Field-scale corn growth monitoring using time series LAI

Crop growth monitoring is an important work for crop management and yield prediction. Aimed to solve the problem of mixed spectrum resulting from small agricultural land plots in China, this study used high and medium spatial resolution images including Landsat-7 ETM+, Landsat-8 OLI, GF-1, and HJ-1 A/B images for corn growth monitoring. In order to avoid too much reliance on the Normalized Difference Vegetation Index(NDVI) parameters, leaf area index(LAI) was selected as the corn growth monitoring parameter and the PROSAIL radiative transfer model was used to retrieve LAI. Three indices(LAI ratio to previous year(RPLAI),vegetation condition index based on LAI(LVCI), and mean vegetation condition index based on LAI(MLVCI)) were used for real-time monitoring of corn growth. The results of a case study in the852 Farm of Heilongjiang Farms & Land Reclamation Administration in 2015 indicate that:(1)The simultaneous GF-1 image reflentance and the Landsat-8 OLI image reflentance are highly correlated. The correlation coefficients R~2 of near-infrared bands, green bands, and red bands of the GF-1 image and the Landsat-8 OLI image are 0.9320, 0.7339, and 0.7153, respectively. This is the precondition for establishing the time series LAI for corn growth monitoring using multi-source remote sensing images.(2) The accuracy of retrieving LAI using PROSAIL radiative transfer model is high——the correlation coefficient R~2 is 0.8030 and the root mean squared error(RMSE)is 0.7675. The retrieved time series LAI indicate that LAI increased quickly at the end of June,reached the maximum at the end of July or the beginning of August, and started to decrease at the end of August.(3) The RPLAI, LVCI, and MLVCI indices were used for the real-time monitoring of corn growth and the results indicate that the growth in 2015 was at an average level, and corn growth in the northern part of the area was better than in the southern part.