A regionally explicit, global SWI calibration based on ISMN observations

2021 ◽  
Author(s):  
Manolis G. Grillakis
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Large Scale ◽  
Root Zone ◽  
Soil Depth ◽  
European Space Agency ◽  
Soil Layer ◽  
Added Value ◽  
Water Index ◽  
Soil Moisture Data

<p>Remote sensing has proven to be an irreplaceable tool for monitoring soil moisture. The European Space Agency (ESA), through the Climate Change Initiative (CCI), has provided one of the most substantial contributions in the soil water monitoring, with almost 4 decades of global satellite derived and homogenized soil moisture data for the uppermost soil layer. Yet, due to the inherent limitations of many of the remote sensors, only a limited soil depth can be monitored. To enable the assessment of the deeper soil layer moisture from surface remotely sensed products, the Soil Water Index (SWI) has been established as a convolutive transformation of the surface soil moisture estimation, under the assumption of uniform hydraulic conductivity and the absence of transpiration. The SWI uses a single calibration parameter, the T-value, to modify its response over time.</p><p>Here the Soil Water Index (SWI) is calibrated using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then use Artificial Neural Networks (ANNs) to find the best physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration of the T-value. The calibration is then used to assess a root zone related soil moisture for the period 2001 – 2018.</p><p>The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land reanalysis soil moisture dataset, showing a good agreement, mainly over mid-latitudes. The results indicate that there is added value to the results of the machine learning calibration, comparing to the uniform T-value. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large scale soil moisture related studies.</p>

scholarly journals From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

2008 ◽  
Vol 12 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
T. Pellarin ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Synthetic Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Water Index ◽  
In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

Area-representative validation of remotely sensed high resolution soil moisture using a cosmic-ray neutron sensor

2020 ◽  
Author(s):  
Dragana Panic ◽  
Isabella Pfeil ◽  
Andreas Salentinig ◽  
Mariette Vreugdenhil ◽  
Wolfgang Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Zone ◽  
Spatial Scales ◽  
Temporal Frequency ◽  
Cosmic Ray ◽  
Irrigation Scheduling ◽  
Water Index ◽  
The Impact

<p>Reliable measurements of soil moisture (SM) are required for many applications worldwide, e.g., for flood and drought forecasting, and for improving the agricultural water use efficiency (e.g., irrigation scheduling). For the retrieval of large-scale SM datasets with a high temporal frequency, remote sensing methods have proven to be a valuable data source. (Sub-)daily SM is derived, for example, from observations of the Advanced Scatterometer (ASCAT) since 2007. These measurements are available on spatial scales of several square kilometers and are in particular useful for applications that do not require fine spatial resolutions but long and continuous time series. Since the launch of the first Sentinel-1 satellite in 2015, the derivation of SM at a spatial scale of 1 km has become possible for every 1.5-4 days over Europe (SSM1km) [1]. Recently, efforts have been made to combine ASCAT and Sentinel-1 to a Soil Water Index (SWI) product, in order to obtain a SM dataset with daily 1 km resolution (SWI1km) [2]. Both datasets are available over Europe from the Copernicus Global Land Service (CGLS, https://land.copernicus.eu/global/). As the quality of such a dataset is typically best over grassland and agricultural areas, and degrades with increasing vegetation density, validation is of high importance for the further development of the dataset and for its subsequent use by stakeholders.</p><p>Traditionally, validation studies have been carried out using in situ SM sensors from ground networks. Those are however often not representative of the area-wide satellite footprints. In this context, cosmic-ray neutron sensors (CRNS) have been found to be valuable, as they provide integrated SM estimates over a much larger area (about 20 hectares), which comes close to the spatial support area of the satellite SM product. In a previous study, we used CRNS measurements to validate ASCAT and S1 SM over an agricultural catchment, the Hydrological Open Air Laboratory (HOAL), in Petzenkirchen, Austria. The datasets were found to agree, but uncertainties regarding the impact of vegetation were identified.</p><p>In this study, we validated the SSM1km, SWI1km and a new S1-ASCAT SM product, which is currently developed at TU Wien, using CRNS. The new S1-ASCAT-combined dataset includes an improved vegetation parameterization, trend correction and snow masking. The validation has been carried out in the HOAL and on a second site in Marchfeld, Austria’s main crop producing area. As microwaves only penetrate the upper few centimeters of the soil, we applied the soil water index concept [3] to obtain soil moisture estimates of the root zone (approximately 0-40 cm) and thus roughly corresponding to the depth of the CRNS measurements. In the HOAL, we also incorporated in-situ SM from a network of point-scale time-domain-transmissivity sensors distributed within the CRNS footprint. The datasets were compared to each other by calculating correlation metrics. Furthermore, we investigated the effect of vegetation on both the satellite and the CRNS data by analyzing detailed information on crop type distribution and crop water content.</p><p>[1] Bauer-Marschallinger et al., 2018a: https://doi.org/10.1109/TGRS.2018.2858004<br>[2] Bauer-Marschallinger et al., 2018b: https://doi.org/10.3390/rs10071030<br>[3] Wagner et al., 1999: https://doi.org/10.1016/S0034-4257(99)00036-X</p>

