Multi-year monitoring land surface phenology in relation to climatic variables using MODIS-NDVI time-series in Mediterranean forest, Northeast Tunisia

2022 ◽  
Vol 114 ◽  
pp. 103804
Author(s):  
Issam Touhami ◽  
Hassane Moutahir ◽  
Dorsaf Assoul ◽  
Kaouther Bergaoui ◽  
Hamdi Aouinti ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Climatic Variables ◽  
Mediterranean Forest ◽  
Land Surface Phenology ◽  
Modis Ndvi ◽  
Ndvi Time Series

scholarly journals Influence of Varying Solar Zenith Angles on Land Surface Phenology Derived from Vegetation Indices: A Case Study in the Harvard Forest

2021 ◽  
Vol 13 (20) ◽  
pp. 4126
Author(s):  
Yang Li ◽  
Ziti Jiao ◽  
Kaiguang Zhao ◽  
Yadong Dong ◽  
Yuyu Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Mean Difference ◽  
Land Surface Phenology ◽  
Harvard Forest ◽  
Ndvi Time Series ◽  
Bidirectional Reflectance Distribution

Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0°, 15°, 30°, 45°, and 60°) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI/EVI time series.

scholarly journals Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

2021 ◽  
Vol 13 (21) ◽  
pp. 4465
Author(s):  
Yu Shen ◽  
Xiaoyang Zhang ◽  
Weile Wang ◽  
Ramakrishna Nemani ◽  
Yongchang Ye ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Cloud Cover ◽  
Landsat 8 ◽  
Field Scale ◽  
Spatiotemporal Resolution ◽  
Essential Information ◽  
Land Surface Phenology ◽  
The Earth ◽  
Sentinel 2

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

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