Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

[1]  V. S. Manivasagam,et al.  Developing Transformation Functions for VENμS and Sentinel-2 Surface Reflectance over Israel , 2019, Remote. Sens..

[2]  A. C. Xavier,et al.  Monitoring leaf area index at watershed level through NDVI from Landsat-7/ETM+ data , 2004 .

[3]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[4]  Ran Pelta,et al.  Kc and LAI Estimations Using Optical and SAR Remote Sensing Imagery for Vineyards Plots , 2020, Remote. Sens..

[5]  J. Dash,et al.  Evaluation of the MERIS terrestrial chlorophyll index , 2004 .

[6]  Guang Zheng,et al.  Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors , 2009, Sensors.

[7]  A. Gitelson,et al.  Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm , 2014 .

[8]  Laurent Tits,et al.  Stem Water Potential Monitoring in Pear Orchards through WorldView-2 Multispectral Imagery , 2013, Remote. Sens..

[9]  Wenjiang Huang,et al.  Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Sukhwinder K. Bali,et al.  In‐season potato yield prediction with active optical sensors , 2020, Agrosystems, Geosciences & Environment.

[11]  V. S. Manivasagam,et al.  Fusion of Sentinel-2 and PlanetScope time-series data into daily 3 m surface reflectance and wheat LAI monitoring , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[12]  P. Dimitrov,et al.  Evaluation of Sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in Bulgaria , 2020 .

[13]  Jan G. P. W. Clevers,et al.  Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison , 2015 .

[14]  V. S. Manivasagam,et al.  Practices for upscaling crop simulation models from field scale to large regions , 2020, Comput. Electron. Agric..

[15]  J. Clevers.  Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture , 1989 .

[16]  Robert Rees,et al.  Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations , 2020, Remote. Sens..

[17]  O. Rozenstein,et al.  Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery , 2021, Remote. Sens..

[18]  Anita Simic Milas,et al.  The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images , 2018 .

[19]  Huazhong Ren,et al.  Decameter Cropland LAI/FPAR Estimation From Sentinel-2 Imagery Using Google Earth Engine , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Amit Kumar,et al.  Kharif crop characterization using combination of SAR and MSI Optical Sentinel Satellite datasets , 2019, Journal of Earth System Science.

[21]  John P. Kerekes,et al.  Potential of Red Edge Spectral Bands in Future Landsat Satellites on Agroecosystem Canopy Green Leaf Area Index Retrieval , 2018, Remote. Sens..

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

[23]  A. Viña,et al.  Comparison of different vegetation indices for the remote assessment of green leaf area index of crops , 2011 .

[24]  A. Skidmore,et al.  Narrow band vegetation indices overcome the saturation problem in biomass estimation , 2004 .

[25]  Robert M. Rees,et al.  The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development , 2019, Remote. Sens..

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

[27]  Gregoriy Kaplan,et al.  Estimating cotton water consumption using a time series of Sentinel-2 imagery , 2018 .

[28]  Luis Alonso,et al.  How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment , 2016, Remote. Sens..

[29]  R. C. Kaarsemaker,et al.  Effect of Leaf Area on Tomato Yield , 2005 .

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

[31]  I. Herrmann,et al.  LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands , 2011 .

[32]  José Moreno,et al.  Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) , 2019, Sensors.

[33]  G. Asrar,et al.  Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat1 , 1984 .

[34]  Giovanni Laneve,et al.  Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape , 2020, Remote Sensing Letters.

[35]  G. Waldhoff,et al.  Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA) , 2016, PloS one.

[36]  Yuanheng Sun,et al.  Red-Edge Band Vegetation Indices for Leaf Area Index Estimation From Sentinel-2/MSI Imagery , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Jan G. P. W. Clevers,et al.  Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop , 2017, Remote. Sens..

[38]  Marco Heurich,et al.  Comparison of Landsat-8 and Sentinel-2 Data for Estimation of Leaf Area Index in Temperate Forests , 2019, Remote. Sens..

[39]  Frank Ewert,et al.  Modelling plant responses to elevated CO2: how important is leaf area index? , 2004, Annals of botany.

[40]  Jan G. P. W. Clevers,et al.  Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[41]  L. Alonso,et al.  A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems , 2013 .

[42]  O. Rozenstein,et al.  Validation of the cotton crop coefficient estimation model based on Sentinel-2 imagery and eddy covariance measurements , 2019, Agricultural Water Management.

[43]  N. Broge,et al.  Comparison of selected vegetation indices as indicators of crop status. , 2003 .

[44]  Yingying Dong,et al.  Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[45]  J. Moreno,et al.  Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring. , 2020, Remote sensing of environment.

[46]  Gary R. Watmough,et al.  Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation , 2013 .

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