Agriculture is the foundation of the national economy. Identification of agricultural information by using remote sensing technique in real-time have been a hot topic. This paper aims to study the distribution of the main crops (soybean, corn, rice) effectively in large scale. Firstly, with the Asymmetric Gaussians method of TIMESAT software, the MOD09Q1 datasets with 250 m resolution were used to filter and reconstruct the time-series NDVI curves. Then seven phenological characteristics (start time of the growth season, end time of the growth season, length of the season, amplitude of NDVI, left derivative of NDVI at the beginning of the growth season, right derivative of NDVI at the end of the growth season and integral of NDVI during the growth season) were extracted. Secondly, to analyze the characteristics of time-series NDVI curve of vegetables, water and construction land were masked off because their maximum NDVI values were less than 0.5. Then in order to get the optimal classification accuracy of the crop land, hierarchical classification method was conducted as below: 1) using SVM classification to extract agricultural area based on the time-series NDVI data; 2) using SVM classification to identify three crop classes (soybean, corn, rice) with different combination of three bands (NDVI: NDVI bands; PH: phonological bands; NDWI: NDWI bands) on the basis of the first step. We compare the Overall Accuracy and Kappa coefficient of different combinations, and the result was as below: NDVI+NDWI>NDVI+PH+NDWI>PH+NDWI>NDVI+PH>NDVI>PH, the combination of NDVI+NDWI being the best. It was found that higher dimensions won't bring higher accuracy necessarily, and the application of NDWI can improve the overall accuracy of rice effectively. In addition, it is workable to identify the crop types with the help of phonological information.

Photochemical Reflectance Index (PRI) is an effective index to rapidly estimate Light Use Efficiency (LUE). The analysis of various factors influencing the relationship between these two variables is the premise for improving the precision of estimating LUE. In this paper, three kinds of wetland plants Salix matsudana, Tamarix chinensis and Phragm itescommunis in Hangzhou Bay were selected, and the diurnal variations of their photosynthesis and the synchronous vegetation spectrum were measured. At the same time, the PROSPECT-SAIL model was used to simulate the Leaf Area Index (LAI) of canopies from small to large vegetation, and the effect of LAI on the relationship between canopy PRI and LUE were analyzed. The results showed that: 1) the diurnal variations of PRI and LUE all have good relations for Phragm itescommunis, Salix matsudana and Tamarix chinensis, and the R² were 0.581 6, 0.524 6 and 0.514 6, respectively; however the correlation between PRI and LUE of the same vegetation in different periods of growth will be weakened, since PRI can't accurately estimate the LUE when the canopy LAI is small. 2) When LAI < 5, the soil background has great influence on canopy PRI, and the low brightness of soil has less affected on the canopy PRI. 3) When LAI > 5, the soil background has little influence on canopy PRI, and canopy LAI itself is the key factor that influence the relationships between PRI and LUE. Therefore, the relationships between PRI and LUE needs further exploration, such as model improving and more parameters should be researched in order to constantly improve the accuracy of the LUE estimation.