Soil moisture retrieval at regional scale from AMSR2 data (Conference Presentation)

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

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Multi-Dimensional Drought Assessment in Abbay/Upper Blue Nile Basin: The Importance of Shared Management and Regional Coordination Efforts for Mitigation

Remote Sensing ◽

10.3390/rs13091835 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1835

Author(s):

Yared Bayissa ◽

Semu Moges ◽

Assefa Melesse ◽

Tsegaye Tadesse ◽

Anteneh Z. Abiy ◽

...

Keyword(s):

Soil Moisture ◽

Regional Scale ◽

Earth Observation ◽

Drought Indices ◽

Severe Drought ◽

Nile Basin ◽

Blue Nile ◽

Blue Nile Basin ◽

Small Streams ◽

Upper Blue Nile Basin

Drought is one of the least understood and complex natural hazards often characterized by a significant decrease in water availability for a prolonged period. It can be manifested in one or more forms as meteorological, agricultural, hydrological, and/or socio-economic drought. The overarching objective of this study is to demonstrate and characterize the different forms of droughts and to assess the multidimensional nature of drought in the Abbay/ Upper Blue Nile River (UBN) basin and its national and regional scale implications. In this study, multiple drought indices derived from in situ and earth observation-based hydro-climatic variables were used. The meteorological drought was characterized using the Standardized Precipitation Index (SPI) computed from the earth observation-based gridded CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall data. Agricultural and hydrological droughts were characterized by using the Soil Moisture Deficit Index (SMDI) and Standardized Runoff-discharge Index (SRI), respectively. The monthly time series of SMDI was derived from model-based gridded soil moisture and SRI from observed streamflow data from 1982 to 2019. The preliminary result illustrates the good performance of the drought indices in capturing the historic severe drought events (e.g., 1984 and 2002) and the spatial extents across the basin. The results further indicated that all forms of droughts (i.e., meteorological, agricultural, and hydrological) occurred concurrently in Abbay/Upper Blue Nile basin with a Pearson correlation coefficient ranges from 0.5 to 0.85 both Kiremt and annual aggregate periods. The concurrent nature of drought is leading to a multi-dimensional socio-economic crisis as indicated by rainfall, and soil moisture deficits, and drying of small streams. Multi-dimensional drought mitigation necessitates regional cooperation and watershed management to protect both the common water sources of the Abbay/Upper Blue Nile basin and the socio-economic activities of the society in the basin. This study also underlines the need for multi-scale drought monitoring and management practices in the basin.

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Investigating the Spatio-Temporal Variation of Soil Moisture and Agricultural Drought towards Supporting Water Resources Management in the Red River Basin of Vietnam

Sustainability ◽

10.3390/su13094926 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4926

Author(s):

Nguyen Duc Luong ◽

Nguyen Hoang Hiep ◽

Thi Hieu Bui

Keyword(s):

Soil Moisture ◽

River Basin ◽

Temporal Dynamics ◽

Regional Scale ◽

Dry Season ◽

Red River ◽

Agricultural Drought ◽

Severe Drought ◽

Red River Basin ◽

Spatio Temporal

The increasing serious droughts recently might have significant impacts on socioeconomic development in the Red River basin (RRB). This study applied the variable infiltration capacity (VIC) model to investigate spatio-temporal dynamics of soil moisture in the northeast, northwest, and Red River Delta (RRD) regions of the RRB part belongs to territory of Vietnam. The soil moisture dataset simulated for 10 years (2005–2014) was utilized to establish the soil moisture anomaly percentage index (SMAPI) for assessing intensity of agricultural drought. Soil moisture appeared to co-vary with precipitation, air temperature, evapotranspiration, and various features of land cover, topography, and soil type in three regions of the RRB. SMAPI analysis revealed that more areas in the northeast experienced severe droughts compared to those in other regions, especially in the dry season and transitional months. Meanwhile, the northwest mainly suffered from mild drought and a slightly wet condition during the dry season. Different from that, the RRD mainly had moderately to very wet conditions throughout the year. The areas of both agricultural and forested lands associated with severe drought in the dry season were larger than those in the wet season. Generally, VIC-based soil moisture approach offered a feasible solution for improving soil moisture and agricultural drought monitoring capabilities at the regional scale.

