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

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.

Visual assessment of leaf area index in coffee (Coffea arabica L.) fields

“…The use of instruments that depend on light transmittance through the canopy to estimate the LAI (such as the AccuPAR LP-80 Ceptometer or the LAI 2200-C) may be unreliable, as the underlying models assume canopies with uniform foliar distribution; thus, they are not adequate for crops grown at wide spacing (such as hedgerows) or with shading (Bréda, 2003;Fang et al, 2019). Utilizing remote images (obtained from satellites or unmanned aerial vehicles) for LAI estimation in coffee production can also be a viable option, but at a potentially high cost and with complex implementation (Taugourdeau et al, 2014;Jaramillo-Giraldo et al, 2019;Dos Santos et al, 2020;Bento et al, 2022). Simple and inexpensive methods have also been proposed, such as smartphone apps that estimate LAI through image analysis; however, their precision is still lower than that of instrumentation (Hong et al, 2023).…”

Identificação aprimorada de seringais infestados por fitonematoides no município de Prata, MG, por sensoriamento remoto

“…Alguns trabalhos têm negligenciado a importância de se fazer uma análise criteriosa da série temporal das imagens pesquisadas. No caso do café, cultura em que se tem observado diversas publicações acerca do uso do sensoriamento remoto para o diagnóstico de nematoides (MARTINS et al, 2017;CORTEZ et al, 2020;ABREU JÚNIOR et al, 2020;LE, 2020;SILVA et al, 2021), mesmo sabendo que o índice de área foliar apresenta alta variabilidade espacial e temporal, influenciando na relação entre as variáveis biofísicas (JARAMILLO- GIRALDO et al, 2019), tais circunstâncias são desconsideradas.…”