Regionalizing root‐zone soil moisture estimates from ESA CCI Soil Water Index using machine learning and information on soil, vegetation, and climate.

2021 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Dimitrios D. Alexakis ◽  
Christos Polykretis ◽  
Ioannis N. Daliakopoulos
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Soil Water ◽  
Root Zone ◽  
Water Index ◽  
Vegetation And Climate

scholarly journals Experimental Study of the Effect of Controlled Drainage on Soil Water and Nitrogen Balance

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2241
Author(s):  
Niannian Yuan ◽  
Yujiang Xiong ◽  
Yalong Li ◽  
Baokun Xu ◽  
Fengli Liu
Keyword(s):  
Soil Moisture ◽  
Water Level ◽  
Soil Water ◽  
Nitrogen Balance ◽  
Field Experiments ◽  
Soil Depth ◽  
Soil Layer ◽  
Ammonia Nitrogen ◽  
Soil Water Storage ◽  
Control Water

Field experiments and micro test pit experiments are conducted at the Four Lake Watershed with a shallow groundwater table in the Hubei province of China in order to study the effect of controlled pipe drainage on soil moisture and nitrogen under different experiment scales. Soil moisture and nitrogen contents are continuously observed at the effective soil depth; water and nitrogen balance are calculated after several heavy rainfalls. The results showed that controlled pipe drainage significantly reduced the fluctuation of soil water content in the entire growth stage. There is a positive correlation between the soil moisture and the control water level in the test pits but no obvious correlation between them in the field experiments, which is related to the vertical and lateral recharge of groundwater in the field. After rainfall, soil organic matter mineralization was enhanced, and the control pipe drainage measures increased the relative content of soil mineralized ammonia nitrogen, which enhanced the stability of soil nitrogen and helped to reduce the loss of nitrogen. The calculation of soil water and nitrogen balance in the field and micro-area after rainfall showed that the soil water storage increased in the effective soil layer under the control water level of 30 cm and 50 cm after rainfall, and the amount of nitrogen mineralization was larger than that under the free drainage treatment.

scholarly journals Error Propagation from Remotely Sensed Surface Soil Moisture Into Soil Water Index Using an Exponential Filter

10.29007/kvhb ◽  
2018 ◽  
Author(s):  
Domenico De Santis ◽  
Daniela Biondi
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Error Propagation ◽  
Surface Soil ◽  
Root Zone ◽  
In Situ Measurements ◽  
Surface Soil Moisture ◽  
Water Index ◽  
Study Sites

In this study an error propagation (EP) scheme was introduced in parallel to exponential filter computation for soil water index (SWI) estimation. A preliminarily assessment of the computed uncertainties was carried out comparing satellite-derived SWI and reference root-zone in situ measurements. The EP scheme has shown skills in detecting potentially less reliable SWI values in the study sites, as well as a better understanding of the exponential filter shortcomings. The proposed approach shows a potential for SWI evaluation, providing simultaneous estimates of time-variant uncertainty.

scholarly journals Will the conversion of evergreen and deciduous broad-leaved mixed forests to Chinese fir plantations affect the transportation of soil water?

