[H-TT19] Environmental Remote Sensing

[HTT19-04] Development of a model for estimating forest transpiration using satellite remote sensing -through long-term sap flow observation data, and map of tree species data-

*Asahi HASHIMOTO¹, Chen-Wei Chiu², Yuichi Onda³, Takashi Gomi² (1.College of Geoscience, University of Tsukuba, 2.Graduate School of AgricultureDepartment of International Environmental and Agricultural Science, Tokyo University of Agriculture and Technology, 3.Center for Research on Isotopes and Environmental Dynamics, University of Tsukuba)

Keywords:Satellite remote sensing, Evapotranspiration, Sap flow, Map of tree species

Evapotranspiration is one of the major elements in the global water cycle. In recent years, there have been many water-related disasters and droughts caused by heavy rainfall. Therefore, there is a need for highly accurate estimation of evapotranspiration in forest areas that have water recharge functions. In areas covered by forests, the amount of water transferred to the atmosphere increases, especially as canopy transpiration. The estimation of canopy transpiration is an essential element in the estimation of evapotranspiration for the entire forest. Observations over a wide area are essential to obtain a complete understanding of canopy transpiration. The sap flow rate through the sapwood of trees is widely used to measure the transpiration rate. However, the actual measurement of sap flow over a wide area is difficult to achieve because it requires an enormous amount of time and labor. Satellite remote sensing is a very effective method that does not involve these issues. In previous studies, a lot of models have been developed for the estimation of total evapotranspiration in forests. The main challenges of these models are: 1. they are influenced by the type and condition of vegetation cover in the area, and 2. it is difficult to accurately distinguish between transpiration and evaporation in total evapotranspiration models. Unlike other evapotranspiration factors, canopy transpiration is affected not only by meteorological factors such as temperature and solar radiation, but also by vegetation conditions and photosynthesis. By creating a model for estimating canopy transpiration that considers these factors, it should be possible to estimate canopy transpiration with high accuracy and over a wide area. The purpose of this study was to develop a model for estimating canopy transpiration, taking into account 1. meteorological effects and 2. plant physiological effects, using satellite remote sensing. The meteorological effects were assessed using surface temperatures obtained from satellite images. Plant physiological effects were assessed using photosynthesis and leaf area as influencing factors, spectral reflectance, and vegetation index, which were obtained from multispectral data. As actual measurements of canopy transpiration, we used sap flow rates observed in Mt. Karasawa, Sano City, Tochigi Prefecture, Japan, in a cypress forest from 2011 to 2018. We used Landsat 5, 7, and 8 satellite images for the same period as the sap flow observations. In order to evaluate the meteorological effects, we calculated the density of air from the surface temperature obtained from the satellite images and investigated the relationship with the sap flow rate. As a result, it was found that there was a high correlation between density of air and sap flow rate during the months of January to March. This relationship was used to define the meteorologically influenced tree canopy transpiration rate. We investigated the seasonal variation of visible and near-infrared reflectance in a cypress forest using forest inventory data showing the distribution of tree species (around Kiryu River Dam, Gunma Prefecture) and Landsat5, 7, and 8 images from 2011 to 2018. The results suggest that near-infrared is sensitive to leaf area, while normalized indices using visible light red and visible light green are sensitive to photosynthetic rate. By using the estimated leaf area and photosynthesis, we defined the canopy transpiration rate as affected by plant physiology. The rate at which these two factors are affected varies with the season. In other words, the ratio may depend on the daily temperature to be estimated. In order to define the percentage of influence of each factor, the minimum and maximum surface temperatures from 2011 to 2018 at the sap flow measurement sites were set to 0 and 1, respectively, and an index was created to represent the surface temperature on a given day between 0 and 1. This was used to determine the meteorologically influenced and plant physiologically influenced percentages. Based on these results, a model for estimating canopy transpiration was developed, and the relationship between the measured sap flow rate and the model (for 175 days) was investigated. A high correlation of r² = 0.70 was found, confirming the validity of the model. The model developed in this study has the following advantages: canopy evapotranspiration can be estimated using only satellite images, it is not easily affected by regional climatic differences, and it considers the activity of vegetation.