Detection of anomalous crop condition and soil variability mapping using a 26 year Landsat record and the Palmer crop moisture index

Cost-effective and reliable vegetation monitoring methods are needed for applications ranging from traditional agronomic mapping, to verifying the safety of geologic injection activities. A particular challenge is defining baseline crop conditions and subsequent anomalies from long term imagery records (Landsat) in the face of large spatiotemporal variability. We develop a new method for defining baseline crop response (near peak growth) using the normalized difference vegetation index (NDVI) from 26 years (1986–2011) of Landsat data for 400 km2 surrounding a planned geologic carbon sequestration site near Jacksonville, Illinois. The normal score transform (yNDVI) was applied on a field by field basis to accentuate spatial patterns and level differences due to planting times. We tested crop type and soil moisture (Palmer crop moisture index (CMI)) as predictors of expected crop condition. Spatial patterns in yNDVI were similar between corn and soybeans – the two major crops. Linear regressions between yNDVI and the cumulative CMI (CCMI) exposed complex interactions between crop condition, field location (topography and soils), and annual moisture. Wet toposequence positions (depressions) were negatively correlated to CCMI and dry positions (crests) positively correlated. However, only 21% of the landscape showed a statistically significant (p < 0.05) linear relationship. To map anomalous crop conditions, we defined a tolerance interval based on yNDVI statistics. Tested on an independent image (2013), 63 of 1483 possible fields showed unusual crop condition. While the method is not directly suitable for crop health assessment, the spatial patterns in correlation between yNDVI and CCMI have potential applications for pest damage detection and edaphological soil mapping, especially in the developing world.

[1]  J. S. Robins,et al.  Some Effects of Severe Soil Moisture Deficits at Specific Growth Stages in Corn1 , 1953 .

[2]  Jennifer L. Lewicki,et al.  Using hyperspectral plant signatures for CO2 leak detection during the 2008 ZERT CO2 sequestration field experiment in Bozeman, Montana , 2010 .

[3]  P. H. Zaidi,et al.  Response of maize (Zea mays L.) genotypes to excess soil moisture stress: morpho-physiological effects and basis of tolerance , 2003 .

[4]  Alex B. McBratney,et al.  An overview of pedometric techniques for use in soil survey , 2000 .

[5]  T. S. Colvin,et al.  Cluster Analysis of Spatiotemporal Corn Yield Patterns in an Iowa Field , 2003 .

[6]  M. Kaddah,et al.  Salinity Effects on the Growth of Corn at Different Stages of Development1 , 1964 .

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

[8]  W. Alley.  The Palmer Drought Severity Index: Limitations and Assumptions , 1984 .

[9]  Kazuhito Ichii,et al.  Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation , 2001 .

[10]  T. S. Colvin,et al.  Relationship of Corn and Soybean Yield to Soil and Terrain Properties , 2004 .

[11]  J. Ritchie,et al.  INFLUENCE OF MANAGEMENT HISTORY AND LANDSCAPE VARIABLES ON SOIL ORGANIC CARBON AND SOIL REDISTRIBUTION , 2004 .

[12]  R. H. Shaw,et al.  The Effects of Soil Moisture Stress at Different Stages of Growth on the Development and Yield of Corn1 , 1960 .

[13]  R. Mueller,et al.  The 2009 Cropland Data Layer. , 2010 .

[14]  J. Ritchie,et al.  Soil and soil organic carbon redistribution on the landscape , 2007 .

[15]  Lin Yan,et al.  Automated crop field extraction from multi-temporal Web Enabled Landsat Data , 2014 .

[16]  Wayne C. Palmer,et al.  Keeping Track of Crop Moisture Conditions, Nationwide: The New Crop Moisture Index , 1968 .

[17]  L. West,et al.  Field-Scale Mapping of Surface Soil Organic Carbon Using Remotely Sensed Imagery , 2000 .

[18]  Clayton V. Deutsch,et al.  GSLIB: Geostatistical Software Library and User's Guide , 1993 .

[19]  Brian K. Slater,et al.  A Comparison Between Contour Elevation Data Sources for DEM Creation and Soil Carbon Prediction, Coshocton, Ohio , 2005, Trans. GIS.

[20]  F. Huff,et al.  Mesoscale Spatial Variability in Midwestern Precipitation , 1968 .

[21]  W. G. Howe.  Two-Sided Tolerance Limits for Normal Populations—Some Improvements , 1969 .

[22]  A. Viña,et al.  Drought Monitoring with NDVI-Based Standardized Vegetation Index , 2002 .

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

[24]  N. McKenzie,et al.  Spatial prediction of soil properties using environmental correlation , 1999 .

[25]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[26]  Laura C. Bowling,et al.  Detecting subsurface drainage systems and estimating drain spacing in intensively managed agricultural landscapes. , 2009 .

[27]  J. Bouma,et al.  Future Directions of Precision Agriculture , 2005, Precision Agriculture.

[28]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[29]  K. Pregitzer,et al.  Soil temperature, matric potential, and the kinetics of microbial respiration and nitrogen mineralization , 1999 .

[30]  R. H. Shaw,et al.  Availability of Soil Water to Plants as Affected by Soil Moisture Content and Meteorological Conditions1 , 1962 .

[31]  A. K. Skidmore,et al.  The effects of high soil CO2 concentrations on leaf reflectance of maize plants , 2009 .

[32]  M. Steven,et al.  Effects of CO2 gas as leaks from geological storage sites on agro-ecosystems , 2010 .

[33]  P. H. Zaidi,et al.  Tolerance to excess moisture in maize (Zea mays L.): susceptible crop stages and identification of tolerant genotypes , 2004 .

[34]  P. Chavez.  An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data , 1988 .

[35]  Welch Bl.  THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .

[36]  J. Ritchie,et al.  Identifying spatial patterns of erosion for use in precision conservation , 2005 .