2021 ◽  
Author(s):  
qi Chen ◽  
Yuanqiu Liu ◽  
Jiahui Huang ◽  
Yunhong Xie ◽  
Tianjun Bai ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Water ◽  
Water Movement ◽  
Soil Depth ◽  
Soil Layer ◽  
Chinese Fir ◽  
Jiangxi Province ◽  
Natural Forests ◽  
Mixed Forests

The conversion of natural forests to planted forests has become a global trend, and the practice has wide-ranging effects on soil. This study aimed to explore the differences in soil water movement after the conversion of evergreen and deciduous broad-leaved mixed forests (natural forest, NF) to Chinese fir (Cunninghamia lanceolate (Lamb.) Hook.) plantations (CFP, 20–21 years old). Soil samples from five layers (0–5, 5–10, 10–20, 20–30, and 30–50 cm) were collected from NF and CFP before and after rainfall event in the Peng Chongjian watershed, Jiangxi Province. The physical properties of the soils, including the mean and coefficient of variation (CV) of soil moisture content and the soil particle composition, were determined in both forest types. The δD of soil water and the litter water-holding capacity were also measured. The results showed that the variation ranges of moisture content in each soil layer after the rainfall was 21.13%–49.40% in CFP and 21.33%–43.87% in NF. There were no significant differences in soil bulk density or porosity; the clay and silt contents were significantly increased in topsoil, while the sand was significantly decreased (P < 0.05). After the rainfall, soil water in CFP responded more promptly than NF. In the process of infiltration, the contribution of rainfall to soil moisture gradually decreased with increasing soil depth. Topsoil (0–5 cm) in NF responded promptly to rainfall, but the response showed a lag effect with the increase of soil depth. With the extension of infiltration time, the contribution of precipitation to deep soil gradually increased. The results showed that the soil did not degrade after the conversion of NF to CFP, a significant guiding result for plantation cultivation.

scholarly journals Assessment of SMADI and SWDI agricultural drought indices using remotely sensed root zone soil moisture

2018 ◽  
Vol 380 ◽  
pp. 55-66
Author(s):  
Miriam Pablos ◽  
Ángel González-Zamora ◽  
Nilda Sánchez ◽  
José Martínez-Fernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Deficit ◽  
Surface Soil ◽  
Root Zone ◽  
Soil Layer ◽  
Statistical Correlation ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Moisture Index

Abstract. The increasing frequency of drought events has expanded the research interest in drought monitoring. In this regard, remote sensing is a useful tool to globally mapping the agricultural drought. While this type of drought is directly linked to the availability of root zone soil moisture (RZSM) for plants growth, current satellite soil moisture observations only characterize the water content of the surface soil layer (0–5 cm). In this study, two soil moisture-based agricultural drought indices were obtained at a weekly rate from June 2010 to December 2016, using RZSM estimations at 1 km from the Soil Moisture and Ocean Salinity (SMOS) satellite, instead of surface soil moisture (SSM). The RZSM was estimated by applying the Soil Water Index (SWI) model to the SMOS SSM. The Soil Moisture Agricultural Drought Index (SMADI) and the Soil Water Deficit Index (SWDI) were assessed over the Castilla y León region (Spain) at 1 km spatial resolution. They were compared with the Atmospheric Water Deficit (AWD) and the Crop Moisture Index (CMI), both computed at different weather stations distributed over the study area. The level of agreement was analyzed through statistical correlation. Results showed that the use of RZSM does not influence the characterization of drought, both for SMADI and SWDI.

Towards long-term satellite root-zone soil moisture: 40-year Soil Water Index dataset from ESA CCI COMBINED Soil Moisture product.

2020 ◽  
Author(s):  
Adam Pasik ◽  
Bernhard Bauer-Marschallinger ◽  
Wolfgang Preimesberger ◽  
Tracy Scanlon ◽  
Wouter Dorigo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Land Surface ◽  
Root Zone ◽  
Land Surface Model ◽  
Surface Model ◽  
Water Index ◽  
Global Coverage ◽  
T Values

&lt;p&gt;Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling.&amp;#160;&lt;br&gt;Here we present the first long-term SWI dataset from ESA CCI Soil Moisture v04.5 COMBINED product, covering a 40-year period between 1978 and 2018. The ESA CCI dataset is unique because of its long-term global coverage based on merged observations from both active and passive sensors. The SWI is calculated for eight T-values (1, 5, 10, 15, 20, 40, 60, 100), where T-value is a temporal length ruling the infiltration; depending on the soil characteristics it translates into different soil depths.&lt;br&gt;Primary results show promise for pursuing development of an operational SWI product. Here, we present the results of SWI validation against data from the International Soil Moisture Network (ISMN) using the QA4SM framework, as well as results of the attempt to establish relationship between T-values and particular soil depths.&lt;/p&gt;

Estimating soil moisture at various depths from near surface ESA CCI Soil Moisture

