Analysis of Spatiotemporal Variation of Site-Specific Management Zones in a Topographic Relief Area over a Period of Six Years Using Image Segmentation and Satellite Data

Abstract Over fertilization has resulted in serious soil compaction and acidification, as well as nonpoint pollution. A strategy for variable fertilization according to Site-Specific Management Zones (SSMZ) is urgently needed. We developed an object-oriented method using image segmentation for SSMZ delineation for a farming cooperative founded in 2013 in northeastern China. The method is based on Normalized Difference Vegetation Index (NDVI) during the crop growth period from 2011 to 2016. We analyzed and validated SSMZ results with plant sampling data, NDVI, soil temperature and moisture. The results indicated that (i) SSMZs derived from NDVI and image segmentation enhanced the difference between SSMZs and the homogeneity within SSMZs; (ii) crop dry biomass was consistent with NDVI, and NDVI showed a clear change in the regular SSMZ pattern with terrain variation after 2013; and (iii) SSMZ patterns were mainly affected by different farming practices or by topographic factors before and after the cooperative was founded. This new approach for SSMZ delineation will accelerate the application of precision agriculture in relief areas. If the study results are applied to other areas, the main factors of SSMZ need to be determined because of climate differences as well as specific soil and terrain conditions.

[1]  Li Xiang,et al.  Delineation and Scale Effect of Precision Agriculture Management Zones Using Yield Monitor Data Over Four Years , 2007 .

[2]  Frank Y. Shih,et al.  Image Segmentation , 2007, Encyclopedia of Biometrics.

[3]  M. H. Prieto,et al.  Using NDVI and guided sampling to develop yield prediction maps of processing tomato crop , 2015 .

[4]  LiFeng,et al.  Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land , 2007 .

[5]  R. Godwin,et al.  A Review of the technologies for mapping within-field variability , 2003 .

[6]  Kenneth A. Sudduth,et al.  Soil Electrical Conductivity and Topography Related to Yield for Three Contrasting Soil – Crop Systems , 2003 .

[7]  Gary A. Peterson,et al.  Soil Attribute Prediction Using Terrain Analysis , 1993 .

[8]  Wu Gang,et al.  Evaluation of Optimal Segmentation Scale with Object-oriented Method in Remote Sensing , 2011 .

[9]  David W. Franzen,et al.  Evaluation of Soil Survey Scale for Zone Development of Site-Specific Nitrogen Management , 2002 .

[10]  L. Kumar,et al.  A review of data assimilation of remote sensing and crop models , 2018 .

[11]  K. Hofmockel,et al.  A modified incubation method reduces analytical variation of soil hydrolase assays , 2015 .

[12]  Bernhard Höfle,et al.  Comparative classification analysis of post-harvest growth detection from terrestrial LiDAR point clouds in precision agriculture , 2015 .

[13]  N. Zhang,et al.  Precision agriculture—a worldwide overview , 2002 .

[14]  Lalit Kumar,et al.  Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data , 2016, Remote. Sens..

[15]  Christopher B. Field,et al.  Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .

[16]  Soizik Laguette,et al.  Remote sensing applications for precision agriculture: A learning community approach , 2003 .

[17]  M. Bindi,et al.  A simple model of regional wheat yield based on NDVI data , 2007 .

[18]  Zhou Shi,et al.  Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land , 2007 .

[19]  D. Mulla,et al.  Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and thematic mapper images , 1991 .

[20]  Hiroshi Matsuyama,et al.  Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures , 2010 .

[21]  Rattan Lal,et al.  Slope Position and Erosional Effects on Soil Properties and Corn Production on a Miamian Soil in Central Ohio , 1997 .

[22]  Ahmad Al Bitar,et al.  Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data , 2016 .

[23]  James S. Schepers,et al.  Appropriateness of Management Zones for Characterizing Spatial Variability of Soil Properties and Irrigated Corn Yields across Years , 2004, Agronomy Journal.

[24]  A. Gitelson,et al.  Remote estimation of crop gross primary production with Landsat data , 2012 .

[25]  A. Y. Hanna,et al.  Soil Available Water as Influenced by Landscape Position and Aspect1 , 1982 .

[26]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[27]  Bahram Salehi,et al.  Temperature-Vegetation-soil Moisture Dryness Index (TVMDI) , 2017 .

[28]  Zhengwei Yang,et al.  Regression based corn yield assessment using MODIS based daily NDVI in Iowa state , 2016, 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics).

[29]  Kenneth A. Sudduth,et al.  Soil electrical conductivity and topography related to yield for three contrasting soil-crop systems , 2003 .

[30]  M. Jurado-Expósito,et al.  Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management , 2014 .

[31]  R. C. Frohn,et al.  Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery , 2011 .

[32]  Shusen Wang,et al.  Crop yield forecasting on the Canadian Prairies using MODIS NDVI data , 2011 .

[33]  Henry Lin,et al.  Functional soil mapping for site-specific soil moisture and crop yield management , 2013 .

[34]  J. Schepers,et al.  Site‐Specific Considerations for Managing Phosphorus , 2000 .