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Food security has become a serious concern recently in Southeast Asia. The reduction of agricultural land because of economic development is decreasing the food supply. Simultaneously, due to rapid population growth, the food demand is increasing. Therefore, to ensure a stable food supply, it is important to estimate the supply capability of rice, which is the staple food in most Asian countries. In this study, a crop model (SIMRIW-RS) that can combine remote sensing data with a crop model (SIMRIW) was used to estimate rice yield at a regional scale. This model was applied to the estimation of rice yield in paddy fields located in the suburbs of Vientiane, Laos. Satellite (COSMO-SkyMed)-derived data for leaf area index (LAI) were integrated into SIMRIW-RS, and the transplanting date detected by COSMO-SkyMed was used to set the starting date of the simulation. Results were verified by surveying farmers. Transplanting dates were detected with high accuracy in all but a few fields. On the basis of the results of regression analysis between actual LAIs and the corresponding backscatter coefficients of COSMO-SkyMed, we suggest that COSMO-SkyMed can estimate LAIs at early growth stages when LAI is small. The results of yield estimation after integrating the LAIs derived from COS-MO-SkyMed data into SIMRIW-RS indicated that the estimation accuracy of the rice yield was improved compared with the estimation result without adjusting parameters in the model, and this held so long as LAI was retrieved with high accuracy by satellite data. However, when LAI could not be estimated accurately, integration has the potential to worsen the model's accuracy compared with the estimation result without any such readjustment. This study therefore indicates that SIMRIW-RS has the potential to estimate rice yield accurately when the LAI of rice is estimated with high accuracy from satellite data.

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Monitoring the vertical distribution of leaf area index (LAI) is an effective method for evaluating canopy photosynthesis and biomass productivity. In this study, we proposed a novel method to characterize LAI vertical distribution non-destructively by utilizing LAI-2200 plant canopy analyzer, followed by the application of statistical moment equations. Field experiments were conducted with 5 rice cultivars under 2 fertilizer treatments in 2013 and with 3 rice cultivars under 3 plant density treatments in 2014. LAI readings obtained by a plant canopy analyzer for non-destructive stratified measurements were relatively consistent with LAI estimations using the stratified clipping method for every cultivar and treatment. The parameters calculated using the statistical moment equations numerically showed the changes in LAI vertical distribution with plant growth up to the heading stage. The differences in the parameters also quantified the effect of cultivar, fertilizer, and plant density treatments. These results suggest that the non-destructive stratified measurements and the statistical moments evaluated in this study provide quantitative, reliable information on the dynamics of LAI vertical distribution. The method is expected to be utilized by researchers in various research fields sharing common interests.