Evaluation of paddy yield and protein estimation methods based on various vegetation indices, NDSI and PLS using an airborne hyperspectral sensor AISA in Shonai Plain, Yamagata, Japan

This paper describes to evaluate rice yield and protein estimation methods based on various vegetation indices (Vis), NDSI and PLS using an airborne hyperspectral sensor AISA in Shonai plane, northeast Japan. In several developing stages, which are the tillering stage (middle June), the maximum tiller number stage (early July) and the dough ripe stage (late August), PLS has stable and high correlation for all stages. NDSI shows several discriminative wavelengths to estimate rice conditions. Vis slightly estimated those situations in the dough ripe stage. In this result, a hybrid method of PLS and NDSI, which is similar to iPLS, suggests the best estimation method for rice yield and protein.