Plant ecophysiological processes in spectral profiles: perspective from a deciduous broadleaf forest

Abstract

The need for progress in satellite remote sensing of terrestrial ecosystems is intensifying under climate change. Further progress in Earth observations of photosynthetic activity and primary production from local to global scales is fundamental to the analysis of the current status and changes in the photosynthetic productivity of terrestrial ecosystems. In this paper, we review plant ecophysiological processes affecting optical properties of the forest canopy which can be measured with optical remote sensing by Earth-observation satellites. Spectral reflectance measured by optical remote sensing is utilized to estimate the temporal and spatial variations in the canopy structure and primary productivity. Optical information reflects the physical characteristics of the targeted vegetation; to use this information efficiently, mechanistic understanding of the basic consequences of plant ecophysiological and optical properties is essential over broad scales, from single leaf to canopy and landscape. In theory, canopy spectral reflectance is regulated by leaf optical properties (reflectance and transmittance spectra) and canopy structure (geometrical distributions of leaf area and angle). In a deciduous broadleaf forest, our measurements and modeling analysis of leaf-level characteristics showed that seasonal changes in chlorophyll content and mesophyll structure of deciduous tree species lead to a seasonal change in leaf optical properties. The canopy reflectance spectrum of the deciduous forest also changes with season. In particular, canopy reflectance in the green region showed a unique pattern in the early growing season: green reflectance increased rapidly after leaf emergence and decreased rapidly after canopy closure. Our model simulation showed that the seasonal change in the leaf optical properties and leaf area index caused this pattern. Based on this understanding we discuss how we can gain ecophysiological information from satellite images at the landscape level. Finally, we discuss the challenges and opportunities of ecophysiological remote sensing by satellites.

Review: Monitoring of land cover changes and plant phenology by remote‐sensing in East Asia

“…In addition, the instruments are affordable for individual sites, but prohibitively expensive for large‐scale use because of the large number of instruments that would be required. Although satellite data include uncertainties caused by the heterogeneity of the plant species and vegetation cover within the satellite footprint (Nagai, Nasahara, Akitsu, et al, 2020), and the dataset may be reduced by cloud contamination, we should develop methods for the evaluation of leaf traits using daily‐resolution satellite data such as that provided by 500‐m‐resolution MODIS/Terra and Aqua satellites, which would support analyses at a global scale (see also Noda et al, 2021).…”

AMMI Automatic Mangrove Map and Index: Novelty for Efficiently Monitoring Mangrove Changes with the Case Study in Musi Delta, South Sumatra, Indonesia

“…When these young mangroves are 7-10 years old, they have the highest NDVI values; however, as mangroves age, their NDVI values decrease, and the highest values shift to other younger mangroves, and so on. Noda et al [63] investigated the high NDVI in young vegetation, which has a bright green color, ρ Green increased after leaf emergence and decreased after canopy closure during early growth, while ρ Red continued to decline. According to these fndings, the highest NDVI is not always found in the most densely forested mangroves, nor is it always found in the oldest and healthiest mangroves.…”

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

“…While its pigment manages the spectral reflectance of the leaves and canopy, other parameters, e.g., canopy architecture, encompass both the spatial distributions of vegetation components, LAI, and background reflectance, which also contribute to it [14,16,17]. The LAI is an important index that provides crop growth condition by regulating water and predicting yield [18]. Therefore, accurate estimation of LAI can be used to precisely forecast croprelated factors, such as crop coefficient (kc) and crop yield factor (ky) [19].…”