Abstract
As a key input variable to many global climates, land surfaces and crop models, cropping intensity (CI) accurately assesses and predicts crops’ output, in view of the global decline in food production in recent years due to declining natural resources, urban expansion and declining quality of arable land. Hence, research on CI mapping can have a contribution to solve this problem. Unfortunately, existing remote sensing data for CI mapping research, including Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images, are not adequate for obtaining CI information at higher spatial and temporal resolution. In this regard, we develop an algorithm to extract CI based on per-pixel physiognomy. To be specific, the algorithm is based on the Google Earth Engine (GEE) platform and constructs a high temporal (10 days) spatial (30 m) resolution dataset with the fusion of Landsat 7/8 and Sentinel-2 A/B image data and extracts CI information using a time series of peak discovery method, threshold method and phenological period feature extraction to obtain the 2018 Chinese Huai River Basin (HRB) CI map. Our results suggest that the overall accuracy (OA) of CI extraction in the HRB is 92.72%, with a kappa coefficient of 0.864. The single-season crop, double-season crop and three-season crop account for 41.6%, 57.7% and 0.7% of the total farmland area, respectively. Compared to existing CI identification and extraction methods, this approach achieves higher accuracy in the identification and extraction of CI information over a larger area.
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The datasets generated and/or analysed during the current study are not publicly available due personal privacy of research participants may be compromised but are available from the corresponding author on reasonable request.
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Acknowledgements
Thanks to the National Ecosystem Research Network of China, Henan Dabieshan National Field Observation & Research Station of Forest Ecosystems (Xinyang China) for providing data samples for this research and Professor Shao Zhenfeng from State Key Laboratory of Information Engineering of Surveying, Mapping and Remote Sensing, Wuhan University, for his suggestions for improvement and revision of this article.
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This study was supported by the National Natural Science Foundation of China project 42071220 (study on the internal and external mechanisms of the temporal and spatial evolution of specialized villages in the Yellow River Basin) and 41807066 (Scaling effects of runoff and sand transport in small watershed systems from the perspective of erosion energy flow), College Students’ Innovation and Entrepreneurship Training Program Project (S202010475127 and S202110475125).
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Conceptualization, Y.W.; methodology, Y.W.; data analysis and processing, Y.W., R.T. and L.F.; software, Y.W., R.T. and L.F.; validation, Y.W., L.F. and R.T.; field trips, Y.W., L.F. and R.T.; writing-original draft preparation, Y.W.; writing-review and editing, W.Z.; visualization, Y.W. and R.T.; supervision, W.Z.; funding acquisition, W.Z. L.T. and Y.W.; all authors have read and agreed to the published version of the manuscript.
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Wang, Y., Fan, L., Tao, R. et al. Research on cropping intensity mapping of the Huai River Basin (China) based on multi-source remote sensing data fusion. Environ Sci Pollut Res 29, 12661–12679 (2022). https://doi.org/10.1007/s11356-021-15387-z
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DOI: https://doi.org/10.1007/s11356-021-15387-z