Potentials and limits of vegetation indices for LAI and APAR assessment

Kumar and Monteith's (1981) model for the remote sensing of crop growth has been used to estimate continental net primary productivity (NPP) as well as its seasonal and spatial variations. The model assumes a decomposition of NPP into independent parameters such as incident solar radiation (S0), radiation absorption efficiency by canopies (ƒ), and conversion efficiency of absorbed radiation into organic dry matter (e). The precision on some of the input parameters has been improved, compared to previous uses of this model at a global scale: remote sensing data used to derive ƒ have been calibrated, corrected of some atmospheric effects, and filtered; e has been considered as biome-dependent and derived from literature data. The resulting global NPP (approximatively 60 GtC per year) is within the range of values given in the literature. However, mean NPP estimates per biome do not agree with the literature (in particular, the estimation for tropical rain forests NPP is much lower and for cultivations much higher than field estimates), which results in zonal and seasonal variations of continental NPP giving more weight to the temperate northern hemisphere than to the equatorial zone.

Methodology for the estimation of terrestrial net primary production from remotely sensed data

https://pubag.nal.usda.gov/pubag/downloadPDF.xhtml?id=36762&content=PDF

Vegetation indices in crop assessments

Estimating surface soil moisture and leaf area index of a wheat canopy using a dual-frequency (C and X bands) scatterometer

The use of remote sensing as a tool for estimating crop production on a regional scale has been suggested for quite a while. Since the use of remotely sensed information to directly estimate crop production is questionable (primarily due to the indirect link between remotely sensed data and crop state variables), crop models may be used as a companion tool to remote sensing. Various types of crop models (statistical, deterministic, semi-empirical) are described, and specific methods (forcing, recalibration, statistical correction) are described for introducing remotely sensed information into models. The appropriance of combining different model types and sources of remotely sensed information, problems related to time scales, and the need for robust and independent phenological routines are also discussed.

Remote sensing and crop production models: present trends

(…) La relation entre l'indice de vegetation (DN) combinant les reflectances spectrales rouge et proche infra-rouge, et la fraction PAR absorbee par le couvert au cours de la journee (e i ), est analysee a partir de simulations du modele de transfert radiatif SAIL (…) Des mesures de l'indice de vegetation ont ete effectuees au sol sur des cultures de ble en phase d'implantation. Simultanement, la grandeur e i a ete estimee a partir de photographies hemispheriques prises au-dessus des cultures. Les relations experimentales obtenues entre DN et e i sont en bonne concordance avec la relation (a). L'exposant α varie de 1,33 a 2,22 suivant les conditions d'eclairement, rendant la relation entre DN et e i non lineaire

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J.C. Monterrot

Papers

Estimation à partir de mesures de réflectance spectrale du rayonnement photosynthétiquement actif absorbé par une culture de blé