Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

• 作者: Jae-Hyun RYU Dohyeok OH Jaeil CHO
• 来源期刊: 农业科学学报（英文版）
• 年，卷（期）: 2021,（7）
• 所属期刊栏目: Agro-Ecosystem & Environment
• 页码范围: 1969-1986
• 页数: 18页
• 语种: 英文
• DOI: 10.1016/S2095-3119(20)63410-4

摘要

A spectral reflectance sensor (SRS) fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). NDVI is useful for indicating crop growth/phenology, whereas PRI was developed for observing physiological conditions. Thus, the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system. However, capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions, such as solar irradiance and precipitation. Further, unlike growth/phenology, the physiological condition has diurnal changes as well as seasonal characteristics. This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice, barley, and garlic. First, the measurement accuracy of SRSs was compared with handheld spectrometers, and the R2 values between the two devices were 0.96 and 0.81 for NDVI and PRI, respectively. Second, the experimental study of threshold criteria with respect to meteorological variables (i.e., insolation, cloudiness, sunshine duration, and precipitation) was conducted, and sunshine duration was the most useful one for excluding distorted values of the vegetation indices. After data processing based on sunshine duration, the R2 values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004 (NDVI) and 0.065–0.298 (PRI) on the three crops, and the seasonal signals of vegetation indices became noticeably improved. This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.