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Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method

Remote Sensing ◽

10.3390/rs13010133 ◽

2021 ◽

Vol 13 (1) ◽

pp. 133

Author(s):

Hao Sun ◽

Yajing Cui

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Land Surface ◽

Regional Scale ◽

Support Vector ◽

Machine Learning Method ◽

Learning Method ◽

Feed Forward Neural Network ◽

Comparative Performance ◽

Geographical Factors

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.

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The temporally varying roles of rainfall, snowmelt and soil moisture for debris flow initiation in a snow-dominated system

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3493-2018 ◽

2018 ◽

Vol 22 (6) ◽

pp. 3493-3513 ◽

Cited By ~ 10

Author(s):

Karin Mostbauer ◽

Roland Kaitna ◽

David Prenner ◽

Markus Hrachowitz

Keyword(s):

Soil Moisture ◽

Debris Flow ◽

High Intensity ◽

Debris Flows ◽

Regional Scale ◽

Early Summer ◽

Temporal Separation ◽

Antecedent Soil Moisture ◽

Trigger Mechanisms ◽

Debris Flow Initiation

Abstract. Debris flows represent frequent hazards in mountain regions. Though significant effort has been made to predict such events, the trigger conditions as well as the hydrologic disposition of a watershed at the time of debris flow occurrence are not well understood. Traditional intensity-duration threshold techniques to establish trigger conditions generally do not account for distinct influences of rainfall, snowmelt, and antecedent moisture. To improve our knowledge on the connection between debris flow initiation and the hydrologic system at a regional scale, this study explores the use of a semi-distributed conceptual rainfall–runoff model, linking different system variables such as soil moisture, snowmelt, or runoff with documented debris flow events in the inner Pitztal watershed, Austria. The model was run on a daily basis between 1953 and 2012. Analysing a range of modelled system state and flux variables at days on which debris flows occurred, three distinct dominant trigger mechanisms could be clearly identified. While the results suggest that for 68 % (17 out of 25) of the observed debris flow events during the study period high-intensity rainfall was the dominant trigger, snowmelt was identified as the dominant trigger for 24 % (6 out of 25) of the observed debris flow events. In addition, 8 % (2 out of 25) of the debris flow events could be attributed to the combined effects of low-intensity, long-lasting rainfall and transient storage of this water, causing elevated antecedent soil moisture conditions. The results also suggest a relatively clear temporal separation between the distinct trigger mechanisms, with high-intensity rainfall as a trigger being limited to mid- and late summer. The dominant trigger in late spring/early summer is snowmelt. Based on the discrimination between different modelled system states and fluxes and, more specifically, their temporally varying importance relative to each other, this exploratory study demonstrates that already the use of a relatively simple hydrological model can prove useful to gain some more insight into the importance of distinct debris flow trigger mechanisms. This highlights in particular the relevance of snowmelt contributions and the switch between mechanisms during early to mid-summer in snow-dominated systems.

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Pan-Arctic linkages between snow accumulation and growing-season air temperature, soil moisture and vegetation

Biogeosciences ◽

10.5194/bg-10-7575-2013 ◽

2013 ◽

Vol 10 (11) ◽

pp. 7575-7597 ◽

Cited By ~ 8

Author(s):

K. A. Luus ◽

Y. Gel ◽

J. C. Lin ◽

R. E. J. Kelly ◽

C. R. Duguay

Keyword(s):

