Spatial and Temporal Distribution of Multiple Cropping Indices in the North China Plain Using a Long Remote Sensing Data Time Series

Abstract

Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province.

ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products

“…Phenology is a key indicator of vegetation growth and development and plays an important role in vegetation monitoring (Qiu et al, 2015;Tao et al, 2017;Zhong et al, 2016). Accurate information on the timing of key crop phenological stages is critical for determining the optimal timing of agronomic management options, reliable simulations of crop growth and yield, and analyzing the plant response to climate change (Bolton and Friedl, 2013;Brown et al, 2012;Chen et al, 2018a;Sakamoto et al, 2010Sakamoto et al, , 2013Wang et al, 2015;Zhang and Tao, 2013).…”

“…According to some previous studies, it could improve the accuracy of model estimation at a large scale by assimilating reliable remote-sensing data into crop growth models (Bolten et al, 2010;Nearing et al, 2012;Ines et al, 2013;Chen et al, 2018a;Huang et al, 2015;Zhou et al, 2019;de Wit and van Diepen, 2007). Among the state variables used in the assimilation, phenology is one of the essential variables because of its critical roles in affecting dry matter accumulation and distribution during the growing stages and reflecting of crop periodic biological changes influenced by various environmental conditions (e.g., climate; Jin et al, 2018;Zheng et al, 2016).…”

High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data

“…Simultaneously, multiple cropping results in a series of subsequent environment and social issues, such as agricultural land use, water use and nutrient applications, cropland managements, and the reciprocal feedback of climate changes [7][8][9][10]. Information regarding the spatial distribution of multiple cropping is therefore essential for a wide range of applications and studies [11][12][13][14].…”

ChinaCropPhen1km: A high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on LAI products

“…Previous studies have proposed different smoothing methods to reduce the noise of GLASS LAI time series, and found the OFP method varied by studied times, areas, and objectives (Zhao et al, 2016;Wang et al, 2018). Three popular methods were chosen in the study to smooth the LAI time-series curves, including the Double Logistic (DL) method, Savitzky-Golay (S-G) filter method, and Wavelet-based filter (WF) method.…”