Analysis of Changes and Potential Characteristics of Cultivated Land Productivity based on MODIS EVI: A Case Study of Jiangsu Province, China

Cultivated land productivity is a basic guarantee of food security. This study extracted the multiple cropping index (MCI) and most active days (MAD, i.e., days when the EVI exceeded a threshold) based on crop growth EVI curves to analyse the changes and potential characteristics of cultivated land productivity in Jiangsu Province during 2001–2017. The results are as follows: (1) The MCI of 83.8% of cultivated land remained unchanged in Jiangsu, the cultivated land with changed MCI (16.2%) was mainly concentrated in the southern and eastern coastal areas of Jiangsu, and the main cropping systems were single and double seasons. (2) The changes in cultivated land productivity were significant and had an obvious spatial distribution. The areas where the productivity of single cropping system changed occupied 67.8% of the total cultivated land of single cropping system, and the decreased areas (46.5%) were concentrated in southern Jiangsu. (3) For double cropping systems, the percentages of the changed productivity areas accounting for cultivated land were 82.7% and 73.3%. The decreased areas were distributed in central Jiangsu. In addition, the productivity of the first crop showed an overall (72%) increasing trend and increased areas (40.8%) of the second crop were found in northern Jiangsu. (4) During 2001–2017, cultivated land productivity greatly improved in Jiangsu. In the areas where productivity increased, the proportions of cultivated land with productivity potential space greater than 20% in single and double cropping systems were greater than 60% and 90%, respectively. In the areas where productivity decreased, greater than 25% and 75% of cultivated land had potential space in greater than 80% of the single and double cropping systems, respectively. This result shows that productivity still has much room for development in Jiangsu. This study provides new insight for studying cultivated land productivity and provides references for guiding agricultural production.

[1]  M. P. Tuohy,et al.  Using GIS to map impacts upon agriculture from extreme floods in Vietnam , 2013 .

[2]  G. Jiang,et al.  Cultivated land productivity potential improvement in land consolidation schemes in Shenyang, China: assessment and policy implications , 2017 .

[3]  Joon Heo,et al.  Regional-scale rice-yield estimation using stacked auto-encoder with climatic and MODIS data: a case study of South Korea , 2018, International Journal of Remote Sensing.

[4]  Assessing the impact of historical and future climate change on potential natural vegetation types and net primary productivity in Australian grazing lands , 2017 .

[5]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[6]  K. Steenwerth,et al.  Cover crops enhance soil organic matter, carbon dynamics and microbiological function in a vineyard agroecosystem , 2008 .

[7]  P. Shi,et al.  Spatiotemporal patterns, relationships, and drivers of China’s agricultural ecosystem services from 1980 to 2010: a multiscale analysis , 2018, Landscape Ecology.

[8]  Inbal Becker-Reshef,et al.  Forecasting wheat yield from weather data and MODIS NDVI using Random Forests for Punjab province, Pakistan , 2017 .

[9]  Xiaobin Jin,et al.  Spatial coupling differentiation and development zoning trade-off of land space utilization efficiency in eastern China , 2019, Land Use Policy.

[10]  F. Tubiello,et al.  Global food security under climate change , 2007, Proceedings of the National Academy of Sciences.

[11]  Jiali Shang,et al.  Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean. , 2019, The Science of the total environment.

[12]  Aliza Pradhan,et al.  Potential of conservation agriculture (CA) for climate change adaptation and food security under rainfed uplands of India: A transdisciplinary approach , 2017, Agricultural Systems.

[13]  Yong-sheng Wang,et al.  The Challenges and Strategies of Food Security under Rapid Urbanization in China , 2019, Sustainability.

[14]  Chunyang He,et al.  Urban expansion brought stress to food security in China: Evidence from decreased cropland net primary productivity. , 2017, The Science of the total environment.

[15]  Ding Mingju,et al.  Spatial and temporal variations of multiple cropping index in China based on SPOT-NDVI during 1999-2013 , 2015 .

[16]  Rajbir Singh,et al.  Enhancing water and cropping productivity through Integrated System of Rice Intensification (ISRI) with aquaculture and horticulture under rainfed conditions , 2015 .

[17]  L. Burkle,et al.  A dual role for farmlands: food security and pollinator conservation , 2017 .

[18]  David M. Johnson An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States , 2014 .

[19]  S. Vicente‐Serrano,et al.  Early prediction of crop production using drought indices at different time‐scales and remote sensing data: application in the Ebro Valley (north‐east Spain) , 2006 .

[20]  Johan Bouma,et al.  Exploring land quality effects on world food supply 1 Invited paper for the Symposium: Global Carryi , 1998 .

[21]  A. Huete,et al.  MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data , 1999 .

[22]  Martin Skitmore,et al.  Spatial-temporal evolution and classification of marginalization of cultivated land in the process of urbanization , 2017 .

[23]  R. Lal,et al.  A multi-indicator assessment of peri-urban agricultural production in Beijing, China , 2019, Ecological Indicators.

[24]  G. Fischer,et al.  Climate Change and Its Impacts on China's Agriculture , 2000 .

[25]  Xiaomin Xiang,et al.  Spatial-temporal pattern changes of main agriculture natural disasters in China during 1990–2011 , 2015, Journal of Geographical Sciences.

[26]  Kurt Christian Kersebaum,et al.  Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands , 2017 .

[27]  O. Dengiz,et al.  The land productivity dynamics trend as a tool for land degradation assessment in a dryland ecosystem , 2017, Environmental Monitoring and Assessment.

[28]  S. Seneviratne,et al.  Short‐term favorable weather conditions are an important control of interannual variability in carbon and water fluxes , 2016, Journal of geophysical research. Biogeosciences.

[29]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[30]  Huajun Tang,et al.  Potential benefits of climate change for crop productivity in China , 2015 .

[31]  Qingyun Du,et al.  An Improved Evaluation Scheme for Performing Quality Assessments of Unconsolidated Cultivated Land , 2017 .

[32]  H. Haberl,et al.  Rapid growth in agricultural trade: effects on global area efficiency and the role of management , 2014 .

[33]  Hualin Xie,et al.  Determinants of cultivated land recuperation in ecologically damaged areas in China , 2019, Land Use Policy.

[34]  Wu Jin A Methodology for Multiple Cropping Index Extraction Based on NDVI Time-Series , 2008 .

[35]  Wu Bing-fang,et al.  A Methodology for Retrieving Cropping Index from NDVI Profile , 2004, National Remote Sensing Bulletin.

[36]  C. Justice,et al.  A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data , 2010 .

[37]  O. Dengiz Potential impact of land use change on land productivity dynamics with focus on land degradation in a sub-humid terrestrial ecosystem , 2018, Theoretical and Applied Climatology.

[38]  William Allan,et al.  Studies in African land usage in Northern Rhodesia , 1949 .

[39]  Wang Hong Research of Throughput Calculation Based on Agricultural Land Classification and Agriculture Statistics , 2007 .

[40]  G. Ovando,et al.  Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[41]  R. Guo,et al.  Institutional transition and implementation path for cultivated land protection in highly urbanized regions: A case study of Shenzhen, China , 2019, Land Use Policy.

[42]  Potential productivity and human carrying capacity of an agro-ecosystem: An analysis of food production potential of China , 1995 .

[43]  M. Bindi,et al.  Estimation of wheat production by the integration of MODIS and ground data , 2011 .

[44]  Wenpeng Lin,et al.  Winter wheat yield estimation based on MODIS EVI , 2005 .