Relationship Between Spatio-Temporal Leaf Area Index and Crop Coefficient When Monitoring Coffee Plots in Brazil Using the Sentine-2

Carolina Jaramillo-Giraldo,Raphael Bragança Alves Fernandes,Marcelo De Freitas Ribeiro,Humberto Paiva Fonseca,Laís Maria Rodrigues Silva,Williams Pinto Marques Ferreira

doi:10.5539/jas.v11n15p187

Abstract

Robust monitoring techniques for perennial crops have become increasingly possible due to technological advances in the area of Remote Sensing (RS), and the products are available through the European Space Agency (ESA) initiative. RS data provides valuable opportunities for detailed assessments of crop conditions at plot level using high spatial, spectral, and temporal resolution. This study addresses the monitoring of coffee at the plot level using RS, analyzing the relationship between the spatio-temporal variability of the Leaf Area Index (LAI) and the crop coefficient (Kc); the Kc being a biophysical variable that integrates the potential hydrological characteristics of an agroecosystem compared to the reference crop. Daily and one-year Kc were estimated using the relation of crop evapotranspiration and reference. ESA Sentinel-2 images were pre-analyzed and atmospherically corrected, and Top-of-the-Atmosphere (TOA) reflections converted to Top-of-the-Canopy (TOC) reflectance. The TOCs resampled at the 10m resolution, and with the angles corresponding to the directional information at the time of the acquisition, the LAI was estimated using the trained neural network available in the Sentinel Application Platform (SNAP). During 75% of the monitored days, Kc ranged between 1.2 and 1.3 and, the LAI analyzed showed high spatial and temporal variability at the plot level. Based on the relationship between the biophysical variables, the LAI variable can substitute the Kc and be used to monitor the water conditions at the production area as well as analyze spatial variability inside that area. Sentinel-2 products could be more useful in monitoring coffee in the farm production area.

Highlights

Millions of people consume 2.25 billion cups of coffee per day and represents the value in the global market of more than $19 billion USD
Given the presence of clouds determined the discard of other available images, it was not possible to process the series in uniform time intervals in the Leaf Area Index (LAI) monitoring
In the descriptive spatial analysis of LAI at the plot level (Figure 5), the total pixel population (83) per monitored date were divided into two groups: those with the lowest and highest LAI, according to the spatial variability found for each monitored period

Summary

Introduction

Millions of people consume 2.25 billion cups of coffee per day and represents the value in the global market of more than $19 billion USD. Chemura et al (2017a) argued that the development of cost-effective, reliable and easy to implement crop condition monitoring methods are urgently required for perennial shrub crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience weak crop growth, pest infestation and, disease outbreaks, in order to monitor responses to management interventions. The aim of the present study at the plot level was to reveal the monitoring of coffee shrubs with the use of information from the Sentinel-2, through the spatial-temporal analysis of the Leaf Area Index (LAI) and the Crop Coefficient (Kc)

Characterization Site-Monitored

Results

Discussion

Conclusions