Effect of wetness on turfgrass canopy reflectance

Madeira, Ana Carla; Gillespie, T. J.; Duke, C.

doi:10.1016/s0168-1923(00)00230-6

Cited by 15 publications

(12 citation statements)

References 26 publications

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“…Such pixels can consequently be assigned a higher level of uncertainty as described above. 4) Lastly, NDWI time series may be sensitive to water intercepted by leaves and to abrupt increase in soil moisture (Madeira et al, 2001;Xiao et al, 2002aXiao et al, , 2002b. Given the fact that our algorithm uses NDWI thresholds and does not retain isolated NDWI peaks, possible impacts of these effects on phenology are likely to be limited.…”

Section: Discussionmentioning

confidence: 99%

Determination of phenological dates in boreal regions using normalized difference water index

Delbart

Kergoat

Toan

et al. 2005

Remote Sensing of Environment

305

253

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Monitoring and understanding plant phenology are important in the context of studies of terrestrial productivity and global change. Vegetation phenology, such as dates of onsets of greening up and leaf senescence, has been determined by remote sensing using mainly the normalized difference vegetation index (NDVI). In boreal regions, the results suffer from significant uncertainties because of the effect of snow on NDVI. In this paper, SPOT VEGETATION S10 data over Siberia have been analysed to define a more appropriate method. The analysis of time series of NDVI, normalized difference snow index (NDSI), and normalized difference water index (NDWI), together with an analysis of in situ phenological records in Siberia, shows that the vegetation phenology can be detected using NDWI, with small effect of snow. In spring, the date of onset of greening up is taken as the date at which NDWI starts increasing, since NDWI decreases with snowmelt and increases with greening up. In the fall, the date of onset of leaf coloring is taken as the date at which NDWI starts decreasing, since NDWI decreases with senescence and increases with snow accumulation. The results are compared to the results obtained using NDVI-based methods, taking in situ phenological records as the reference. NDWI gives better estimations of the start of greening up than NDVI (reduced RMSE, bias and dispersions, and higher correlation), whereas it does not improve the determination of the start of leaf coloring. A map of greening up dates in central Siberia obtained from NDWI is shown for year 2002 and the reliability of the method is discussed. D

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Section: Discussionmentioning

confidence: 99%

Determination of phenological dates in boreal regions using normalized difference water index

Delbart

Kergoat

Toan

et al. 2005

Remote Sensing of Environment

305

253

View full text Add to dashboard Cite

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“…Leaf wetness has been found to influence canopy reflectance in a range of plants, especially in visible wavelengths (Pinter, 1986;Madeira et al, 2000;Guan and Nutter, 2001).…”

Section: External Factors Influencing Reflectance Measurementsmentioning

confidence: 99%

“…Cloud cover can decrease reflectance (Tumbo et al, 2002;Madeira et al, 2000), while wind can increase variability in reflectance measurements (Lord et al, 1985).…”

Section: External Factors Influencing Reflectance Measurementsmentioning

confidence: 99%

Diurnal Variability in Reflectance Measurements from Cotton

Oliveira

Scharf

2014

Crop Science

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Diurnal variability of reflectance measurements on crop plants has been documented for passive sensors, but minimally studied for active sensors. Active sensors are now commercially available for crop management decisions including diagnosis and control of N rate. Our objective was to quantify variability during the day for both passive and active sensors mounted above a cotton canopy, and to assess the effect that this variability would have on N rate decisions. Measurements were taken from 0600 to 2000 h at early square, midsquare, and early flower growth stages using Cropscan (passive), Crop Circle (active), and GreenSeeker (active) reflectance sensors. Average CV for GreenSeeker was higher than for Crop Circle for visible/near‐infrared (Vis/NIR) (P = 0.01) and possibly for normalized difference vegetative index (NDVI) as well (P = 0.15). Variability in Cropscan reflectance was much higher in north‐south than in east‐west rows. When restricted to east‐west rows, Cropscan CVs were equal to or lower than the other sensors. Nitrogen rates calculated from sensor measurements also were more variable for GreenSeeker (SD ≥ 19 kg N ha–1) than for Crop Circle (SD ≥ 12 kg N ha–1) (P < 0.09). GreenSeeker NDVI followed a consistent diurnal pattern: lower in midday and higher in morning and evening. A regression equation using temperature, solar radiation, and solar time explained 45 to 50% of this variability, and could potentially be used to correct values during field use. Additional study to isolate causal factors of variability is justified. Strategies to compensate for drift in reflectance measurements will be needed when using sensors to control applications of N fertilizer or other inputs, even for active sensors.

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“…Since dewfall represents a latent heat flux at a surface, it can be estimated from the energy balance of an atmosphere-canopy system, if the other components of the EB are available either from measurement or calculation [17][18][19][20][21]. Dewfall estimated with the EB equation is generally in good agreement with measurements.…”

Section: Introductionmentioning

confidence: 93%

Agreement of Four Equations for Computing Dewfall in Northern Germany

Xiao

Meißner

Borg

et al. 2017

Water

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Abstract:The energy balance (EB), turbulent vapour transport (TVT), Penman-Monteith (PM) and Bowen ratio energy balance (BREB) equation were used to estimate dewfall based on meteorological data. Initially there were big disagreements between the estimates from these four equations. However, after multiplying the heat and vapour conductance terms by 0.33 the agreement was much better. This implies that the disagreements derived from improper conductance values. Initially we did not consider the effect of atmospheric stability on the conductances. With stability correction the conductances were on average 0.5 times the values without stability correction. To arrive at the aforementioned 0.33, the conductances with stability correction still need to be lower by a factor of 0.66. The value of the von Karman constant and the relationships for the zero plane displacement and the roughness length we used in our conductance computations are widely used, but not the only possible ones. With different values and relationships also suggested in the literature one can reach this factor. However, it is also possible that our wind speed data contributed to the fact that the conductances we computed were too high. Their computation for a given canopy-atmosphere system requires wind speeds from a wind profile in equilibrium with the vegetation. This in turn requires an adequate fetch around the investigated surface. The highly varied vegetation in and around the site where the study was conducted makes adequate fetch rather doubtful. To obtain valid conductance values the atmospheric stability conditions must be considered, the appropriate values for the von Karman constant, the zero plane displacement and roughness length must be used, and there must be adequate fetch. The BREB equation does not contain a conductance term and therefore does not suffer from the problems just stated. The other three equations do. However, the BREB, like the EB and TVT equations, need the surface temperature which is not routinely measured. This then leaves the PM equation from which this temperature has been eliminated as the only option. Hence, in a future study dewfall estimates from the PM equation should be compared with direct measurements with a high precision weighing lysimeter.

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Effect of wetness on turfgrass canopy reflectance

Cited by 15 publications

References 26 publications

Determination of phenological dates in boreal regions using normalized difference water index

Determination of phenological dates in boreal regions using normalized difference water index

Diurnal Variability in Reflectance Measurements from Cotton

Agreement of Four Equations for Computing Dewfall in Northern Germany

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