Search

In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated. [ABSTRACT FROM AUTHOR]

• Redundant information of the imagery was removed by the feature selection method. • U-Net algorithm is more suitable for crop mapping in the irrigation district. • The key early window period of major crop mapping was determined. • The crop mapping models with strong versatility in the early period were obtained. The information on spatial distribution pattern and area of different crops is particularly useful for monitoring and managing the sustainability of agricultural resources. However, there are still challenges in timely mapping of crop types and planting areas to support production during the present growing season. Here, we proposed to use time-series Sentinel-2 imagery and a deep learning method for major crop mapping (wheat, maize, sunflower, and squash) in the agricultural production areas in the Hetao Irrigation District (HID), Inner Mongolia Autonomous Region, China. A feature selection method that combines the global separability index and feature recursive elimination was proposed to optimize the band features of Sentinel-2 imagery. The Random Forest, Extreme Gradient Boosting, U-Net and deeplabv3+ algorithms were then used for image classification with all spectral bands and the selected band features, respectively. The selected bands and best method were used to determine the key time window for the early identification of four major crops. Integrate all image datasets in the early window period, and build mapping models suitable for images at different times in the window period by combining the selected bands and best method. The results showed that the proposed feature selection method effectively removed redundant features and improved the classification efficiency. The U-Net algorithm was more suitable for the classification of major crops in HID with a higher accuracy whether using all band features or the selected features. The key periods for early recognition of wheat, maize, sunflower, and squash in HID were mid-May, late June, early July, and late June, respectively. The constructed models for crop type mapping in the early period were tested in 2020 and 2021 in the HID, and the F1-score, the mean intersection-over-union and overall accuracy were 86.71 %, 84.66 %, and 98.89 %, respectively. The proposed feature selection method, U-Net classification algorithm, and screening of the early mapping window period in this study provide an efficient, accurate and timely crop mapping tool in HID. Future research can further verify the applicability of the early identification time window proposed in this study to support other agricultural management activities in the areas. [ABSTRACT FROM AUTHOR]