Xiang Hu’s research while affiliated with Fuzhou University and other places

Complex cropping patterns with crop diversity are an underexploited treasure for global food security. However, significant methodological and dataset gaps in fully characterizing cropland cultivated with multiple crops and rotation sequences hinder our ability to understand and promote sustainable agricultural systems. Existing crop mapping models are challenged by the deficiency of ground reference data and the limited transferability capabilities across large spatial domains. This study aimed to fill these gaps by proposing a robust Complex Cropping Pattern Mapping framework (CCPM) capable of national-scale automatic applications using the Sentinel-1 SAR and Sentinel-2 MSI time series datasets. The CCPM framework addresses these challenges by integrating knowledge-based approaches & data-driven algorithms (Dual-driven model) and Phenological Normalization. The CCPM framework was implemented over conterminous China with complex cropping systems dominated by smallholder farms, and the first national-scale 10-m Cropping pattern map with descriptions of cropping intensity and 10 crops in China (ChinaCP-T10) in 2020 was produced. The efficiency of the CCPM framework was validated when evaluated by 18,706 ground-truth reference datasets, with an overall accuracy of 91.47 %. Comparisons with existing crop data products revealed that the ChinaCP-T10 offered more comprehensive and consistent information on diverse cropping patterns. Dominant cropping patterns diversified from single maize in northern China, winter wheat-maize in North China Plain, single oilseeds in Western China, to single rice or double rice in Southern China. The key cropping patterns changed from double-grain cropping, single grain to single cash cropping with increasing altitudes. There were 151,744 km 2 planted areas of double grain cropping patterns in China, and multiple cropping accounted for 36.1 % of grain cultivated area nationally. Over 80 % of grain production was mainly implemented at lower altitudes as the Non-Grain Production (NGP) ratio enhanced from 32 % within elevations below 200 m to over 70 % among elevations above 700 m. Consistent datasets on complex cropping patterns are essential, given the significant roles of diversification and crop rotations in sustainable agriculture and the frequently observed inconsistencies in existing crop data products based on thematic mapping.

Multiple cropping is a widespread agricultural intensification for increasing food production. National-scale Cropping Intensity (CI) mapping is important for achieving sustainable development goals. However, previous studies have largely relied on vegetation indices (VI) waves for detecting valid cropping cycles, which is challenged by the complexity of agricultural systems. i.e. winter crops with multiple VI waves and double rice with less distinctive waves. This study proposed a robust framework for Mapping cropping Intensity through better-characterizing crop Life cycles based on combined considerations of vegetative and productive Stages (MILS). The number of cropping cycles was estimated by the frequency of valid coupled patterns of vegetation and brownness indices from Sentinel-2 (S2) MultiSpectral Instrument (MSI) time series and further improved through fusing Sentinel-1 (S1) SAR and S2 data to reduce the omission errors of double rice. The MILS algorithm was implemented using the Google Earth Engine platform and applied to China, which is dominated by smallholder farms with diverse cropping patterns. This study produced the first 10 m updated CI map over conterminous China (CIChina10m) in 2020 by fusing S1 and S2 time series. The CIChina10m had an overall accuracy of 93.93% when validated with 14,468 widely spread reference sites. The cropping intensity was 1.2769 on the national scale, which illustrated higher values in the Middle and lower reaches of the Yangtze River plain (CI = 1.5879) and South China (CI = 1.5503). There were 1086,620, 412,620 and 1,441 km 2 areas cultivated by single, double and triple cropping in China, which accounted for 72.4%, 27.5% and 0.1% of cropland, respectively. The proposed MILS algorithm showed good performances in detecting complex agricultural systems, which can be further applied to generate continental or global 10-m CI products with good quality. Codes of the MILS algorithm are publicly available at https://code.earthengine.google.com/a7f24f76291bf901ee25a130025a7ce6, and the first 10m national CI data products in China with good accuracy (ChinaCI10m) are also publicly accessed at https://doi.org/10.6084/m9.figshare.23939505.

Multiple cropping is a widespread approach for intensifying crop production through rotations of diverse crops. Maps of cropping intensity with crop descriptions are important for supporting sustainable agricultural management. As the most populated country, China ranked first in global cereal production and the percentages of multiple-cropped land are twice of the global average. However, there are no reliable updated national-scale maps of cropping patterns in China. Here we present the first recent annual 500-m MODIS-based national maps of multiple cropping systems in China using phenology-based mapping algorithms with pixel purity-based thresholds, which provide information on cropping intensity with descriptions of three staple crops (maize, paddy rice, and wheat). The produced cropping patterns maps achieved an overall accuracy of 89% based on ground truth data, and a good agreement with the statistical data (R ² ≥ 0.89). The China Cropping Pattern maps (ChinaCP) are available for public download online. Cropping patterns maps in China and other countries with finer resolutions can be produced based on Sentinel-2 Multispectral Instrument (MSI) images using the shared code.