stimating salinity stress in sugarcane fields with spaceborne hyperspectral egetation indices

The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R2 = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model.

[1]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[2]  Armando Apan,et al.  Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery , 2004 .

[3]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[4]  J. Farifteh,et al.  Assessing salt-affected soils using remote sensing, solute modelling, and geophysics , 2006 .

[5]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[6]  Conghe Song,et al.  Hyperspectral remote sensing of salinity stress on red (Rhizophora mangle) and white (Laguncularia racemosa) mangroves on Galapagos Islands , 2011 .

[7]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

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

[9]  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 .

[10]  Abraham Blum,et al.  Plant Breeding For Stress Environments , 1988 .

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

[12]  W. T. McGeorge,et al.  Diagnosis and Improvement of Saline and Alkaline Soils , 1954 .

[13]  D. Wang,et al.  Interpretation of salinity and irrigation effects on soybean canopy reflectance in visible and near-infrared spectrum domain , 2002 .

[14]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[15]  M. Mulders,et al.  Remote sensing in soil science , 1987 .

[16]  D. M. Moss,et al.  Red edge spectral measurements from sugar maple leaves , 1993 .

[17]  Massimo Menenti,et al.  A field experiment on spectrometry of crop response to soil salinity , 2007 .

[18]  Susan L. Ustin,et al.  MONITORING PACIFIC COAST SALT MARSHES USING REMOTE SENSING , 1997 .

[19]  Zama Eric Mashimbye.  Remote sensing of salt-affected soils , 2013 .

[20]  Tim R. McVicar,et al.  Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes , 2003, IEEE Trans. Geosci. Remote. Sens..

[21]  R. Saxena,et al.  Remote sensing technique for mapping salt affected soils , 1994 .

[22]  Hao Chen,et al.  Processing Hyperion and ALI for forest classification , 2003, IEEE Trans. Geosci. Remote. Sens..

[23]  F. Meer,et al.  Spectral characteristics of salt-affected soils: A laboratory experiment , 2008 .

[24]  G. Taylor,et al.  Image-derived spectral endmembers as indicators of salinisation , 2003 .

[25]  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 .

[26]  Martha C. Anderson,et al.  Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans , 2004 .

[27]  Bin Zhao,et al.  Using hyperspectral vegetation indices as a proxy to monitor soil salinity , 2010 .

[28]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[29]  Kelin Wang,et al.  Detecting Suaeda salsa L. chlorophyll fluorescence response to salinity stress by using hyperspectral reflectance , 2011, Acta Physiologiae Plantarum.

[30]  Julie C. Naumann,et al.  Spatial variations in salinity stress across a coastal landscape using vegetation indices derived from hyperspectral imagery , 2009, Plant Ecology.

[31]  Tian Han,et al.  Detection and correction of abnormal pixels in Hyperion images , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[32]  Julie C. Naumann,et al.  Leaf chlorophyll fluorescence, reflectance, and physiological response to freshwater and saltwater flooding in the evergreen shrub, Myrica cerifera , 2008 .

[33]  A. K. Skidmore,et al.  Derivation of the red edge index using the MERIS standard band setting , 2002 .

[34]  M. F. Baumgardner,et al.  Salinity and spectral reflectance of soils , 1991 .

[35]  Andrew D. Richardson,et al.  Spectral reflectance of Thalassia testudinum (Hydrocharitaceae) seagrass: low salinity effects , 2006 .

[36]  Josep Peñuelas,et al.  Visible and Near‐Infrared Reflectance Assessment of Salinity Effects on Barley , 1997 .

[37]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

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

[39]  A. Gitelson,et al.  Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves¶ , 2001, Photochemistry and photobiology.

[40]  A. Gitelson,et al.  Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy¶ , 2002, Photochemistry and photobiology.

[41]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

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

[43]  Muneer Ahmed,et al.  Hyperspectral reflectance response of freshwater macrophytes to salinity in a brackish subtropical marsh. , 2007, Journal of environmental quality.

[44]  Parviz Rezvani Moghaddam,et al.  History of research on salt-affected lands of Iran: present status and future prospects : Halophytic Ecosystems : , 2000 .

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

[46]  G. Taylor,et al.  Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization , 2002 .

[47]  C. Grieve,et al.  Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared. , 2006, Journal of environmental quality.

[48]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[49]  Susan L. Ustin,et al.  Evaluation of the potential of Hyperion data to estimate wildfire hazard in the Santa Ynez front range, Santa Barbara, California , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[50]  J. Peñuelas,et al.  Relationship between photosynthetic radiation-use efficiency of barley canopies and the photochemical reflectance index (PRI) , 1996 .

[51]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .

[52]  Graciela Metternicht,et al.  Remote sensing of soil salinity: potentials and constraints , 2003 .