Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Autor: Diego Varga, Jaime Bernal-Riobo, Maria Casamitjana, Maria C. Torres-Madronero
Jazyk: angličtina
Rok vydání: 2020
Předmět:
010504 meteorology & atmospheric sciences
andosols
0208 environmental biotechnology
Multispectral image
Soil science
02 engineering and technology
Sòls -- Humitat -- Colòmbia
01 natural sciences
lcsh:Technology
Normalized Difference Vegetation Index
lcsh:Chemistry
remote sensing
Agricultural land
General Materials Science
Instrumentation
Water content
Image resolution
lcsh:QH301-705.5
0105 earth and related environmental sciences
Fluid Flow and Transfer Processes
Soil moisture -- Colombia
Land use
lcsh:T
Sòls -- Humitat -- Teledetecció
Process Chemistry and Technology
General Engineering
Vegetation
lcsh:QC1-999
020801 environmental engineering
Computer Science Applications
Andosol
Soil moisture -- Remote sensing
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Environmental science
soil moisture
lcsh:Engineering (General). Civil engineering (General)
lcsh:Physics
Zdroj: Applied Sciences, Vol 10, Iss 5540, p 5540 (2020)
Applied Sciences, 2020, vol. 10, núm. 16, p. 5540
Articles publicats (D-G)
DUGiDocs – Universitat de Girona
instname
Applied Sciences
Volume 10
Issue 16
ISSN: 2076-3417
Popis: Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI
the normalized difference vegetation index, NDVI
the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.
Databáze: OpenAIRE
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