Nobuhiro Matsuoka

A proposal for universal formulas for estimating leaf water status of herbaceous and woody plants based on spectral reflectance properties

The present study deals with the relationships between water status parameters of plant leaves and reflectances (Rλ) at characteristic wavelengths, between 522 and 2450 nm, as well as reflectance ratios, Rλ/R1430, Rλ/R1650, Rλ/R1850, Rλ/R1920, and Rλ/R1950, based on the air-drying experimental results of soybean (Glycine max Merr.), maize (Zea mays L.), tuliptree (Liriodendron tulipifera L.) and viburnum (Viburnum awabuki K. Koch.) plants. The water status parameters include leaf water content per unit leaf area (LWC), specific leaf water content (SWC), leaf moisture percentage of fresh weight (LMP), relative leaf water content (RWC) and relative leaf moisture percentage on fresh weight basis (RMP). Effective spectral reflectances and reflectance ratios for estimating the LWC, SWC, LMP, RWC and RMP were identified. With these spectral indices, approaches to estimating LWC, RWC and RMP were discussed. Eventually, an attempt on universal formulas was made for estimating the leaf moisture conditions of both herbaceous and woody plants as mentioned above. Moreover, applicability of these formulas was checked with the field experimental results of soybean and maize grown under water and nutrient stresses.

A combination model for estimating stomatal conductance of maize (Zea mays L.) leaves over a long term

Abstract In this study, an environmental variable model for estimating stomatal conductance of maize (Zea mays L.) leaves over a long term is developed, based on research results concerning responses of stomatal conductance to environmental variables. This model is actually a combination of optimized formulae for potential stomatal conductance during daytime, PSC, and for the relative degree of stomatal opening during daytime, RDO, with a basic form expressed as a multiplication of inter-day and intra-day factors. We call this environmental variable model as the combination model. Compared with other models presented in the past, this combination model is convenient in practical use because it can estimate not only maximum and average values but also instantaneous values of stomatal conductance during daytime. Also, the theoretical description of this model has been clarified by introducing into it the concept of environmental stress. As compared with Jarvis-type multiplication environmental variable models, we think this combination model is a refinement and development of the Jarvis-type models.

Spatial and Temporal Relationships among NDVI, Climate Factors, and Land Cover Changes in Northeast Asia from 1982 to 2009

This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact.

The effects of sand dust storms on greenhouse gases

In Asia, sand dust storms (SDSs) occur nearly every year, especially in northern China. However, there is little research about the relationship between SDSs and greenhouse gases (GHGs). In this article, we selected four SDSs that occurred in Asia in the spring of 2009 and 2010. We monitored the areas covered by these SDSs using Moderate Resolution Imaging Spectroradiometer (MODIS) data, then we used Greenhouse Gases Observing Satellite (GOSAT) data to check how the SDSs affected the concentrations of CO2 and CH4. We then compared the concentrations of CO2 and CH4 on SDS days with the monthly mean values of the months in which SDSs occurred. We also compared the concentrations of CO2 and CH4 on SDS days with the values before and after the SDSs. After analysis, we found that SDSs had increased the concentrations of CO2 and CH4 in the atmosphere. When the SDSs occurred, the concentrations of CO2 and CH4 increased and reached peak values on the last or penultimate days of the storms and then decreased to their normal values. Atmospheric flow is the main reason for the increase in concentration of CO2, and the lack of the free radical (OH) during SDSs and the presence of CH4 sources in southeast China are the main reasons for the increase in concentration of CH4. We also found that in arid and semi-arid areas, SDSs had little effect on the concentration of these two GHGs.