Evaluation and cross-comparison of vegetation indices for crop monitoring from sentinel-2 and worldview-2 images

Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing applications are designed to provide farmers with timely crop monitoring and production information. Such information can be used to identify crop vigor problems. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation. However, due to the various sensor characteristics, there are differences among VIs derived from multiple sensors for the same target. Therefore, multi-sensor VI capability and effectiveness are critical but complicated issues in the application of multi-sensor vegetation observations. Various factors such as the atmospheric conditions during acquisition, sensor and geometric characteristics, such as viewing angle, field of view, and sun elevation influence direct comparability of vegetation indicators among different sensors. In the present study, two experimental areas were used which are located near the villages Nea Lefki and Melia of Larissa Prefecture in Thessaly Plain area, containing a wheat and a cotton crop, respectively. Two satellite systems with different spatial resolution, WorldView-2 (W2) and Sentinel-2 (S2) with 2 and 10 meters pixel size, were used. Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were calculated and a statistical comparison of the VIs was made to designate their correlation and dependency. Finally, several other innovative indices were calculated and compared to evaluate their effectiveness in the detection of problematic plant growth areas.

[1]  G. Daily,et al.  Food Production, Population Growth, and the Environment , 1998, Science.

[2]  D. Watson.  Comparative Physiological Studies on the Growth of Field Crops: I. Variation in Net Assimilation Rate and Leaf Area between Species and Varieties, and within and between Years , 1947 .

[3]  G. Guyot,et al.  Utilisation de la Haute Resolution Spectrale pour Suivre L'etat des Couverts Vegetaux , 1988 .

[4]  Vincent V. Salomonson,et al.  The Landsat Program: Its Origins, Evolution, and Impacts , 1997 .

[5]  C. Wiegand,et al.  Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield. I. Rationale. , 1990 .

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

[7]  A. J. Richardson,et al.  Vegetation indices in crop assessments , 1991 .

[8]  X. Mu,et al.  Indirect measurement of leaf area index on the basis of path length distribution , 2014 .

[9]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[10]  John L. Dwyer,et al.  Landsat: building a strong future , 2012 .

[11]  K. Weber,et al.  Multi-sensor Analyses of Vegetation Indices in a Semi-arid Environment , 2010 .

[12]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[13]  S. T. Gower,et al.  Leaf area index of boreal forests: theory, techniques, and measurements , 1997 .

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

[15]  Emmanouil Psomiadis,et al.  The role of spatial and spectral resolution on the effectiveness of satellite-based vegetation indices , 2016, Remote Sensing.

[16]  Ning Wang,et al.  Review: Wireless sensors in agriculture and food industry-Recent development and future perspective , 2006 .

[17]  Laure Roupioz,et al.  Improved Surface Reflectance from Remote Sensing Data with Sub-Pixel Topographic Information , 2014, Remote. Sens..

[18]  N. Dalezios,et al.  Agrometeorological Remote Sensing of High Resolution for Decision Support in Precision Agriculture , 2013 .

[19]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[20]  Kathryn Sheffield,et al.  Comparing inter-sensor NDVI for the analysis of horticulture crops in south-eastern Australia , 2014 .

[21]  Daniel P. Ames,et al.  Evaluation of a Method for Estimating Irrigated Crop-Evapotranspiration Coefficients from Remotely Sensed Data in Idaho , 2008 .

[22]  Maohua Wang,et al.  Wireless sensors in agriculture and food industry — Recent development and future perspective , 2005 .

[23]  Lammert Kooistra,et al.  Opportunities for Sentinel-2 in an integrated sensor approach to support decision making in precision agriculture , 2012 .

[24]  M. P. Tuohy,et al.  In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture , 2011, Precision Agriculture.

[25]  J. Kumhálová,et al.  Yield variability prediction by remote sensing sensors with different spatial resolution , 2017 .

[26]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[27]  Abderrazak Bannari,et al.  Transformed difference vegetation index (TDVI) for vegetation cover mapping , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[28]  A. Gitelson,et al.  Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .

[29]  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.

[30]  D. Greenland.  Use of satellite-based sensing in land surface climatology , 1994 .

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

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