Time Tracking of Different Cropping Patterns Using Landsat Images under Different Agricultural Systems during 1990–2050 in Cold China

Abstract

Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China contains different agricultural systems (state and private farming), and such systems could lead to different cropping patterns. So far, such changes have not been revealed yet. Based on the Landsat images, this study tracked cropping information in five-year increments (1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015) and predicted future patterns for the period of 2020–2050 under different agricultural systems using developed method for determining cropland patterns. The following results were obtained: The available time series of Landsat images in Cold China met the requirements for long-term cropping pattern studies, and the developed method exhibited high accuracy (over 91%) and obtained precise spatial information. A new satellite evidence was observed that cropping patterns significantly differed between the two farm types, with paddy field in state farming expanding at a faster rate (from 2.66 to 68.56%) than those in private farming (from 10.12 to 34.98%). More than 70% of paddy expansion was attributed to the transformation of upland crop in each period at the pixel level, which led to a greater loss of upland crop in state farming than private farming (9505.66 km2 vs. 2840.29 km2) during 1990–2015. Rapid cropland reclamation is projected to stagnate in 2020, while paddy expansion will continue until 2040 primarily in private farming in Cold China. This study provides new evidence for different land use change pattern mechanisms between different agricultural systems, and the results have significant implications for understanding and guiding agricultural system development.

Dynamic Variation of Ecosystem Services Value under Land Use/Cover Change in the Black Soil Region of Northeastern China

“…Natural resources, such as water, may have a significant influence on this land transition, notably paddy expansion for increased food production. We found that there were abundant water resources to increase the paddy area in the BSRNC, which has an average surface runoff of 2634.3 × 10 8 m 3 /year and precipitation of 567.5 × 10 8 m 3 /year [ 36 ]. In this context, it is evident that water supply resources provide favorable conditions for agricultural production, which can also explain the expansion of paddy land area during the study period.…”

“…The black soil region of northeastern China (BSRNC) is one of the four black soil regions in the world. This area represents the area with the largest amount of grain production in China, accounting for 1/4 of the country’s total grain output [ 36 , 37 ]. The BSRNC has evolved from natural forest and grassland ecosystems to artificial farmland ecosystems [ 38 ].…”

Editorial of Special Issue “Remote Sensing for Land Cover/Land Use Mapping at Local and Regional Scales”

“…Pan et al [2], using a time series of Landsat images, developed an approach for tracking cropping information in 5-year increments between 1990 and 2015 in Cold China. Based on the analysis of the trajectory transformation over this 35-year period, and a cellular automata/Markov Change prediction model the cropping patterns were extrapolated for the epochs of 2020, 2030, 2040, and 2050.…”

“…Land use and land cover mapping is full connected with change detection and quantification [1], and four [2][3][4][5] of the Special Issue articles, apart from generating local to regional Land-Use/Land-Cover (LULC) products with innovative or state-of the art methods, also analyzed multi-temporal change trajectories. Multi-temporal Landsat imagery was used in six of the articles [2][3][4][5][6][7], while two contributions [8,9] were developed using more complex hyperspectral and Light Detection and Ranging (LiDAR)/radar Earth Observation (EO) data. Four articles [3,5,6,9] presented approaches relying on EO data fusion, in line with the large number of available Earth Observation sensors.…”

The Differentiation in Cultivated Land Quality between Modern Agricultural Areas and Traditional Agricultural Areas: Evidence from Northeast China

“…The top province for grain output in China is Heilongjiang province, which produced 11.3% of Chinese grain in 2020. Heilongjiang province possesses 12.5% of China's high-standard cultivated land; however, it also contains two agricultural concepts, namely modern agricultural area (MA) and traditional agricultural area (TA) [50]. MA implements a management system with business enterprises in state-owned farms so that cultivated land ownership is managed and controlled by the state.…”