Remote-Monitoring of the Physiological-ecological Status of Crops : V. Texture analysis of canopy reflectance based on optical density in photographs and remote estimation of leaf chlorophyll concentration by spectral reflectances

Japanese Journal of Crop Science

Yoshio INOUE¹ 

关键词: Diagnosis of crop growth;    画像解析;    Image analysis;    作物生育診断;    Leaf chlorophyll concentration;    テクスチュア解析;    Remote sensing;    分光反射率;    Spectral reflectance;    リモートセンシング;    Texture analysis;    葉身クロロフィル濃度;   

DOI  :  10.1626/jcs.57.105

学科分类：农业科学（综合）

来源: Nihon Sakumotsu Gakkai / Crop Science Society of Japan

【 摘 要 】

The objective of the present study was to obtain basic knowledge necessary for monitoring the physiological and ecological status of crops in fields by remote methods. The texture of reflectance within corn and soybean canopies was estimated from drum scanner analysis of color photographs, and compared with the actual distribution of leaf chlorophyll concentration (Chl) in the canopy. A model that estimated leaf Chl using multispectral remote sensing techniques was also examined. Actual Chl within the canopy had a near normal distribution (Fig. 2). However, the distribution of optical density in canopy photographs usually had a bimodal distribution pattern, which did not correspond with the actual distribution of Chl of leaves (Fig. 3A∼B, Fig. 4A∼B). The mean value of optical density was strongly affected not only by the color of each plant element but also by the degree of shading and angle of each leaf (Fig. 3C, Fig. 4C). The difference between the distribution mode and mean was reduced by avoiding the shaded parts (Fig. 4D). The spectral reflectance data from both crops revealed that wavelengths centered at 550 nm (Red) were most sensitive to the canopy Chl, while those in a near infrared region 750∼1, 050nm (NIR) were least sensitive. Statistically significant correlations were found between the ratio of NIR to Red reflectances (850nm/550nm) and leaf Chl for corn (0.68) and soybean (0.83). Differences between the regression lines for the two crops were attributed to leaf thickness (SLA)(Table 1, Fig. 5).

【 授权许可】

Unknown