Detecting and distinguishing moisture- and salinity-induced stress in wheat and maize through<i>in situ</i>spectroradiometry measurements

Elmetwalli, Adel Mohamed H.; Tyler, Andrew N.; Hunter, Peter; Salt, C. A.

doi:10.1080/01431161.2011.599346

Cited by 30 publications

(23 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this region, water stressed corn had higher reflectance than unstressed corn in the visible spectrum in contrast to much higher reflectance from unstressed plants when compared to lower reflectance from stressed plants in the NIR spectrum (Elmetwalli et al, 2012). Elmetwalli et al (2012) also observed that water stressed wheat (Triticum aestivum L.) canopy had higher reflectance in the visible region and lower reflectance in the NIR regions when compared to unstressed wheat canopy. Weber et al (2012) observed similar response of water stressed corn to those of Elmetwalli et al (2012) and our study.…”

Section: Figurementioning

confidence: 51%

“…Elmetwalli et al (2012) also observed that water stressed wheat (Triticum aestivum L.) canopy had higher reflectance in the visible region and lower reflectance in the NIR regions when compared to unstressed wheat canopy. Weber et al (2012) observed similar response of water stressed corn to those of Elmetwalli et al (2012) and our study. Our results were slightly different from those of Elmetwalli et al (2012) that water stressed plant could not quickly enough respond to watering to recover.…”

Section: Figurementioning

confidence: 70%

“…Alberte et al (1977) found that the majority of chlorophyll lost in response to water stress occurred in the mesophyll cells with a lesser amount being lost from the bundle sheath cells. Increasing severity of water stress clearly reduced the photochemical activity of chlorophyll, absorption of nutrients by corn roots, and nutrient transportation from root to shoots (Elmetwalli et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, research on irrigation and water management has focused on crop yield responses to water supply (Chen et al, 2010 a;Köksal, 2011). The use of remote sensing for irrigation practices, water resource management, and disease and insect management has been largely investigated (Ozdogan, 2011;Elmetwalli et al, 2012). The spectral characteristics of healthy vegetative surfaces are distinctive with low reflectance in blue, high in green, very low in red and very high in the near infrared (NIR).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Paprastojo kukurūzo (Zea mays L.) drėgmės streso nustatymas, taikant spektrinį atspindį ir klasifikavimo medžio metodą

Genç

Inalpulat

Kızıl

et al. 2013

Zemdirbyste-Agriculture

View full text Add to dashboard Cite

Water stress is one of the most important growth limiting factors in crop production. Several methods have been used to detect and evaluate the effect of water stress on plants. The use of remote sensing is deemed particularly and practically suitable for assessing water stress and implementing appropriate management strategies because it presents unique advantages of repeatability, accuracy, and cost-effectiveness over the ground-based surveys for water stress detection. The objectives of this study were to 1) determine the effect of water stress on sweet corn (Zea mays L.) using spectral indices and chlorophyll readings and 2) evaluate the reflectance spectra using the classification tree (CT) method for distinguishing water stress levels/severity. Spectral measurements and chlorophyll readings were taken on sweet corn exposed to four levels of water stress with 0, 33, 66 and 100 % of pot capacity (PC) before and after each watering time. The results demonstrated that reflectance in the red portion (600-700 nm) of the electromagnetic spectrum decreased and increased in the near infrared (NIR) region (700-900 nm) with the increasing field capacity of water level. Reflectance measured before the irrigation was generally higher than after irrigation in the NIR region and lower in the red region. However, when the four levels of PC and before or after irrigation only were compared, reflectance spectra indicated that water stressed corn plants absorbed less light in the visible and more light in the NIR regions of the spectrum than the less water stressed and unstressed plants. There was a similar trend to reflectance behaviour of water stress levels using chlorophyll readings that decreased over time. The CT analysis revealed that water stress can be assessed and differentiated using chlorophyll readings and reflectance data when transformed into spectral vegetation indices.

show abstract

Section: Figurementioning

confidence: 51%

Section: Figurementioning

confidence: 70%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Paprastojo kukurūzo (Zea mays L.) drėgmės streso nustatymas, taikant spektrinį atspindį ir klasifikavimo medžio metodą

Genç

Inalpulat

Kızıl

et al. 2013

Zemdirbyste-Agriculture

View full text Add to dashboard Cite

show abstract

“…Reduction in green leaf area due to growth limiting factors (pathogens, insect feeding, nutrient deficiencies), leaf senescence, and defoliation causes high reflectance in the visible spectrum due to chlorophyll degradation, low reflectance in NIR due to reduced green leaf area and senescence, and high reflectance in SWIR due to modified tissue chemistry [38,40,41]. There are numerous successful applications of stress and disease detection and quantification in wheat and other vegetation canopies using a variety of sensor systems including aerial photographs, airborne and satellite multispectral and hyperspectral sensors, ground-based instruments and other spatial information technologies [42][43][44][45][46][47]. Significant correlations between spectral reflectance data and symptoms of net blotch (Pyrenophora teres) in barley, glume blotch (Stagonospora nodorum) in winter wheat, and both diseases in spring wheat were reported by [48].…”

Section: Introductionmentioning

confidence: 99%

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

Mırık¹,

Ansley²,

Price³

et al. 2013

ARS

View full text Add to dashboard Cite

Wheat streak mosaic (WSM), caused by Wheat streak mosaic virus is a viral disease that affects wheat (Triticum aestivum L.), other grains, and numerous grasses over large geographical areas around the world. To improve disease management and crop production, it is essential to have adequate methods for monitoring disease epidemics at various scales and multiple times. Remote sensing has become an essential tool for monitoring and quantifying crop stress due to biotic and abiotic factors. The objective of our study was to explore the utility of Landsat 5 TM imagery for detecting, quantifying, and mapping the occurrence of WSM in irrigated commercial wheat fields. The infection and progression of WSM was biweekly assessed in the Texas Panhandle during the 2007-2008 crop years. Diseased-wheat was separated from uninfected wheat on the images using a sub-pixel classifier. The overall classification accuracies were >91% with kappa coefficient between 0.80 and 0.94 for disease detection were achieved. Omission errors varied between 2% and 14%, while commission errors ranged from 1% to 21%. These results indicate that the TM image can be used to accurately detect and quantify disease for site-specific WSM management. Remote detection of WSM using geospatial imagery may substantially improve monitoring, planning, and management practices by overcoming some of the shortcomings of the ground-based surveys such as observer bias and inaccessibility. Remote sensing techniques for accurate disease mapping offer a unique set of advantages including repeatability, large area coverage, and cost-effectiveness over the ground-based methods. Hence, remote detection is particularly and practically critical for repeated disease monitoring and mapping over time and space during the course of a growing season.

show abstract

Fusion of Three Optical Sensors for Nondestructive Detection of Water Content in Lettuce Canopies

et al. 2021

View full text Add to dashboard Cite

Detecting and distinguishing moisture- and salinity-induced stress in wheat and maize throughin situspectroradiometry measurements

Cited by 30 publications

References 9 publications

Paprastojo kukurūzo (Zea mays L.) drėgmės streso nustatymas, taikant spektrinį atspindį ir klasifikavimo medžio metodą

Paprastojo kukurūzo (Zea mays L.) drėgmės streso nustatymas, taikant spektrinį atspindį ir klasifikavimo medžio metodą

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

Fusion of Three Optical Sensors for Nondestructive Detection of Water Content in Lettuce Canopies

Contact Info

Product

Resources

About