Abstract
China is a large grain producer and consumer. Thus, obtaining information about the cropping intensity (CI) in cultivated land, as well as understanding the intensified utilization of cultivated land, is important to ensuring an increased grain production and food security for China. This study aims to detect and map the changes in CI over a period of 36 years across China’s core grain-producing area—the North China Plain (NCP)— using remotely sensed leaf area index (LAI) time series data acquired by the Global LAnd Surface Satellite (GLASS) products. We first selected 2132 sample points that consisted entirely, or almost entirely, of cultivated cropland from all pixels; the biennial LAI curves for the sample points were then extracted; the Savitzky–Golay filter and second-order difference algorithm were then applied to reconstruct the biennial LAI curves and obtain the number of peaks in these curves. In addition, the multiple cropping index (MCI) was calculated to represent the CI. Finally, the spatial distribution of the CI of cultivated land on the NCP was mapped from 1982 to 2018 using a geo-statistical kriging approach. Spatially, the results indicate that the CI of cultivated land over the NCP exhibits a distinct spatial pattern that conforms to “high in the south, low in the north”. The single cropping system (SCS) mainly occurred in the higher latitude area ranging from 37.04°N to 42.54°N, and the double cropping system (DCS) mainly existed in the lower latitude area between 31.95°N and 39.97°N. Temporally, the CI increased over the study period, but there were some large fluctuations in CI from 1982 to 1998 and it maintained relatively stable since 2000. Across the NCP, 68.14% of cultivated land experienced a significant increase in CI during the 36-year period, while only 3.87% showed a significant decrease. We also found that, between 1982 and 2018, the northern boundary of the area for DCS underwent a significant westward expansion and northward movement. Our results show a good degree of consistency with statistical data and previous research and also help to improve the reliability of satellite-based identification of CI using low spatial resolution LAI products. The results provide important information that can be used for analyzing and evaluating the rational utilization of cultivated land resources; thus, ensuring food security and realizing agricultural sustainability not only for the NCP, but for China as a whole. These results also highlight the value of satellite remote sensing to the long-term monitoring of cropping intensity at large scales.
Highlights
The peak shown occurred between day 120 and day 300 of a year; In the case of a unimodal peak, the leaf area index (LAI) value at the peak should not be less than two; In the case of multimodal peaks, the LAI value at the smaller peak should not be less than 40% of the value of the highest peak
There was a high degree of consistency between the extraction results for the single cropping system (SCS) and double cropping system (DCS)—the corresponding kappa coefficient was 0.65, which indicated that the extraction results for cropping systems on the North China Plain (NCP) obtained using interpolation corresponded well to the results obtained directly from LAI data
Based on Global LAnd Surface Satellite (GLASS) LAI data, in this study, S-G filtering and a second-order difference algorithm were used to extract the distribution of cropping intensity (CI) in cultivated land across the NCP
Summary
CI is often measured by the multiple cropping index (MCI), which is the planting frequency of crops in the same farmland in one year; MCI equals to the ratio of the total sown area of crops to the arable land area in one year and can be obtained by three main methods [11] These include a statistical method based on the ratio of the crop-sown area to the total cultivated area, the potential estimation model, which is based on an analysis of the factors that limit increases in the MCI, and the spatial-monitoring method, which is based on a long-term series of remote sensing data [12]. For fragmented cultivated areas, it is hard to improve the accuracy of mapping CI based on moderate- or low-resolution remote sensing data [16,17]
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