Crop identification and leaf area index calculations with Landsat multitemporal data

Chance, Joseph E.

doi:10.1080/01431168108948337

Cited by 21 publications

(3 citation statements)

References 4 publications

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“…However, as atmospheric conditions vary continuously, the necessary corrections needed are impossible to know. Therefore, it was decided to assume the clear standard atmosphere of Eltermann (1964) and to introduce corrections for atmosphere, solar zenith angle and LANDSAT MSS relative responsivity in a manner similar to Chance (1981). The results are as follows, with R(A.)…”

Section: The Suits Model and Its Parameter Requirementsmentioning

confidence: 99%

A test of the Suits vegetative-canopy reflectance model with LARS soybean-canopy reflectance data

Chance

Lemaster

1985

International Journal of Remote Sensing

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The Suits vegetative-canopy reflectance model is tested with an extensive set of field reflectance measurements made by the Laboratory for Application of Remote Sensing (LARS) for soybean canopies. The model is tested for the full hemisphere of observer directions as well as the nadir direction. The results show moderate agreement for the visible channels of the LANDSAT MSS and poor agreement in the near-infrared channel of LANDSAT MSS. An analysis of errors is given.

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Section: The Suits Model and Its Parameter Requirementsmentioning

confidence: 99%

A test of the Suits vegetative-canopy reflectance model with LARS soybean-canopy reflectance data

Chance

Lemaster

1985

International Journal of Remote Sensing

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“…Remote sensing has been incrasingly used for mapping soils and land use, because (1) images of large areas can be acquired rapidly, (2) images can be of a spatial resolution that matches the degree of detail required for the survey, (3) remote sensing eliminates the problem of surface access, (4) images provide a perspective that is lacking in ground surveys, (5) image interpretation is faster and less expensive than conducting ground surveys and (6) images provide an unbiased, permanent data set that may be interpreted for a wide range of specific land use classes (Sabins, 1978).…”

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confidence: 99%

Visual and digital classification of landsat tm data for soil, physiography and land use mapping in axios alluvial plain, thessaloniki, Greece

Silleos

1988

Geocarto International

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Visual interpretation of analogue images and digital analysis of Landsat-5 TM CCT (August 1987) were applied (l) for land use/land cover classification, (2) to study the relationships between physiography, soils and land use classes of the survey area and (3) to construct an integrated land system map of Axios alluvial plain (Thessaloniki, Greece).The study of physiography and soils, as well as the final land system map were carried out by the application of visual interpretation of LANDSAT TM black/white and colour composites at 1:125,000-1:250,000 scale.Crops and land use classes were classified by (1) visual interpretation and (2) image processing techniques, using supervised and unsupervised classification algorithms, which allow the interpreter to select training sites of known features and to identify similar areas throughout the scene.The results show that at reconnaissance level and using visual interpretation, a sufficient volume of data (soil, land use, physiography) can be collected in a short term period at low cost.Computered-aided image classification was checked in 100 sites, in order to define the classification accuracy.Maximum classification accuracy (about 85% overall) was obtained using supervised classification and includes rice and other irrigated and non-irrigated crops as well as bare land.Red, red-brown colored soils (Inceptisols, Alfisols), covered with cereal straw, were distinguished well, when unsupewised classification was applied on band 6 (thermal band).Land use types are strongly correlated with physiography and soils in the survey area and four well differentiated land systems were defined with similar soils, climate, physiography, geology and climate.

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“…Spectral indices have been linked with biophysical characteristics of a crop canopy such as LA/ (Pollock and Kanemasu, 1979;Chance, 1981;Asrar et al, 1985a;Lapitan, 1986). Significant linear correlations of the spectral indices with LA/ were obtained using Landsat satellite data (Wiegand et al, 1979).…”

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Spectral Estimates of Daily Carbon Dioxide Exchange Rates in Wheat Canopies

et al. 1990

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Spectral indices have been used to predict leaf area index (LAI) and the interception efficiency (e1) of photosynthetically active radiation (PAR) within a plant canopy. The objectives of this work were to investigate the influence of canopy geometry on the temporal trends in LAI and spectral indices, and to develop equations to predict the daily canopy gross carbon dioxide exchange rates (DGCE) from daily PAR. Winter wheat (Triticum aestivum L. cv. Newton) was planted in north‐south and east‐west row orientations with 0.18‐, 0.35‐, 0.53‐, and 0.71‐m row spacings, during 1982—1983 and 1983—1984. Net CO2 exchange rates were measured with a transparent dynamic positive‐pressure chamber and a differential infrared gas analysis system. Daytime dark respiration rate correction were made to obtain the DGCE. Canopy spectral reflectance was monitored with a multispectral radiometer, while absorption of PAR was measured with quantum sensors. Daily absorbed PAR was derived by taking the product of e1, estimated from either the LAI or spectral indices, and total incoming PAR. Row orientation did not significantly influence the temporal trends in LAI or the spectral response, whereas significantly greater LAI and spectral response occurred in the narrower‐ vs. wider‐spaced canopies. A linear model characterized the relationship between DGCE and daily absorption of PAR. A common slope model (0.74 g CO2 mol−1 photon) indicated that the LAI and spectral indices had similar predictive capabilities. Good agreement occurred between an equation developed with 1984 data and measured values from 1983 (i.e., R2 0.49—0.65). Remotely sensed multispectral canopy reflectance data, therefore, can be used to estimate DGCE in wheat canopies.

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Crop identification and leaf area index calculations with Landsat multitemporal data

Cited by 21 publications

References 4 publications

A test of the Suits vegetative-canopy reflectance model with LARS soybean-canopy reflectance data

A test of the Suits vegetative-canopy reflectance model with LARS soybean-canopy reflectance data

Visual and digital classification of landsat tm data for soil, physiography and land use mapping in axios alluvial plain, thessaloniki, Greece

Spectral Estimates of Daily Carbon Dioxide Exchange Rates in Wheat Canopies

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