Assessing the Accuracy of Forest Phenological Extraction from Sentinel-1 C-Band Backscatter Measurements in Deciduous and Coniferous Forests

Abstract

Satellite remote sensing is an important method for forest phenological studies at continental or global scales. Sentinel-1 (S1), a polar orbit satellite with a spatial resolution of 10 m, provides an opportunity to observe high-resolution forest phenology. The sensitivities of S1 C-band backscatter measurements to vegetation phenology, such as crops, meadows, and mixed forests, have been discussed, whereas their performance for different forest types has not yet been quantitatively assessed. It is necessary to evaluate accuracy before adapting S1 datasets in forest phenological studies. This study discusses the seasonal variations in S1 backscatter measurements and assesses the accuracy of S1-based forest phenological metrics in two types of typical forests: deciduous and coniferous. S1 C-band SAR dual-polarization backscatter measurements for the period 2017–2019 were used to extract forest phenology metrics using the Fourier transform (FT) and double logistic (DL) functions. Phenological metrics from the ground-based PhenoCam dataset were used for evaluation. The S1 backscatter VV-VH signal peaks for deciduous and coniferous forests occur in the winter and summer, respectively. The S1 backscatter could reasonably characterize the start of season (SOS) of deciduous forests, with R² values up to 0.8, whereas the R² values for coniferous forest SOS were less than 0.30. Moreover, the retrieved end of season (EOS) was less accurate than the SOS. The differences in accuracy of S1 backscatter phenological metrics between deciduous and coniferous forests can be explained by the differences in seasonal changes in their corresponding canopy structures. To conclude, S1 C-band backscatter has a reasonable performance when monitoring the SOS of deciduous broadleaf forests (R² = 0.8) and relatively poor performance when extracting EOS of deciduous broadleaf forests (R² = 0.25) or phenology of evergreen needleleaf forests (R² = 0.2).

Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index

“…Our study revealed the seasonal change in CR time series. Previous studies, such as Ling, et al [32], extracted forests phenology based on seasonally changed CR time series. In this study, we related the variation of CR with the variation of structure and water content during the growth of deciduous forests (Fig.…”

“…The process of determining the threshold fully considered the asynchronism between the variation of the woody water content and the variation of leaf greenness of deciduous forests. Previous study on forests phenology used the same threshold to extract phenology from SAR and optical data [32], without considering the unique characteristics of CR and NDVI. Moreover, our study was carried out based on 41 PhenoCam deciduous forests sites in the eastern United States, rather than based on few sites [32].…”

“…Previous studies on phenology extraction using SAR data mostly focused on croplands [28][29][30][31]. The potential of SAR data for large-scale forest phenology extraction is yet to be investigated [32]. In addition, the physical linkage between the CR dynamics and deciduous forests phenology is unclear [18,32,33].…”

“…The potential of SAR data for large-scale forest phenology extraction is yet to be investigated [32]. In addition, the physical linkage between the CR dynamics and deciduous forests phenology is unclear [18,32,33]. Therefore, it is importance to disentangle how CR changes across different growth stages of forest phenology and the drivers of the changes.…”

Effects of aspect on phenology of Larix gmelinii forest in Northeast China

“…Plants as ecosystem producers are the most sensitive to climate change, of which phenology is one of the most important indicators, plant phenology is the phenomenon of periodic changes in plants under the influence of natural conditions such as climate, soil and topography. Plant phenology research mainly includes two approaches, one is through observing and recording the periodic growth and development rhythm of plants such as germination, leaf spreading, flowering, fruiting, leaf falling, withering and wilting at the species scale 6 , the second is remote sensing phenology (RSP), which use various remotely sensed vegetation indexes to determine the start of growing season (SOS), end of growing season (EOS) and length of growing season (LOS) et al at the regional scale 7 .…”