Soil Moisture ◽

Air Temperature ◽

Land Surface ◽

Regional Scale ◽

Surface Characteristics ◽

Growing Season ◽

Snow Accumulation ◽

Snow Season ◽

Air Temperatures ◽

Land Surface Characteristics

Abstract. Arctic field studies have indicated that the air temperature, soil moisture and vegetation at a site influence the quantity of snow accumulated, and that snow accumulation can alter growing-season soil moisture and vegetation. Climate change is predicted to bring about warmer air temperatures, greater snow accumulation and northward movements of the shrub and tree lines. Understanding the responses of northern environments to changes in snow and growing-season land surface characteristics requires: (1) insights into the present-day linkages between snow and growing-season land surface characteristics; and (2) the ability to continue to monitor these associations over time across the vast pan-Arctic. The objective of this study was therefore to examine the pan-Arctic (north of 60° N) linkages between two temporally distinct data products created from AMSR-E satellite passive microwave observations: GlobSnow snow water equivalent (SWE), and NTSG growing-season AMSR-E Land Parameters (air temperature, soil moisture and vegetation transmissivity). Due to the complex and interconnected nature of processes determining snow and growing-season land surface characteristics, these associations were analyzed using the modern nonparametric technique of alternating conditional expectations (ACE), as this approach does not impose a predefined analytic form. Findings indicate that regions with lower vegetation transmissivity (more biomass) at the start and end of the growing season tend to accumulate less snow at the start and end of the snow season, possibly due to interception and sublimation. Warmer air temperatures at the start and end of the growing season were associated with diminished snow accumulation at the start and end of the snow season. High latitude sites with warmer mean annual growing-season temperatures tended to accumulate more snow, probably due to the greater availability of water vapor for snow season precipitation at warmer locations. Regions with drier soils preceding snow onset tended to accumulate greater quantities of snow, likely because drier soils freeze faster and more thoroughly than wetter soils. Understanding and continuing to monitor these linkages at the regional scale using the ACE approach can allow insights to be gained into the complex response of Arctic ecosystems to climate-driven shifts in air temperature, vegetation, soil moisture and snow accumulation.

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Soil moisture regimes in Mexico in a global 1.5°C warming scenario

International Journal of Climate Change Strategies and Management ◽

10.1108/ijccsm-08-2018-0062 ◽

2019 ◽

Vol 11 (4) ◽

pp. 465-482 ◽

Cited By ~ 1

Author(s):

Jesus David Gomez Diaz ◽

Alejandro I. Monterroso ◽

Patricia Ruiz ◽

Lizeth M. Lechuga ◽

Ana Cecilia Conde Álvarez ◽

...

Keyword(s):

Climate Change ◽

Soil Moisture ◽

Regional Scale ◽

Moisture Regime ◽

Monthly Precipitation ◽

Climate Change Scenarios ◽

Content Type ◽

Warming Scenario ◽

Moisture Regimes ◽

Gfdl Model

Purpose This study aims to present the climate change effect on soil moisture regimes in Mexico in a global 1.5°C warming scenario. Design/methodology/approach The soil moisture regimes were determined using the Newhall simulation model with the database of mean monthly precipitation and temperature at a scale of 1: 250,000 for the current scenario and with the climate change scenarios associated with a mean global temperature increase of 1.5°C, considering two Representative Concentration Pathways, 4.5 and 8.5 W/m2 and three general models of atmospheric circulation, namely, GFDL, HADGEM and MPI. The different vegetation types of the country were related to the soil moisture regimes for current conditions and for climate change. Findings According to the HADGEM and MPI models, almost the entire country is predicted to undergo a considerable increase in soil moisture deficit, and part of the areas of each moisture regime will shift to the next drier regime. The GFDL model also predicts this trend but at smaller proportions. Originality/value The changes in soil moisture at the regional scale that reveal the impacts of climate change and indicate where these changes will occur are important elements of the knowledge concerning the vulnerability of soils to climate change. New cartography is available in Mexico.

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Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China

Hydrology and Earth System Sciences ◽

10.5194/hess-19-615-2015 ◽

2015 ◽

Vol 19 (1) ◽

pp. 615-629 ◽

Cited By ~ 15

Author(s):

X. Han ◽

H.-J. H. Franssen ◽

R. Rosolem ◽

R. Jin ◽

X. Li ◽

...

