欧亚国际

来自以色列的科学家在知名期刊Remote Sens发表了题为A Hyperspectral-Physiological Phenomics System: Measuring Diurnal Transpiration Rates and Diurnal Reflectance一篇新文章，科学家开发了一种新型高光谱生理系统，该系统可监控植物对非生物胁迫改变时的植物动态反馈。系统是一款传感器到植物（sensor-to-plant ）平台，可测定一天最佳时间，生理性状能成功通过光谱均值鉴别出来。直接测量性状包括即时与每日蒸腾速率以及植物重量定期的增减。该系统监控和评估了辣椒作物对不同水准钾肥使用的反馈。在07:00-10:00和14:00-17:00，处理间的即时蒸腾速率有着显著差异。

A novel hyperspectral-physiological system that monitors plants dynamic response to abiotic alterations was developed. The system is a sensor-to-plant platform which can determine the optimal time of day during which physiological traits can be successfully identified via spectral means. The directly measured traits include momentary and daily transpiration rates throughout the daytime and daily and periodical plant weight loss and gain. The system monitored and evalsuated pepper plants response to varying levels of potassium fertilization. Significant momentary transpiration rates differences were found between the treatments during 07:00-10:00 and 14:00-17:00. The simultaneous frequently measured high-resolution spectral data provided the means to correlate the two measured data sets. Significant correlation coefficients between the spectra and momentary transpiration rates resulted with a selection of three bands (ρ523, ρ697 and ρ818nm) that were used to capture transpiration rate differences using a normalized difference formula during the morning, noon and the afternoon. These differences also indicated that the best results are not always obtained when spectral (remote or proximal) measurements are typically preformed around noon (when solar illumination is the highest). Valuable information can be obtained when the spectral measurements are timed according to the plants' dynamic physiological status throughout the day, which may vary among plant species and should be considered when planning remote sensing data acquisition. 

A Hyperspectral-Physiological Phenomics System: Measuring Diurnal Transpiration Rates and Diurnal Reflectance
Shahar Weksler et. al. (2020), Remote Sens 12, 1493; DOI:10.3390/rs12091493