@Article{PetriGaArSiAlOlFe:2022:SoIlEf,
author = "Petri, Caio Arlanche and Galv{\~a}o, L{\^e}nio Soares and
Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de and Silva, Ricardo
Dalagnol da and Almeida, Catherine Torres de and Oliveira, Afonso
Henrique Moraes and Feliz, Iara Musse",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Solar illumination effects on the dry-season variability of
spectral and spatial attributes calculated from PlanetScope data
over tropical forests of the Amazon",
journal = "International Journal of Remote Sensing",
year = "2022",
volume = "43",
number = "11",
pages = "4087--4116",
month = "June",
keywords = "Solar illumination, satellite constellation, green-up, tropical
forests, dry season, vegetation indices, Amazon.",
abstract = "The spectral variability of tropical forests during the Amazonian
dry season is not entirely understood because of the divergent
responses in Moderate Resolution Imaging Spectroradiometer (MODIS)
vegetation indices (VIs) measured under-increased water deficit
and high insolation. Here, we used a dataset composed of 493
cloud-free PlanetScope (PS) images to investigate possible effects
of solar illumination on the dry-season variability of spectral
and spatial attributes. The attributes were calculated from June
to September over dense tropical forests of the Amazon. The
dry-season images were obtained at nadir viewing between 2017 and
2019 over 12 selected sites representing different climatic and
environmental conditions. To detect dry-season patterns of
vegetation brightness with changes in the geometry of image
acquisition, we applied principal component analysis (PCA) over
the PS surface reflectance. We plotted the average surface
reflectance (2017-2019) for each of the four PS bands and
inspected the variability of two VIs with distinct levels of
anisotropy to bidirectional effects: the Enhanced Vegetation Index
(EVI) and the Normalized Difference Vegetation Index (NDVI). We
also investigated the signal of textural metrics from Grey Level
Co-occurrence Matrix (GLCM) obtained from the near-infrared (NIR)
band of PS. Finally, we generated shade fractions from Spectral
Mixture Analysis (SMA), correlated the spectral and spatial
attributes of vegetation with solar angles, and observed the
dry-season variability in reflectance and VIs over
pseudo-invariant soil surfaces. The results showed the existence
of solar illumination effects on PS image acquisition during the
dry season of the Amazon, which affected differently the NDVI and
EVI. From the beginning (June) to the end (September) of the dry
season, the solar zenith angle (SZA) decreased and the solar
azimuth angle (SAA) increased during the period of acquisition of
the PS images. The amplitude of SZA between June and September
increased towards south of the Amazon, while the amplitude of SAA
increased towards north of this region. Changes in vegetation
brightness from June to September were captured by PCA over some
sites. Because of the overall increase in both red and NIR band
reflectance, solar illumination effects were compensated during
the NDVI calculation. In contrast, because the EVI is largely
driven by changes in NIR reflectance, these effects contributed to
increase the EVI signal at the end of the dry season. For most
sites, GLCM texture mean increased towards the end of the dry
season, while texture variance decreased in the opposite
direction. Shade fractions decreased towards September when
reduced amounts of canopy shadows were sensed by PS. EVI was more
anisotropic than NDVI and presented higher negative correlations
with SZA and shade fractions and higher positive correlations with
SAA and texture mean. The dry-season increase in EVI with solar
illumination effects was also observed over pseudo-invariant soil
surfaces. From this unprecedent scale of observations at high
spatial and temporal resolutions, we recommend caution when using
anisotropic VIs for large-scale phenological studies over the
Amazon because biophysical and non-biophysical signals may be
coupled together.",
doi = "10.1080/01431161.2022.2106801",
url = "http://dx.doi.org/10.1080/01431161.2022.2106801",
issn = "0143-1161",
language = "en",
urlaccessdate = "06 jun. 2024"
}