Mapping spatial variability of crop growth conditions using RapidEye data in Northern Ontario, Canada

Abstract Information on spatial and temporal variability of crop growth status is important for understanding the interaction between plants and environmental conditions. In particular, information at the sub-field level is critical for detecting and combating within-field yield-limiting factors for implementation of precision agriculture. High-resolution optical satellite sensors are useful tools to acquire the necessary information in this regard. This study used multi-temporal RapidEye data to estimate the green effective plant area index (PAI) and leaf chlorophyll content of two field crops, spring wheat ( Triticum aestivum L.) and canola ( Brassica napus L.). The objective was to evaluate the capability of RapidEye imagery for detecting within-field variability of crop growth conditions. Field experiments were carried out in northern Ontario over two consecutive years, 2012 and 2013. Several vegetation indices were selected and derived from the RapidEye data to correlate with crop PAI measured using digital hemispherical photography and leaf chlorophyll content index (CCI) measured using a CCM-200 chlorophyll meter. Results showed that the effective PAI and CCI of the two crops were best correlated with different indices. For effective PAI, the modified triangular vegetation index 2 (MTVI2) was the best for spring wheat (R 2  = 0.74, RMSE = 0.72 and n = 200) whereas the red-edge normalized difference vegetation index (NDVI RE ) was the best for canola (R 2  = 0.83, RMSE = 0.49 and n = 147). For CCI, the chlorophyll vegetation index (CVI) was the best for spring wheat (R 2  = 0.78, RMSE = 4.1 and n = 128) whereas the red-edge chlorophyll index (CI RE ) was the best for canola (R 2  = 0.33, RMSE = 6.4 and n = 88). The study also confirmed that the combined transformed chlorophyll absorption reflectance index/optimized soil adjusted vegetation index (TCARI/OSAVI) was satisfactory for spring wheat leaf chlorophyll content estimation when the crop canopy is not sparse. Using selected vegetation indices, RapidEye data can be used to map the within-field variability of crop growth conditions, which is useful for applications in precision agriculture. The reflectance in the red-edge or the green bands is useful in mapping crop leaf chlorophyll variability.

[1]  A. Chehbouni,et al.  Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices , 2006 .

[2]  Pamela L. Nagler,et al.  High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors , 2014, Remote. Sens..

[3]  Onisimo Mutanga,et al.  Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers , 2014 .

[4]  A. Gitelson,et al.  Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC) , 2015 .

[5]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[6]  Andrew K. Skidmore,et al.  Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[7]  J. Chen,et al.  Defining leaf area index for non‐flat leaves , 1992 .

[8]  R. Fernandes,et al.  Landsat-5 TM and Landsat-7 ETM+ based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data , 2003 .

[9]  W. Diepenbrock.  Yield analysis of winter oilseed rape (Brassica napus L.): a review , 2000 .

[10]  Fei Liu,et al.  Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging , 2013 .

[11]  E. Pattey,et al.  Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons , 2012 .

[12]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[13]  Gérard Dedieu,et al.  Estimation of leaf area and clumping indexes of crops with hemispherical photographs , 2008 .

[14]  Gyanesh Chander,et al.  Radiometric and geometric assessment of data from the RapidEye constellation of satellites , 2013 .

[15]  J. Chen.  Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

[16]  Jiali Shang,et al.  Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images , 2014 .

[17]  A. Gitelson.  Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation. , 2004, Journal of plant physiology.

[18]  Gwendal Latouche,et al.  A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids , 2012, Physiologia plantarum.

[19]  A. Escudero,et al.  Estimation of chlorophyll in Quercus leaves using a portable chlorophyll meter: effects of species and leaf age , 2011, Annals of Forest Science.

[20]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[21]  M. Vincini,et al.  A broad-band leaf chlorophyll vegetation index at the canopy scale , 2008, Precision Agriculture.

[22]  A.R.G. Lang,et al.  Application of some of Cauchy's theorems to estimation of surface areas of leaves, needles and branches of plants, and light transmittance , 1991 .

[23]  T. A. Black,et al.  Characteristics of shortwave and longwave irradiances under a Douglas-fir forest stand , 1991 .

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

[25]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[26]  D. Mulla.  Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[27]  Bing Zhang,et al.  A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index , 2014 .

[28]  S. G. Nelson,et al.  Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape , 2008, Sensors.

[29]  Christopher Conrad,et al.  Derivation of leaf area index for grassland within alpine upland using multi-temporal RapidEye data , 2013 .

[30]  R. Houborg,et al.  Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data , 2008 .

[31]  U. Meier,et al.  Growth stages of mono- and dicotyledonous plants , 1997 .

[32]  Evapotranspiration estimation in the Brazil using NDVI data , 2005, SPIE Remote Sensing.

[33]  John R. Miller,et al.  Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived From Hyperspectral Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[35]  Chaoyang Wu,et al.  Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation , 2008 .

[36]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[37]  Christopher Conrad,et al.  Validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery , 2012 .

[38]  W. Bausch,et al.  QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize , 2010, Precision Agriculture.

[39]  Claire Marais-Sicre,et al.  Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data—From Temporal Signatures to Crop Parameters Estimation , 2013 .

[40]  W. Verhoef,et al.  Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral–directional radiometric data , 2015 .

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

[42]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[43]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[44]  Elizabeth Pattey,et al.  Assessment of in situ crop LAI measurement using unidirectional view digital photography , 2013 .

[45]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[46]  C. Atzberger,et al.  Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery , 2012 .

[47]  J. Kovacs,et al.  Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data , 2014 .

[48]  B. Kleinschmit,et al.  Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data , 2012 .