Articles
HIGH RESOLUTION SATELLITE IMAGERY TO DETECT STRESS IN POTATO
Article number
1038_10
Pages
97 – 104
Language
English
Abstract
Proper application rate of water and nitrogen fertilization are crucial factors for producing high quality potato tubers.
Mapping and detecting stress at both local and regional scales by remote sensing are very important in site specific management.
This work aimed to assess the ability of high spatial resolution remote sensing imagery to detect stress in potato in South-Tripoli, Libya.
A field experiment was conducted to investigate the response of potato to water and nitrogen deficiency.
A GeoEye-1 satellite imagery was planned to be acquired at the flowering stage of potato to assess the potential of the remote detection of stress in potato.
Ground reference data including soil samples, vegetation samples, water samples, chlorophyll estimates, and GPS coordinates were collected.
The filed work was timed to coincide with the acquisition of GeoEye-1 satellite imagery (21 November, 2010). The results demon¬strated that the GeoEye-1 image successfully detected stress within field and local scales, and therefore can be a robust tool in site specific management.
A strong linear relation between RVI and NDVI derived from satellite data and potato tuber yield (r =0.77). Strong correlations were also found between chlorophyll content of potato and NDVI derived from the 21st November image (r = 0.76). The results further showed that the Minimum Distance supervised algorithm (MDC) is an effective classification algorithm for differentiating different crops within the study area.
Mapping and detecting stress at both local and regional scales by remote sensing are very important in site specific management.
This work aimed to assess the ability of high spatial resolution remote sensing imagery to detect stress in potato in South-Tripoli, Libya.
A field experiment was conducted to investigate the response of potato to water and nitrogen deficiency.
A GeoEye-1 satellite imagery was planned to be acquired at the flowering stage of potato to assess the potential of the remote detection of stress in potato.
Ground reference data including soil samples, vegetation samples, water samples, chlorophyll estimates, and GPS coordinates were collected.
The filed work was timed to coincide with the acquisition of GeoEye-1 satellite imagery (21 November, 2010). The results demon¬strated that the GeoEye-1 image successfully detected stress within field and local scales, and therefore can be a robust tool in site specific management.
A strong linear relation between RVI and NDVI derived from satellite data and potato tuber yield (r =0.77). Strong correlations were also found between chlorophyll content of potato and NDVI derived from the 21st November image (r = 0.76). The results further showed that the Minimum Distance supervised algorithm (MDC) is an effective classification algorithm for differentiating different crops within the study area.
Authors
A.H. Elmetwalli, T.Z. Fouda, E.Y. Ali
Keywords
remote sensing, water, nitrogen, deficiency, crops
Online Articles (86)