Joint retrieval of growing season corn canopy LAI and leaf chlorophyll content by fusing Sentinel-2 and MODIS images

Continuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of corn canopy LAI and chlorophyll content using filtered reflectances from Sentinel-2 and MODIS data acquired during the corn growing season, which, being generally hot and rainy, results in few cloud-free Sentinel-2 images. In addition, the retrieved time series of LAI and chlorophyll content results were used to monitor the corn growth behavior in the study area. Our results showed that: (1) the joint retrieval of LAI and chlorophyll content using the proposed joint probability distribution method improved the estimation accuracy of both corn canopy LAI and chlorophyll content. Corn canopy LAI and chlorophyll content were retrieved jointly and accurately using the PROSAIL model with fused Kalman filtered (KF) reflectance images. The relation between retrieved and field measured LAI and chlorophyll content of four corn-growing stages had a coefficient of determination (R2) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1-0.2 and 0.0-0.3, respectively. (2) Kalman filtering is a good way to produce continuous high-resolution reflectance images by synthesizing Sentinel-2 and MODIS reflectances. The correlation between fused KF and Sentinel-2 reflectances had an R2 value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R2 value of 0.8598 and RMSE of 0.0404. (3) The derived continuous KF reflectances captured the crop behavior well. Our analysis showed that the LAI increased from day of year (DOY) 181 (trefoil stage) to DOY 236 (filling stage), and then increased continuously until harvest, while the chlorophyll content first also increased from DOY 181 to DOY 236, and then remained stable until harvest. These results revealed that the jointly retrieved continuous LAI and chlorophyll content could be used to monitor corn growth conditions

Early mapping of winter wheat in Henan province of China using time series of Sentinel-2 data

Accurate mapping of winter wheat in its early stages is crucial for crop growth monitoring and crop yield forecasting. However, early mapping of winter wheat using remotely sensed data is challenging because remote sensing observations can only be used for a part of the growth period. In this study, a framework was proposed for early season mapping of winter wheat using spectral and temporal information of Sentinel-2 images. First, time series of temporal and spectral features were derived using Whittaker smoothing. Subsequently, sensitivities of different parameters (i.e. input features, time interval, and length of time-series data) to early mapping were analyzed. Finally, early maps of winter wheat were generated based on optimal parameters. Results show that the earliest identifiable timing was delayed as the time interval of the time series increased. Winter wheat can be mapped in the early overwintering period (5 months before harvest) with an overall accuracy of 0.91, which is comparable to that of post-season mapping (0.94). In addition, the misclassification in early mapping was caused by uneven sample spatial patterns, natural conditions, and planting management; however, most errors can be gradually amended during the green-up and jointing periods, and the overall accuracy remained stable after the jointing stage. This study demonstrates that it is feasible to implement large-scale early mapping of winter wheat using satellite observations. The proposed approach potentially provides a reference for early mapping of other crop types in agricultural regions worldwide.</p