2020 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Aristeidis G. Koutroulis ◽  
Christos Polykretis ◽  
Dimitrios D. Alexakis
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
European Space Agency ◽  
Soil Layer ◽  
Global Scale ◽  
Systematic Evaluation ◽  
Multilinear Regression ◽  
Infiltration Process ◽  
Near Surface ◽  
Different Depths

&lt;p&gt;Soil moisture drought is a natural, reoccurring phenomenon that can affect any part of the land. It consists one of the most challenging problems for the modern agriculture as it directly affects the water, energy and food security nexus. Remote sensed soil moisture products have been proved to be valuable tools for the study of the soil moisture droughts. The European Space Agency (ESA), through the Climate Change Initiative (CCI) is currently providing nearly 4 decades of global satellite observed, fully homogenized soil moisture (SM) data for the uppermost soil layer. This data is valuable as it consists one of the most complete in time and space observed soil moisture dataset available. One of the main limitations that ESA CCI SM exhibits is the limited depth at which the soil moisture is estimated (limited to approximately 5cm of soil). In this work we use the ESA CCI SM data to estimate the Soil Water Index (SWI) at the global scale, which can serve as a soil moisture approximation for different depths. The SWI is a simple index that simulates the infiltration process. It utilizes an infiltration parameter T, which is related to the hydraulic characteristics. In this work, the T parameter is calibrated and validated at point scale based on soil moisture measurements of the International Soil Moisture Network (ISMN) and the FluxNet2015 (Tier 1) datasets. The regionalization of the T parameter at global scale is performed by linking T to physical soil descriptors using multilinear regression. Physical soil descriptors were obtained from the Soil Grids 250m dataset, i.e. bulk density, sand/silt/clay fractions, soil organic carbon and coarse fragments. The result of this operation is an SWI dataset for a series of different depths between 0 and 1m. This dataset can be used for the systematic evaluation of global hydrological models on their ability to simulate the soil water.&lt;/p&gt;

Value of satellite-derived soil moisture data to improve the internal consistency of process-based ecohydrological models

2021 ◽  
Author(s):  
Doris Duethmann ◽  
Aaron Smith ◽  
Lukas Kleine ◽  
Chris Soulsby ◽  
Doerthe Tetzlaff
Keyword(s):  
Stable Isotopes ◽  
Soil Moisture ◽  
Soil Water ◽  
Internal Consistency ◽  
Model Performance ◽  
Field Measurements ◽  
Parameter Uncertainties ◽  
Water Index ◽  
Soil Moisture Data ◽  
Ne Germany

&lt;p&gt;It is widely acknowledged that calibrating and evaluating hydrological models only against streamflow may lead to inconsistencies of internal model states and large parameter uncertainties. Soil moisture is a key variable for the energy and water balance, which affects the partitioning of solar radiation into latent and sensible heat as well as the partitioning of precipitation into direct runoff and catchment storage. In contrast to ground-based measurements, satellite-derived soil moisture (SDSM) data are widely available and new data products benefit from improved spatio-temporal resolutions. Here we use a soil water index product based on data fusion of microwave data from METOP ASCAT and Sentinel 1 CSAR for calibrating the process-based ecohydrological model EcH&lt;sub&gt;2&lt;/sub&gt;O-iso in the 66 km&amp;#178; Demnitzer Millcreek catchment in NE Germany. Available field measurements in and close to this intensively monitored catchment include soil moisture data from 74 sensors and water stable isotopes in precipitation, stream and soil water. Water stable isotopes provide information on flow pathways, storage dynamics, and the partitioning of evapotranspiration into evaporation and transpiration. Accounting for water stable isotopes in the ecohydrologic model therefore provides further insights regarding the consistency of internal processes. We first compare the SDSM data to the ground-based measurements. Based on a Monte Carlo approach, we then investigate the trade-off between model performance in terms of soil moisture and streamflow. &lt;em&gt;In situ&lt;/em&gt; soil moisture and water stable isotopes are further consulted to evaluate the internal consistency of the model. Overall, we find relatively good agreements between satellite-derived and ground based soil moisture dynamics. Preliminary results suggest that including SDSM in the model calibration can improve the simulation of internal processes, but uncertainties of the SDSM data should be accounted for. The findings of this study are relevant for reliable ecohydrological modelling in catchments that lack detailed field measurements for model evaluation.&lt;/p&gt;

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