Seasonal spectral response patterns of winter wheat canopy for crop performance monitoring

Rumiana Kancheva; Georgi Georgiev

doi:10.1117/12.2029196

16 October 2013 Seasonal spectral response patterns of winter wheat canopy for crop performance monitoring

Rumiana Kancheva, Georgi Georgiev

Proceedings Volume 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV; 88871V (2013) https://doi.org/10.1117/12.2029196
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

Agricultural monitoring is an important and continuously spreading activity in remote sensing and applied Earth observations. It supplies valuable information on crop condition and growth processes. Much research has been carried out on vegetation phenology issues. In agriculture, the timing of seasonal cycles of crop activity is important for species classification and evaluation of crop development, growing conditions and potential yield. The correct interpretation of remotely sensed data, however, and the increasing demand for data reliability require ground-truth knowledge of the seasonal spectral behaviuor of different species and their relation to crop vigour. For this reason, we performed groundbased study of the seasonal response of winter wheat reflectance patterns to crop growth patterns. The goal was to quantify crop seasonality by establishing empirical relationships between plant biophysical and spectral properties in main ontogenetic periods. Phenology and agr-specific relationships allow to assess crop condition during different portions of the growth cycle and thus effectively track plant development and make yield predictions. The applicability of different vegetation indices for monitoring crop seasonal dynamics, health condition, and yield potential was examined.

Citation Download Citation

Rumiana Kancheva and Georgi Georgiev "Seasonal spectral response patterns of winter wheat canopy for crop performance monitoring", Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 88871V (16 October 2013); https://doi.org/10.1117/12.2029196

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KEYWORDS

Vegetation

Agriculture

Remote sensing

Reflectivity

Data modeling

Mendelevium

Spectral models

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