Empirical Modeling of Leaf Chlorophyll Content in Coffee (Coffea Arabica) Plantations With Sentinel-2 MSI Data: Effects of Spectral Settings, Spatial Resolution, and Crop Canopy Cover

Approaches to monitor coffee leaf Chl are necessary for field, landscape, regional, and national scale decision making to improve coffee stand health and productivity and to reduce yield losses. In this study, we investigated the influence of spectral settings, spatial resolution, and age-related crop canopy cover on leaf Chl modeling performance with Sentinel-2 MSI data. We ran the random forest algorithm to predict coffee leaf Chl using all nine Sentinel-2 MSI bands at 20 m, all nine bands at 10 m, five bands at 10 m, and four bands at 20 m spatial resolutions for all coffee stands (3–8 years, <inline-formula><tex-math notation="LaTeX">$N = 72$</tex-math></inline-formula>), and then for mature stands only (5–8 years, <inline-formula><tex-math notation="LaTeX">$N = 60$</tex-math> </inline-formula>). Results showed that the best modeling results <inline-formula><tex-math notation="LaTeX">$(R^{{2}}= \text{0.69},\text{RMSE}= \text{6.8})$</tex-math></inline-formula> were achieved when all the bands at 10 m spatial resolution were used in modeling coffee leaf Chl for all coffee stands. The prediction accuracy improved <inline-formula><tex-math notation="LaTeX">$(R^{{2}}= \text{0.77},\text{RMSE}= \text{5.9})$</tex-math></inline-formula> when only mature coffee stands were used. The 20-m bands (red-edge and shortwave infrared) performed similarly in coffee leaf Chl estimation as using all bands at 20 m and using only 10-m bands for all coffee stands and for mature coffee stands. Accuracy metrics were highest and error values lowest when coffee leaf Chl was estimated for mature stands only compared to when all coffee stands were modeled. We concluded that Sentinel-2 MSI is a valuable dataset for predicting coffee Chl; however, based on our findings, we suggest that finer spatial resolutions of 10 m applied on mature coffee stands should be adopted in modeling coffee Chl.

[1]  C. Borgemeister,et al.  Some Like It Hot: The Influence and Implications of Climate Change on Coffee Berry Borer (Hypothenemus hampei) and Coffee Production in East Africa , 2011, PloS one.

[2]  Holly Croft,et al.  Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground, CASI, Landsat TM 5 and MERIS reflectance data , 2013 .

[3]  Stefano Amaducci,et al.  Empirical Estimation of Leaf Chlorophyll Density in Winter Wheat Canopies Using Sentinel-2 Spectral Resolution , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[4]  S. Ollinger,et al.  Examining spectral reflectance features related to foliar nitrogen in forests: Implications for broad-scale nitrogen mapping , 2016 .

[5]  Marc Niezette,et al.  Sentinel-2 Level 2A Prototype Processor: Architecture, Algorithms And First Results , 2013 .

[6]  Z. Malenovský,et al.  Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. , 2009, Journal of experimental botany.

[7]  D. Lawlor.  Photosynthesis, productivity and environment , 1995 .

[8]  Daniella Malin,et al.  Climate change adaptation, mitigation and livelihood benefits in coffee production: where are the synergies? , 2014, Mitigation and Adaptation Strategies for Global Change.

[9]  K. P. P. Nair,et al.  The Agronomy and Economy of Important Tree Crops of the Developing World , 2010 .

[10]  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.

[11]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[12]  R. Moran.  Formulae for determination of chlorophyllous pigments extracted with n,n-dimethylformamide. , 1982, Plant physiology.

[13]  Margaret Kalacska,et al.  Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: Scaling from leaf to image , 2015 .

[14]  L. Zambolim,et al.  Characterization of differential coffee tree hosts for Hemileia vastatrix Berk. et Br. with RAPD markers , 2004 .

[15]  A. P. Vitória,et al.  Alterations in chlorophyll a fluorescence, pigment concentrations and lipid peroxidation to chilling temperature in coffee seedlings , 2009 .

[16]  Jan G. P. W. Clevers,et al.  Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  E. Malavolta,et al.  Photosynthesis, Chlorophylls, and SPAD Readings in Coffee Leaves in Relation to Nitrogen Supply , 2009 .

[18]  Subashisa Dutta,et al.  Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery , 2016 .

[19]  M. Vincini,et al.  Sensitivity of leaf chlorophyll empirical estimators obtained at Sentinel-2 spectral resolution for different canopy structures , 2016, Precision Agriculture.

[20]  Stéphanie Nicolin.  Implementing environmental water requirements in Buzi River basin, Mozambique An impact analysis based on the Water Resource Yield Model , 2011 .

[21]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[22]  F. Damatta,et al.  Photosynthesis and photoprotection in coffee leaves is affected by nitrogen and light availabilities in winter conditions. , 2010, Journal of plant physiology.

[23]  Matthew F. McCabe,et al.  Leaf chlorophyll constraint on model simulated gross primary productivity in agricultural systems , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[24]  F. Baret,et al.  Estimating Canopy Characteristics from Remote Sensing Observations: Review of Methods and Associated Problems , 2008 .

[25]  S. Ollinger.  Sources of variability in canopy reflectance and the convergent properties of plants. , 2011, The New phytologist.

[26]  Roshanak Darvishzadeh,et al.  Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  A. Chemura,et al.  Determination of the age of oil palm from crown projection area detected from WorldView-2 multispectral remote sensing data: The case of Ejisu-Juaben district, Ghana , 2015 .

[28]  Ricardo Bressan-Smith,et al.  Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves , 2005 .

[29]  D. Kutywayo,et al.  Bioclimatic modelling of current and projected climatic suitability of coffee (Coffea arabica) production in Zimbabwe , 2016, Regional Environmental Change.

[30]  O. Mutanga,et al.  Remote sensing leaf water stress in coffee (Coffea arabica) using secondary effects of water absorption and random forests , 2017 .

[31]  A. Viña,et al.  Remote estimation of canopy chlorophyll content in crops , 2005 .

[32]  Onisimo Mutanga,et al.  Developing detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor , 2017 .

[33]  Onisimo Mutanga,et al.  Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices , 2016 .