Identification of Infiltration Features and Hydraulic Properties of Soils Based on Crop Water Stress Derived from Remotely Sensed Data.

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F21%3A43902925" target="_blank" >RIV/60076658:12220/21:43902925 - isvavai.cz</a>

Alternative codes found

RIV/00027049:_____/21:N0000047 RIV/00027049:_____/21:N0000048 RIV/00027006:_____/21:10174589

Result on the web

<a href="https://www.mdpi.com/2072-4292/13/20/4127" target="_blank" >https://www.mdpi.com/2072-4292/13/20/4127</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs13204127" target="_blank" >10.3390/rs13204127</a>

Result language

angličtina

Original language name

Identification of Infiltration Features and Hydraulic Properties of Soils Based on Crop Water Stress Derived from Remotely Sensed Data.

Original language description

Knowledge of the spatial variability of soil hydraulic properties is important for many reasons, e.g., for soil erosion protection, or the assessment of surface and subsurface runoff. Nowadays, precision agriculture is gaining importance for which knowledge of soil hydraulic properties is essential, especially when it comes to the optimization of nitrogen fertilization. The present work aimed to exploit the ability of vegetation cover to identify the spatial variability of soil hydraulic properties through the expression of water stress. The assessment of the spatial distribution of saturated soil hydraulic conductivity (Ks) and field water capacity (FWC) was based on a combination of ground-based measurements and thermal and hyperspectral airborne imaging data. The crop water stress index (CWSI) was used as an indicator of crop water stress to assess the hydraulic properties of the soil. Supplementary vegetation indices were used. The support vector regression (SVR) method was used to estimate soil hydraulic properties from aerial data. Data analysis showed that the approach estimated Ks with good results (R2 = 0.77) for stands with developed crop water stress. The regression coefficient values for estimation of FWC for topsoil (0–0.3 m) ranged from R2 = 0.38 to R2 = 0.99. The differences within the study sites of the FWC estimations were higher for the subsoil layer (0.3–0.6 m). R2 values ranged from 0.12 to 0.99. Several factors affect the quality of the soil hydraulic features estimation, such as crop water stress development, condition of the crops, period and time of imaging, etc. The above approach is useful for practical applications for its relative simplicity, especially in precision agriculture.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10511 - Environmental sciences (social aspects to be 5.7)

Project

<a href="/en/project/TH02030133" target="_blank" >TH02030133: Agriculture management system integrating efficient nutrients utilization by crops and water conservation against non-point source pollution</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Remote Sensing

ISSN

2072-4292

e-ISSN

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Volume of the periodical

13

Issue of the periodical within the volume

4127

Country of publishing house

CH - SWITZERLAND

Number of pages

25

Pages from-to

20

UT code for WoS article

000714484900001

EID of the result in the Scopus database

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