Analysis of surface soil moisture patterns in agricultural landscapes using empirical orthogonal functions

Abstract. Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture. Nevertheless, its spatio-temporal patterns in agriculturally used landscapes affected by multiple natural (rainfall, soil, topography etc.) and agronomic (fertilisation, soil management etc.) factors are often not well known. The aim of this study is to determine the dominant factors governing the spatio-temporal patterns of surface soil moisture in a grassland and an arable land test site within the Rur catchment in Western Germany. Surface soil moisture (0–6 cm) has been measured in an approx. 50×50 m grid at 14 and 17 dates (May 2007 to November 2008) in both test sites. To analyse spatio-temporal patterns of surface soil moisture, an Empirical Orthogonal Function (EOF) analysis was applied and the results were correlated with parameters derived from topography, soil, vegetation and land management to connect the pattern to related factors and processes. For the grassland test site, the analysis results in one significant spatial structure (first EOF), which explains about 57.5% of the spatial variability connected to soil properties and topography. The weight of the first spatial EOF is stronger on wet days. The highest temporal variability can be found in locations with a high percentage of soil organic carbon (SOC). For the arable land test site, the analysis yields two significant spatial structures, the first EOF, explaining 38.4% of the spatial variability, shows a highly significant correlation to soil properties, namely soil texture. The second EOF, explaining 28.3% of the spatial variability, is connected to differences in land management. The soil moisture in the arable land test site varies more during dry and wet periods on locations with low porosity.

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Analysis of surface soil moisture patterns in agricultural landscapes using Empirical Orthogonal Functions

Hydrology and Earth System Sciences ◽

10.5194/hess-14-751-2010 ◽

2010 ◽

Vol 14 (5) ◽

pp. 751-764 ◽

Cited By ~ 53

Author(s):

W. Korres ◽

C. N. Koyama ◽

P. Fiener ◽

K. Schneider

Keyword(s):

Soil Moisture ◽

Spatial Variability ◽

Soil Properties ◽

Land Management ◽

Surface Soil ◽

Temporal Patterns ◽

Test Site ◽

Agricultural Landscapes ◽

Surface Soil Moisture ◽

Spatio Temporal

Abstract. Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture. Nevertheless, its spatio-temporal patterns in agriculturally used landscapes that are affected by multiple natural (rainfall, soil, topography etc.) and agronomic (fertilisation, soil management etc.) factors are often not well known. The aim of this study is to determine the dominant factors governing the spatio-temporal patterns of surface soil moisture in a grassland and an arable test site that are located within the Rur catchment in Western Germany. Surface soil moisture (0–6 cm) was measured in an approx. 50×50 m grid during 14 and 17 measurement campaigns (May 2007 to November 2008) in both test sites. To analyse the spatio-temporal patterns of surface soil moisture, an Empirical Orthogonal Function (EOF) analysis was applied and the results were correlated with parameters derived from topography, soil, vegetation and land management to link the patterns to related factors and processes. For the grassland test site, the analysis resulted in one significant spatial structure (first EOF), which explained 57.5% of the spatial variability connected to soil properties and topography. The statistical weight of the first spatial EOF is stronger on wet days. The highest temporal variability can be found in locations with a high percentage of soil organic carbon (SOC). For the arable test site, the analysis resulted in two significant spatial structures, the first EOF, which explained 38.4% of the spatial variability, and showed a highly significant correlation to soil properties, namely soil texture and soil stone content. The second EOF, which explained 28.3% of the spatial variability, is linked to differences in land management. The soil moisture in the arable test site varied more strongly during dry and wet periods at locations with low porosity. The method applied is capable of identifying the dominant parameters controlling spatio-temporal patterns of surface soil moisture without being affected by single random processes, even in intensively managed agricultural areas.

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The role of vegetation and soil properties on the spatio-temporal variability of the surface soil moisture in a maize-cropped field

Journal of Hydrology ◽

10.1016/j.jhydrol.2013.03.007 ◽

2013 ◽

Vol 489 ◽

pp. 148-159 ◽

Cited By ~ 49

Author(s):

G. Baroni ◽

B. Ortuani ◽

A. Facchi ◽

C. Gandolfi

Keyword(s):

Soil Moisture ◽

Soil Properties ◽

Temporal Variability ◽

Surface Soil ◽

Surface Soil Moisture ◽

Spatio Temporal

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Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India).

10.5194/egusphere-egu21-8125 ◽

2021 ◽

Author(s):

Cécile Gomez ◽

Dharumarajan Subramanian ◽

Philippe Lagacherie ◽

Jean Riotte ◽

Sylvain Ferrant ◽

...

