@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 = "01 maio 2024"
}