Light Interception and Leaf Area Estimates from Measurements of Grass Canopy Reflectance

Asrar, Ghassem; Kanemasu, E. T.; Miller, George P.; Weiser, Russell L.

doi:10.1109/tgrs.1986.289590

Cited by 58 publications

(26 citation statements)

References 15 publications

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“…Links between NDVI and LAI have frequently been established in the past (e.g., Asrar et al 1986, although results have not been consistent owing to a number of factors, particularly the combined e ects of canopy structure and sun-sensor geometry. Hall et al ( 1992 ) noted that SVI-LAI relationships improved when green LAI was used instead of total LAI, which suggests that SVIs respond primarily to the photosynthetically active components of vegetation (i.e., green foliage).…”

Section: Multi-sensor Multi-date Comparisons Of Surface Re¯ectance Amentioning

confidence: 99%

Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site

Goetz¹

1997

International Journal of Remote Sensing

318

137

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A unique remotely sensed data set derived for a temperate mixed grassland in the central United States was used to test the comparability of a suite of satellite and aircraft sensors, and to characterize temporal variability in the normalized di erence vegetation index (NDVI), retrieved surface radiant temperature ( Ts), and surface biophysical variables. The temporal evolution of atmospherically corrected NDVI images through two growing seasons was found to be consistent among sensors. Maximum NDVI compositing of AVHRR data compared favourably with Landsat TM and SPOT-HRV, despite large variations in individual band re¯ectances with viewing geometry. Surface radiant temperatures retrieved from the various sensors, including aircraft TM-simulators, were also comparable, and in good agreement with surface measurements after radiometric calibration and atmospheric correction (2´7ß C rmse). The relationship between NDVI and Ts was determined largely by vegetation and environmental conditions (e.g., leaf area index and soil moisture), and was thus related to the partitioning of energy¯uxes. The NDVI/ Ts slopes were also a ected by acquisition time, but were not signi® cantly di erent among sensors over the growing season. These results suggest that data from di erent sensors can be used to augment spatial and temporal characteristics of datasets, when calibrated and corrected. Such capability diminishes the trade-o of spatial resolution at the expense of temporal resolution (and vice versa), thus allowing observation of short-term variations in biospheric processes.

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Section: Multi-sensor Multi-date Comparisons Of Surface Re¯ectance Amentioning

confidence: 99%

Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site

Goetz¹

1997

International Journal of Remote Sensing

318

137

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“…The choice of the method may depend on several factors including the purpose of the research, the structure of the vegetation, the size of the area to be assessed, repeatability of the sampling, accuracy required, scientific traditions, time and workforce available (Catchpole and Wheeler, 1992). In grass and shrub dominated communities, the most widely used methods for estimating aboveground biomass are: (1) visual cover estimation of abundance (Braun-Blanquet, 1932, Peet et al 1998, Sykes et al 1983, (2) line intercept (Canfield, 1941) and point intercept (Goodall, 1952, Jonasson, 1988 methods, and more recently, (3) ground-based remote sensing techniques, such as field spectroscopy (Milton et al 2009, Pearson et al 1976, light interception methods (Asrar et al 1986) and image analysis (Röttgermann et al 2000).…”

Section: Introductionmentioning

confidence: 99%

Comparing the accuracy of three non-destructive methods in estimating aboveground plant biomass

et al. 2017

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Abstract:Aboveground plant biomass is one of the most important features of ecosystems, and it is widely used in ecosystem research. Non-destructive biomass estimation methods provide an important toolkit, because the destructive harvesting method is in many cases not feasible. However, only few studies have compared the accuracy of these methods in grassland communities to date. We studied the accuracy of three widely used methods for estimation of aboveground biomass: the visual cover estimation method, the point intercept method, and field spectroscopy. We applied them in three independent series of field samplings in semi-arid sand grasslands in Central Hungary. For each sampling method, we applied linear regression to assess the strength of the relationship between biomass proxies and actual aboveground biomass, and used coefficient of determination to evaluate accuracy. We found no evidence that the visual cover estimation, which is generally considered as a subjective method, was less accurate than point intercept method or field spectroscopy in estimating biomass. Based on our three datasets, we found that accuracy was lower for the point intercept method compared to the other two methods, while field spectroscopy and visual cover estimation were similar to each other in the semi-arid sand grassland community. We conclude that visual cover estimation can be as accurate for estimating aboveground biomass as other approaches, thus the choice amongst the methods should be based on additional pros and cons associated with each of the method and related to the specific research objective. Nomenclature:The Plant List (2010).

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“…Radiative transfer models have been used to simulate reflection characteristics of vegetation canopies as a function of canopy-structure, physiological parameters and LAI (Sellers, 1985;Asrar, 1986). Those models focus on the photo-synthetically active radiation (PAR) in the range of 0.4-0.7 ~ and on the abovementioned visible and NIR channels.…”

Section: Vegetation and Leaf Area Index (Lai)mentioning

confidence: 99%

Satellite Remote Sensing and Energy Balance Modelling for Water Balance Assessment in (Semi-) Arid Regions

Griend

Gurney

1988

Estimation of Natural Groundwater Recharge

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The terms of the water balance in semi-arid regions may be monitored using different types of remotely sensed information from satellites. Such an integrated approach focusses on the possibilities of monitoring the soil moisture status and evapotranspiration over time from the combination of (a) thermal infrared, (b) visible and near infrared and (c) passive microwave remote sensing.Application of such an approach is expected to give a better insight into the spatial and temporal variability of soil moisture content and evapotranspiration, and therefore may contribute to a better understanding of large scale recharge phenomena in semi-arid regions. Actual recharge depends on subsurface hydrogeological conditions for percolation to underlying aquifers, and may vary between localized concentrated percolation on the one hand and more regional diffuse percolation on the other. Thus a subsurface scaling problem comes to the fore which is beyond the remote sensing approach described in this paper which focusses on the average surface conditions at pixel scale, as a possible boundary condition for recharge.The surface energy balance may be modelled using thermal infrared surface temperature observations and large scale near surface meteorological information, which allows the evapotranspiration rate to be estimated and the soil moisture status to be inferred together with stress conditions of the vegetation. This requires a remotely sensed estimate of the vegetation cover and biomass which may be derived from visible and NIR signatures. Separately, the soil moisture status of the top soil may be derived from passive microwave signatures. This paper illustrates the approach of using all these data together and describes some aspects of an energy and mass balance model to be used in the data analysis for scarcely vegetated areas, such as in semi-arid regions. 89 I. Simmers (ed.), Estimation of Natural Groundwater Recharge, 89-116.

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Light Interception and Leaf Area Estimates from Measurements of Grass Canopy Reflectance

Cited by 58 publications

References 15 publications

Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site

Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site

Comparing the accuracy of three non-destructive methods in estimating aboveground plant biomass

Satellite Remote Sensing and Energy Balance Modelling for Water Balance Assessment in (Semi-) Arid Regions

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