Keyword(s):

Soil Moisture ◽

Soil Properties ◽

South India ◽

Surface Soil ◽

Soil Surface ◽

Calibration Data ◽

Surface Soil Moisture ◽

Soil Color ◽

Color Changes ◽

Sentinel 2

Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modification of the landscape, calling for new methods to identify these changes. A striking example of anthropogenic modifications of soil properties is the widespread practice in South India of applying large quantities of silt from dry river dams (or &#8220;tanks&#8221;) to agricultural fields. Whereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. Over pedological contexts characterized by Vertisol, Ferralsols and Chromic Luvisols in sub-humid and semi-arid Tropical climate, this practice is characterized by an application of black-colored tank silt providing from Vertisol, to red-colored soils such as Ferralsols. The objective of this work was to evaluate the usefulness of Sentinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application.We used data collected in a cultivated watershed (Berambadi, Karnataka state, South India) including 217 soil surface samples characterized in terms of Munsell color. We used two Sentinel-2 images acquired on February 2017 and April 2017. The surface soil color over each Sentinel-2 image was classified into two-class (&#8220;Black&#8221; and &#8220;Red&#8221; soils). A change of soil color from &#8220;Red&#8221; in February 2017 to &#8220;Black&#8221; in April 2017 was attributed to tank silt application. Soil color changes were analyzed accounting for possible surface soil moisture changes. The proposed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibration dataset thanks to the Synthetic Minority Over-sampling Technique (SMOTE) methodology, coupled to the Cost-Sensitive Classification And Regression Trees (Cost-Sensitive CART) algorithm. To estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from &#8220;Red&#8221; to &#8220;Black&#8221;, a bootstrap procedure was used providing fifty two-class classifications for each Sentinel-2 image.The results showed that 1) the CART method allowed to classify the &#8220;Red&#8221; and &#8220;Black&#8221; soil with overall accuracy around 0.81 and 0.76 from the Sentinel-2 image acquired on February and April 2017, respectively, 2) a tank silt application was identified over 97 fields with high confidence and over 107 fields with medium confidence, based on the bootstrap results and 3) the identified soil color changes are not related to a surface soil moisture change between both dates. With the actual availability of the Sentinel-2 and the past availability of the LANDSAT satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.

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Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

Remote Sensing ◽

10.3390/rs12081242 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1242 ◽

Cited By ~ 2

Author(s):

Sumanta Chatterjee ◽

Jingyi Huang ◽

Alfred E. Hartemink

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Soil Properties ◽

Soil Water ◽

Surface Soil ◽

Reference Network ◽

Surface Soil Moisture ◽

Ancillary Data ◽

Terrain Parameters ◽

The U.S

Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backscatter data, terrain parameters, and land cover. The approach shows the potential for retrieving SSM using Sentinel-1 data in a combination of high-resolution ancillary data across the conterminous United States (CONUS). Future work is required to improve the model performance by including more SSM network measurements, assimilating Sentinel-1 data with other microwave, optical and thermal remote sensing products. There is also a need to improve the spatial resolution and accuracy of land surface parameter products (e.g., soil properties and terrain parameters) at the regional and global scales.

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Influence of cracking clays on satellite estimated and model simulated soil moisture

Hydrology and Earth System Sciences ◽

10.5194/hess-14-979-2010 ◽

2010 ◽

Vol 14 (6) ◽

pp. 979-990 ◽

Cited By ~ 21

Author(s):

Y. Y. Liu ◽

J. P. Evans ◽

M. F. McCabe ◽

R. A. M. de Jeu ◽

A. I. J. M. van Dijk ◽

...

Keyword(s):

Soil Moisture ◽

Surface Temperature ◽

Soil Properties ◽

Surface Soil ◽

Clay Soils ◽

Soil Types ◽

Soil Porosity ◽

Surface Soil Moisture ◽

Near Surface ◽

Community Land Model

Abstract. Vertisols are clay soils that are common in the monsoonal and dry warm regions of the world. One of the characteristics of these soil types is to form deep cracks during periods of extended dry, resulting in significant variation of the soil and hydrologic properties. Understanding the influence of these varying soil properties on the hydrological behavior of the system is of considerable interest, particularly in the retrieval or simulation of soil moisture. In this study we compare surface soil moisture (θ in m3 m−3) retrievals from AMSR-E using the VUA-NASA (Vrije Universiteit Amsterdam in collaboration with NASA) algorithm with simulations from the Community Land Model (CLM) over vertisol regions of mainland Australia. For the three-year period examined here (2003–2005), both products display reasonable agreement during wet periods. During dry periods however, AMSR-E retrieved near surface soil moisture falls below values for surrounding non-clay soils, while CLM simulations are higher. CLM θ are also higher than AMSR-E and their difference keeps increasing throughout these dry periods. To identify the possible causes for these discrepancies, the impacts of land use, topography, soil properties and surface temperature used in the AMSR-E algorithm, together with vegetation density and rainfall patterns, were investigated. However these do not explain the observed θ responses. Qualitative analysis of the retrieval model suggests that the most likely reason for the low AMSR-E θ is the increase in soil porosity and surface roughness resulting from cracking of the soil. To quantitatively identify the role of each factor, more in situ measurements of soil properties that can represent different stages of cracking need to be collected. CLM does not simulate the behavior of cracking soils, including the additional loss of moisture from the soil continuum during drying and the infiltration into cracks during rainfall events, which results in overestimated θ when cracks are present. The hydrological influence of soil physical changes are expected to propagate through the modeled system, such that modeled infiltration, evaporation, surface temperature, surface runoff and groundwater recharge should be interpreted with caution over these soil types when cracks might be present. Introducing temporally dynamic roughness and soil porosity into retrieval algorithms and adding a "cracking clay" module into models are expected to improve the representation of vertisol hydrology.

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