Remote sensing used to detect moisture status of pecan orchards grown in a desert environment

Othman, Yahia A.; Steele, C. M.; VanLeeuwen, Dawn; Heerema, Richard J.; Bawazir, Salim; Hilaire, Rolston St.

doi:10.1080/01431161.2013.873834

Cited by 16 publications

(31 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This region of the vegetation spectrum allows the identification of vegetation stress due to drought (Serrano et al, 2000;Fensholt and Sandholt, 2003;Othman et al, 2014). The SWIR region also shows great potential for discriminating green vegetation from senescent vegetation (Daughtry et al, 2005), and for estimating the LAI of senescent vegetation.…”

Section: Introductionmentioning

confidence: 99%

Brown and green LAI mapping through spectral indices

Delegido

Verrelst

Rivera

et al. 2015

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Brown and green LAI mapping through spectral indices

Delegido

Verrelst

Rivera

et al. 2015

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

“…This means that farmers also must pump groundwater to supplement irrigation, which makes pecan vulnerable to water deficits. Low soil moisture negatively affects several physiological processes in pecan trees, such as photosynthesis (A) and gas exchange (Othman et al, 2014a). Water deficit reduced pecan yield 5% to 24% when the applied water was reduced from 5% to 52% relative to control (Garrot et al, 1993).…”

mentioning

confidence: 99%

“…This indicator variable must be sufficiently sensitive to water status so that the threshold at which irrigation starts can be determined with some precision (Jones, 2004). Midday stem water potential has been proposed for detecting moisture status and monitoring irrigation in commercial orchards, including pecan (Jones, 2004;Othman et al, 2014a). However, using y smd for irrigation scheduling, especially, on a large scale is labor intensive (therefore, expensive), slow, and unsuitable for automation (Jones, 2004).…”

mentioning

confidence: 99%

“…Remote sensing applications hold potential for predicting plant water status, growth, and development (Othman et al, 2014a;Rossi et al, 2010). Hyperspectral sensors measure reflectance in a narrow wavelength range (usually 10 nm or less) and contain hundreds of contiguous bands over the electromagnetic spectrum that can be used to estimate the biochemical properties of vegetation (Huber et al, 2008).…”

mentioning

confidence: 99%

“…In a previous study, we screened several leaf-level physiological measurements to determine which of these leaf-level parameters best represented changes in plant moisture status (Othman et al, 2014a). We concluded that y smd was the best performing physiological indicator for detecting moisture status in pecan trees (Othman et al, 2014a).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Hyperspectral Surface Reflectance Data Detect Low Moisture Status of Pecan Orchards during Flood Irrigation

Othman¹,

Steele²,

VanLeeuwen³

et al. 2015

J. Amer. Soc. Hort. Sci.

Self Cite

View full text Add to dashboard Cite

For large fields, remote sensing might permit plant low moisture status to be detected early, and this may improve drought detection and monitoring. The objective of this study was to determine whether canopy and soil surface reflectance data derived from a handheld spectroradiometer can detect moisture status assessed using midday stem water potential (ψsmd) in pecan (Carya illinoinensis) during cyclic flood irrigations. We conducted the study simultaneously on two mature pecan orchards, one in a sandy loam (La Mancha) and the other in a clay loam (Leyendecker) soil. We were particularly interested in detecting moisture status in the −0.90 to −1.5 MPa ψsmd range because our previous studies indicated this was the critical range for irrigating pecan. Midday stem water potential, photosynthesis (A) and canopy and soil surface reflectance measurements were taken over the course of irrigation dry-down cycles at ψsmd levels of −0.40 to −0.85 MPa (well watered) and −0.9 to −1.5 MPa (water deficit). The decline in A averaged 34% in La Mancha and 25% in Leyendecker orchard when ψsmd ranged from −0.9 to −1.5 MPa. Average canopy surface reflectance of well-watered trees (ψsmd −0.4 to −0.85 MPa) was significantly higher than the same trees experiencing water deficits (ψsmd −0.9 to −1.5 MPa) within the 350- to 2500-nm bands range. Conversely, soil surface reflectance of well-watered trees was lower than water deficit trees over all bands. At both orchards, coefficient of determinations between ψsmd and all soil and canopy bands and surface reflectance indices were less than 0.62. But discriminant analysis models derived from combining soil and canopy reflectance data of well-watered and water-deficit trees had high classification accuracy (overall and cross-validation classification accuracy >80%). A discriminant model that included triangular vegetation index (TVI), photochemical reflectance index (PRI), and normalized soil moisture index (NSMI) had 85% overall accuracy and 82% cross-validation accuracy at La Mancha orchard. At Leyendecker, either a discriminant model weighted with two soil bands (690 and 2430 nm) or a discriminant model that used PRI and soil band 2430 nm had an overall classification and cross-validation accuracy of 99%. In summary, the results presented here suggest that canopy and soil hyperspectral data derived from a handheld spectroradiometer hold promise for discerning the ψsmd of pecan orchards subjected to flood irrigation.