◽

Jicheng Liu

Keyword(s):

Soil Moisture ◽

Real Time ◽

Land Surface ◽

Vital Role ◽

Product System ◽

Global Soil ◽

One Stop ◽

Satellite Sensors ◽

Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

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Mechanical insights into the effect of fluctuation in soil moisture on nitrous oxide emissions from paddy soil

Paddy and Water Environment ◽

10.1007/s10333-016-0554-y ◽

2016 ◽

Vol 15 (2) ◽

pp. 359-369 ◽

Cited By ~ 4

Author(s):

Lan Ma ◽

Yi Cheng ◽

Jinyang Wang ◽

Xiaoyuan Yan

Keyword(s):

Nitrous Oxide ◽

Soil Moisture ◽

Paddy Soil ◽

Nitrous Oxide Emissions

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TILLAGE AND RESIDUE MULCHING EFFECT ON PRODUCTIVITY OF MAIZE (ZEA MAYS)–TORIA (BRASSICA CAMPESTRIS) CROPPING SYSTEM IN FRAGILE ECOSYSTEM OF NORTHEAST INDIAN HIMALAYAS

Experimental Agriculture ◽

10.1017/s0014479714000179 ◽

2014 ◽

Vol 51 (1) ◽

pp. 107-125 ◽

Cited By ~ 7

Author(s):

ANUP DAS ◽

P. K. GHOSH ◽

M. R. VERMA ◽

G. C. MUNDA ◽

S. V. NGACHAN ◽

...

Keyword(s):

Zea Mays ◽

Soil Moisture ◽

Brassica Campestris ◽

Poultry Manure ◽

Ambrosia Artemisiifolia ◽

Eastern Region ◽

Maize Crop ◽

Weed Biomass ◽

Indian Himalayas

SUMMARYThe north eastern region (NER) of India receives a high amount of rainfall (2450 mm) both in terms of intensity and frequency. Most of the precipitation goes waste because of improper conservation measures and inadequate rainwater harvesting. Growing a second crop during winter (rabi) season on hill slopes and uplands without moisture conservation measure is almost impossible. A simple and very low-cost technique of in situ soil moisture conservation in maize (Zea mays L.)–toria (Brassica campestris L.) system has been developed using residue of preceding rainy season maize crop and mulching with locally available weed biomass Ambrosia artemisiifolia. Six residue mulching combinations tested were viz. control, Maize stalk cover (MSC), MSC + Ambrosia sp. 5 t/ha, MSC + Ambrosia sp. 10 t/ha, MSC + farmyard manure (FYM) 10 t/ha and MSC + Ambrosia sp. 5 t/ha + poultry manure 5 t/ha under zero tillage (ZT) and conventional tillage (CT) systems. Results showed that in situ residue retention of preceding maize crop along with green biomass of Ambrosia sp., applied before sowing of toria, maintained optimum soil moisture for good growth and higher yield of toria. The soil moisture content was consistently higher under residue mulched plots than that under control. All the residue mulching measures recorded higher crop yield for maize and toria than those observed under residue removal (control). The productivity of toria was enhanced by about 99%, only due to retention of MSC as mulch. Mulching with MSC + Ambrosia sp. 5 t/ha + poultry manure 5 t/ha recorded the highest seed yield of toria (four-year average: 641 kg/ha), which was 228% and 64% higher than no mulching (control) and MSC alone. MSC + FYM 10 t/ha (568.3 t/ha) and MSC + Ambrosia sp. 10 t/ha (517.4 t/ha) were found equally effective and produced significantly higher toria yield than that of control. MSC + Ambrosia mulch 10 t/ha gave the highest net returns and B:C ratio of the maize–toria system. The overall B:C ratios were better under ZT than CT. Thus, the study indicated that the integrated management of crop residues and weed biomass (Ambrosia sp.) under ZT created favourable soil moisture to support double cropping with high yield in hill eco-system of northeastern Indian Himalayas.

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