Detecting and Assessing Nondominant Farmland Area with Long-Term MODIS Time Series Images

While most land use and land cover (LULC) studies have focused on modeling, change detection and driving forces at the class or categorical level, few have focused on the subclass level, especially regarding the quality change within a class such as farmland. The concept of nondominant farmland area (NAF) is proposed in this study to assess within class variability and quantify farmland areas where poor environmental conditions, unsuitable natural factors, natural disasters or unsustainable management practices lead to poor crop growth and thus low yield. A 17-year (2000–2016) time series of the Normalized Difference Vegetation Index (NDVI) was used to develop a NAF extraction model with abnormal features in the NDVI curves and subsequently applied to Heilongjiang province in China. The NAF model was analyzed and assessed from three aspects: agricultural disasters, soil types and medium- and low-yield fields, to determine dominant factors of the NAF patterns. The results suggested that: (1) the NAF model was able to extract a variety of NAF types with an overall accuracy of ~80%. The NAF area accumulated more than 8 years in 17 years is 6.20 thousand km2 in Heilongjiang Province, accounting for 3.75% of the total cultivated land area; (2) the NAF had significant spatial clustering characteristics and temporal variability. 53.24% of the NAF accumulated more than 8 years in 17 years is mainly concentrated in the west of Heilongjiang Province. The inter-annual NAF variability was related with meteorological variations, topography and soil properties; and (3) the spatial and temporal NAF patterns seem to reflect a cumulative impact of meteorological disasters, poor farmland quality, and soil degradation on crop growth. The determinant factors of the observed NAF patterns differed across regions, and must be interpreted in the local context of topography, soil properties and meteorological environment. Spatial and temporal NAF variability could provide useful, diagnostic information for precision farmland management.

[1]  S. Drake,et al.  Effects of aeolian deposition on soil properties and crop growth in sandy soils of northern China , 2007 .

[2]  Behzad Rayegani,et al.  Remotely sensed data capacities to assess soil degradation , 2016 .

[3]  陈玉洁,et al.  The Spatio-temporal Pattern Change and Optimum Layout of Grain Production in the West of Northeast China , 2016 .

[4]  Zhengwei Yang,et al.  Preface: Recent Advances in Remote Sensing for Crop Growth Monitoring , 2016, Remote. Sens..

[5]  Martha C. Anderson,et al.  Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery , 2017 .

[6]  Y. Xue,et al.  Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis , 2012 .

[7]  N. Ramankutty,et al.  Influence of extreme weather disasters on global crop production , 2016, Nature.

[8]  P. Tittonell,et al.  Practical assessment of soil degradation on smallholder farmers' fields in Zimbabwe: Integrating local knowledge and scientific diagnostic indicators , 2017 .

[9]  J. Steibel,et al.  Cover crop effect on corn growth and yield as influenced by topography , 2014 .

[10]  X. Mao,et al.  The coupled impact of plastic film mulching and deficit irrigation on soil water/heat transfer and water use efficiency of spring wheat in Northwest China , 2018 .

[11]  Laurent Tits,et al.  Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006 , 2017, Remote. Sens..

[12]  Geert Sterk,et al.  Spatial evaluation of soil erosion risk in the West Usambara Mountains, Tanzania , 2006 .

[13]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[14]  Heather McNairn,et al.  International Journal of Applied Earth Observation and Geoinformation , 2014 .

[15]  Anatoly A. Gitelson,et al.  An evaluation of MODIS 8- and 16-day composite products for monitoring maize green leaf area index , 2012 .

[16]  W. Bai,et al.  Relationships between drought disasters and crop production during ENSO episodes across the North China Plain , 2015, Regional Environmental Change.

[17]  Chandranath Chatterjee,et al.  Are recent frequent high floods in Mahanadi basin in eastern India due to increase in extreme rainfalls , 2014 .

[18]  Du Zheng,et al.  Spatial and temporal variability in the net primary production of alpine grassland on the Tibetan Plateau since 1982 , 2014, Journal of Geographical Sciences.

[19]  M. R. Rahman,et al.  Meteorological drought in Bangladesh: assessing, analysing and hazard mapping using SPI, GIS and monthly rainfall data , 2016, Environmental Earth Sciences.

[20]  Daniel Zízala,et al.  Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic , 2017, Remote. Sens..

[21]  Jialin Yu,et al.  Strawberry, black medic (Medicago lupulina), and Carolina geranium (Geranium carolinianum) growth under light-limiting conditions , 2019, Weed Technology.

[22]  Jiyuan Liu,et al.  Potential promoted productivity and spatial patterns of medium- and low-yield cropland land in China , 2016, Journal of Geographical Sciences.

[23]  Sylvain Delzon,et al.  Assessing the effects of climate change on the phenology of European temperate trees , 2011 .

[24]  Meng Zhang,et al.  Geochemical evaluation of land quality in China and its applications , 2014 .

[25]  Nan Wang,et al.  Allocate soil individuals to soil classes with topsoil spectral characteristics and decision trees , 2018, Geoderma.

[26]  J. Lipiec,et al.  Quantification of compaction effects on soil physical properties and crop growth , 2003 .

[27]  Andrew D. Richardson,et al.  Phenology of a northern hardwood forest canopy , 2006 .

[28]  E. Chung,et al.  Meteorological hazard assessment based on trends and abrupt changes in rainfall characteristics on the Korean peninsula , 2015, Theoretical and Applied Climatology.

[29]  Hongqi Zhang,et al.  Spatially-explicit sensitivity analysis for land suitability evaluation , 2013 .

[30]  Jingfeng Huang,et al.  Cold Damage Risk Assessment of Double Cropping Rice in Hunan, China , 2013 .

[31]  R. Leemans,et al.  Adaptation to climate change and climate variability in European agriculture: The importance of farm level responses , 2010 .