Abstract

Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error (RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture.

Highlights

  • Rice (Oryza sativa L.) is one of the most important staple food crops, feeding over half of the world’s population

  • The original multi-spectral reflectances in addition to NDVI were both used as the descriptive variables in the stepwise regression, which might help to remove the effects of the NDVI saturation problem on the model performance at later growth stages

  • The overall accuracy of the entire classified map was improved remarkably from 81.8% to 91.6%. This highly accurate rice cultivation map provides an ideal basis for further analyses of rice crops in the study area

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Summary

Introduction

Rice (Oryza sativa L.) is one of the most important staple food crops, feeding over half of the world’s population. According to the IA, the regression models performed best in the heading stage for all agronomic variables, probably resulting from the higher biomass density and lower field soil and water effects compared to the early stages. In the study of Gnyp et al [72], a specific vegetation index such as NDVI or RVI (ratio vegetation index) was used as the single independent variable in the regression models They reported that NDVI became saturated at later growth stages before biomass reached 3 t/ha, which might have affected the model performance. The original multi-spectral reflectances in addition to NDVI were both used as the descriptive variables in the stepwise regression, which might help to remove the effects of the NDVI saturation problem on the model performance at later growth stages. Different measurement methods, RS instruments, physical conditions of the rice crop, sun angles, and sensor view angles, may contribute to the discrepancy in these two studies

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