Keyword(s):

Soil Moisture ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Regional Scale ◽

Cosmic Ray ◽

Systematic Bias ◽

Area Index ◽

Community Land Model ◽

Independent Field

Abstract. The recent development of the non-invasive cosmic-ray soil moisture sensing technique fills the gap between point-scale soil moisture measurements and regional-scale soil moisture measurements by remote sensing. A cosmic-ray probe measures soil moisture for a footprint with a diameter of ~ 600 m (at sea level) and with an effective measurement depth between 12 and 76 cm, depending on the soil humidity. In this study, it was tested whether neutron counts also allow correcting for a systematic error in the model forcings. A lack of water management data often causes systematic input errors to land surface models. Here, the assimilation procedure was tested for an irrigated corn field (Heihe Watershed Allied Telemetry Experimental Research – HiWATER, 2012) where no irrigation data were available as model input although for the area a significant amount of water was irrigated. In the study, the measured cosmic-ray neutron counts and Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products were jointly assimilated into the Community Land Model (CLM) with the local ensemble transform Kalman filter. Different data assimilation scenarios were evaluated, with assimilation of LST and/or cosmic-ray neutron counts, and possibly parameter estimation of leaf area index (LAI). The results show that the direct assimilation of cosmic-ray neutron counts can improve the soil moisture and evapotranspiration (ET) estimation significantly, correcting for lack of information on irrigation amounts. The joint assimilation of neutron counts and LST could improve further the ET estimation, but the information content of neutron counts exceeded the one of LST. Additional improvement was achieved by calibrating LAI, which after calibration was also closer to independent field measurements. It was concluded that assimilation of neutron counts was useful for ET and soil moisture estimation even if the model has a systematic bias like neglecting irrigation. However, also the assimilation of LST helped to correct the systematic model bias introduced by neglecting irrigation and LST could be used to update soil moisture with state augmentation.

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Regional scale soil moisture content estimation based on multi-source remote sensing parameters

International Journal of Remote Sensing ◽

10.1080/01431161.2019.1701723 ◽

2019 ◽

Vol 41 (9) ◽

pp. 3346-3367 ◽

Cited By ~ 5

Author(s):

Mireguli Ainiwaer ◽

Jianli Ding ◽

Nijat Kasim ◽

Jingzhe Wang ◽

Jinjie Wang

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Moisture Content ◽

Soil Moisture Content ◽

Regional Scale

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Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale

Hydrology and Earth System Sciences ◽

10.5194/hess-10-353-2006 ◽

2006 ◽

Vol 10 (3) ◽

pp. 353-368 ◽

Cited By ~ 116

Author(s):

J. Parajka ◽

V. Naeimi ◽

G. Blöschl ◽

W. Wagner ◽

R. Merz ◽

...

Keyword(s):

Soil Moisture ◽

Regional Scale ◽

Hydrologic Model ◽

Hydrologic Models ◽

Ungauged Catchments ◽

Soil Moisture Dynamics ◽

Model Efficiency ◽

The Relationship ◽

Runoff Model ◽

Moisture Dynamics

Abstract. This paper examines the potential of scatterometer data from ERS satellites for improving hydrological simulations in both gauged and ungauged catchments. We compare the soil moisture dynamics simulated by a semidistributed hydrologic model in 320 Austrian catchments with the soil moisture dynamics inferred from the satellite data. The most apparent differences occur in the Alpine areas. Assimilating the scatterometer data into the hydrologic model during the calibration phase improves the relationship between the two soil moisture estimates without any significant decrease in runoff model efficiency. For the case of ungauged catchments, assimilating scatterometer data does not improve the daily runoff simulations but does provide more consistent soil moisture estimates. If the main interest is in obtaining estimates of catchment soil moisture, reconciling the two sources of soil moisture information seems to be of value because of the different error structures.

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