Crop biomass evaluation using radiometric measurements

Baret, Frédéric; Guyot, G.; Major, D. J.

doi:10.1016/0031-8663(89)90001-x

Cited by 77 publications

(40 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The predictive ability of these models can be significantly improved by adjusting the model input data on biomass generated at certain stages of crop development. (Baret et al, 1989;Chistensen & Goudriaan, 1993). Water stress and nutrition reduces LAI for a smaller size and greater leaf senescence.…”

Section: Effect Of Intercepted Radiation and Leaf Area Index On Growtmentioning

confidence: 99%

Solar Radiation Effect on Crop Production

Campillo¹,

Fortes²,

Prieto³

2012

Solar Radiation

View full text Add to dashboard Cite

Section: Effect Of Intercepted Radiation and Leaf Area Index On Growtmentioning

confidence: 99%

Solar Radiation Effect on Crop Production

Campillo¹,

Fortes²,

Prieto³

2012

Solar Radiation

View full text Add to dashboard Cite

“…a=0.9, b=0.1 (Richardson & Wiegand, 1977) Transformed soil-adjusted vegetation index TSAVI a q TM4 À aq TM3 À b ð Þ aq TM4 þ aq TM3 À ab a=0.9, b=0.1 (Baret et al, 1989) Mid-infrared vegetation index MVI q TM4 q TM5 (Fassnacht et al, 1997) Greenness vegetation index GVI À 0:2848q TM1 À 0:2435q TM2 À 0:5436q TM3 þ 0:7243q TM4 þ 0:0840q TM5 À 0:1800q TM7 (Christ & Cicone, 1984) q-reflectance, TM-thematic mapper.…”

Section: Abbreviation Equation Referencementioning

confidence: 99%

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

Schlerf¹,

Atzberger²,

Hill³

2005

Remote Sensing of Environment

277

171

View full text Add to dashboard Cite

This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables (n=40). Ratio-based and soil-linerelated broadband VI were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI involved all possible types of two-band combinations of ratio VI (RVI) and perpendicular VI (PVI) and the red edge inflection point (REIP) computed from two techniques, inverted Gaussian Model and Lagrange Interpolation. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A PVI based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) (R 2 =0.67, RMSE=0.69 m 2 m À2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R 2 =0.77, RMSE=0.54 m 2 m À2 (17% of the mean). A PVI based on wavebands at 885 nm and 948 nm was linearly related to the crown volume (VOL) (R 2 =0.79, RMSE=0.52). VOL was derived from measured biophysical variables through factor analysis (varimax rotation). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI and VOL with good accuracy. For broadband multispectral data, the accuracy was generally lower. It can be stated that the hyperspectral data set contains more information relevant to the estimation of the forest stand variables LAI and VOL than multispectral data. When the pooled data set was analysed, soil-line-related VI performed better than ratio-based VI. When age classes were analysed separately, hyperspectral VI performed considerably better than broadband VI. Best hyperspectral VI in relation with LAI were typically based on wavebands related to prominent water absorption features. Such VI are related to the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved. D

show abstract

“…The short-term variability of the light-use efficiency is a result of temperature, nutrient and water conditions that eventually can lead to plant stress. The seasonal integration of radiometric measurements theoretically improves the capability of estimating biomass compared to one-time measurements (Baret et al, 1989;Rembold et al 2013). In the present study PAR, fAPAR and the stress factors was acquired/computed over the whole wheat season (mid October to end April) at a temporal resolution of 8 days and then integrated.…”

Section: Methodsmentioning

confidence: 99%

Operational semi-physical spectral-spatial wheat yield model development

Tripathy

Chaudhary

Nigam

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Spectral yield models based on Vegetation Index (VI) and the mechanistic crop simulation models are being widely used for crop yield prediction. However, past experience has shown that the empirical nature of the VI based models and the intensive data requirement of the complex mechanistic models has limited their use for regional and spatial crop yield prediction especially for operational use. The present study was aimed at development of an intermediate method based on the use of remote sensing and the physiological concepts such as the photo-synthetically active solar radiation (PAR) and the fraction of PAR absorbed by the crop (fAPAR) in Monteith's radiation use efficiency based equation (Monteith, 1977) for operational wheat yield forecasting by the Department of Agriculture (DoA). Net Primary Product (NPP) has been computed using the Monteith model and stress has been applied to convert the potential NPP to actual NPP. Wheat grain yield has been computed using the actual NPP and Harvest index. Kalpana-VHRR insolation has been used for deriving the PAR. Maximum radiation use efficiency has been collected from literature and wheat crop mask was derived at MNCFC, New Delhi using RS2-AWiFS data. Water stress has been derived from the Land Surface Water Index (LSWI) which has been derived periodically from the MODIS surface reflectance data (NIR and SWIR1). Temperature stress has been derived from the interpolated daily mean temperature. Results indicated that this model underestimated the yield by 3.45 % as compared to the reported yield at state level and hence can be used to predict wheat yield at state level. This study will be able to provide the spatial wheat yield map, as well as the district-wise and state level aggregated wheat yield forecast. It is possible to operationalize this remote sensing based modified Monteith's efficiency model for future yield forecasting with around 0.15 t ha-1 RMSE at state level.

show abstract

Crop biomass evaluation using radiometric measurements

Cited by 77 publications

References 23 publications

Solar Radiation Effect on Crop Production

Solar Radiation Effect on Crop Production

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

Operational semi-physical spectral-spatial wheat yield model development

Contact Info

Product

Resources

About