Tang, Jianwu, Christian Körner, Hiroyuki Muraoka, Shilong Piao, Miaogen Shen, Stephen Thackeray, and Xi Yang. 2016. “Emerging Opportunities and Challenges in Phenology: A Review”. Ecosphere 7 (8).
Publications
2016
Yang, Xi, Jianwu Tang, John F. Mustard, Jin Wu, Kaiguang Zhao, Shawn Serbin, and Jung Eun Lee. 2016. “Seasonal Variability of Multiple Leaf Traits Captured by Leaf Spectroscopy at Two Temperate Deciduous Forests”. Remote Sensing of Environment 179: 1–12. https://doi.org/10.1016/j.rse.2016.03.026.
Understanding the temporal patterns of leaf traits is critical in determining the seasonality and magnitude of terrestrial carbon, water, and energy fluxes. However, we lack robust and efficient ways to monitor the temporal dynamics of leaf traits. Here we assessed the potential of leaf spectroscopy to predict and monitor leaf traits across their entire life cycle at different forest sites and light environments (sunlit vs. shaded) using a weekly sampled dataset across the entire growing season at two temperate deciduous forests. The dataset includes field measured leaf-level directional-hemispherical reflectance/transmittance together with seven important leaf traits [total chlorophyll (chlorophyll a and b), carotenoids, mass-based nitrogen concentration (Nmass), mass-based carbon concentration (Cmass), and leaf mass per area (LMA)]. All leaf traits varied significantly throughout the growing season, and displayed trait-specific temporal patterns. We used a Partial Least Square Regression (PLSR) modeling approach to estimate leaf traits from spectra, and found that PLSR was able to capture the variability across time, sites, and light environments of all leaf traits investigated (R2= 0.6-0.8 for temporal variability; R2= 0.3-0.7 for cross-site variability; R2= 0.4-0.8 for variability from light environments). We also tested alternative field sampling designs and found that for most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate characterization of the seasonal patterns. Compared with the estimation of foliar pigments, the performance of Nmass, Cmassand LMA PLSR models improved more significantly with sampling frequency. Our results demonstrate that leaf spectra-trait relationships vary with time, and thus tracking the seasonality of leaf traits requires statistical models calibrated with data sampled throughout the growing season. Our results have broad implications for future research that use vegetation spectra to infer leaf traits at different growing stages.
2015
Liu, Zhunqiao, Haibo Hu, Hua Yu, Xi Yang, Hualei Yang, Cunxin Ruan, Yan Wang, and Jianwu Tang. 2015. “Relationship Between Leaf Physiologic Traits and Canopy Color Indices During the Leaf Expansion Period in an Oak Forest”. Ecosphere 6 (12): 1–9.
Yang, Xi, Jianwu Tang, John Mustard, Jung-Eun Lee, and Micol Rossini. 2015. “Solar-Induced Chlorophyll Fluorescence Correlates With Canopy Photosynthesis on Diurnal and Seasonal Scales in a Temperate Deciduous Forest”. Geophysical Research Letters, 2977–2987. https://doi.org/10.1002/2015GL063201.Received.
Lee, Jung-Eun, Joseph A. Berry, Christiaan Van Der Tol, Xi Yang, Luis Guanter, Alexander Damm, Ian Baker, and Christian Frankenberg. 2015. “Simulations of Chlorophyll Fluorescence Incorporated into the Community Land Model Version 4”. Global Change Biology 21 (9): 3469–3477. https://doi.org/10.1111/gcb.12948.
Several studies have shown that satellite retrievals of solar-induced chlorophyll fluorescence (SIF) provide useful information on terrestrial photosynthesis or gross primary production (GPP). Here, we have incorporated equations coupling SIF to photosynthesis in a land surface model, the National Center for Atmospheric Research Community Land Model version 4 (NCAR CLM4), and have demonstrated its use as a diagnostic tool for evaluating the calculation of photosynthesis, a key process in a land surface model that strongly influences the carbon, water, and energy cycles. By comparing forward simulations of SIF, essentially as a byproduct of photosynthesis, in CLM4 with observations of actual SIF, it is possible to check whether the model is accurately representing photosynthesis and the processes cou- pled to it. We provide some background on how SIF is coupled to photosynthesis, describe how SIF was incorporated into CLM4, and demonstrate that our simulated relationship between SIF and GPP values are reasonable when com- pared with satellite (Greenhouse gases Observing SATellite; GOSAT) and in situ flux-tower measurements. CLM4 overestimates SIF in tropical forests, and we show that this error can be corrected by adjusting the maximum carboxyl- ation rate (Vmax) specified for tropical forests in CLM4. Our study confirms that SIF has the potential to improve photo- synthesis simulation and thereby can play a critical role in improving land surface and carbon cycle models.
2014
Huang, Qingxu, Xi Yang, Bin Gao, Yang Yang, and Yuanyuan Zhao. 2014. “Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review”. Remote Sensing 6 (8): 6844–6866.
Xu, Hong, Tracy Twine, and Xi Yang. 2014. “Evaluating Remotely Sensed Phenological Metrics in a Dynamic Ecosystem Model”. Remote Sensing 6 (6): 4660–4686.
Shen, Miaogen, Yanhong Tang, Jin Chen, Xi Yang, Cong Wang, Xiaoyong Cui, Yongping Yang, et al. 2014. “Earlier-Season Vegetation Has Greater Temperature Sensitivity of Spring Phenology in Northern Hemisphere”. PloS One 9 (2): e88178.
Yang, Xi, Jianwu Tang, and John F. Mustard. 2014. “Beyond Leaf Color: Comparing Camera-Based Phenological Metrics With Leaf Biochemical, Biophysical, and Spectral Properties Throughout the Growing Season of a Temperate Deciduous Forest”. Journal of Geophysical Research: Biogeosciences 119 (3): 181-91. https://doi.org/10.1002/2013JG002460.
Abstract Plant phenology, a sensitive indicator of climate change, influences vegetation-atmosphere interactions by changing the carbon and water cycles from local to global scales. Camera-based phenological observations of the color changes of the vegetation canopy throughout the growing season have become popular in recent years. However, the linkages between camera phenological metrics and leaf biochemical, biophysical, and spectral properties are elusive. We measured key leaf properties including chlorophyll concentration and leaf reflectance on a weekly basis from June to November 2011 in a white oak forest on the island of Martha's Vineyard, Massachusetts, USA. Concurrently, we used a digital camera to automatically acquire daily pictures of the tree canopies. We found that there was a mismatch between the camera-based phenological metric for the canopy greenness (green chromatic coordinate, gcc) and the total chlorophyll and carotenoids concentration and leaf mass per area during late spring/early summer. The seasonal peak of gcc is approximately 20 days earlier than the peak of the total chlorophyll concentration. During the fall, both canopy and leaf redness were significantly correlated with the vegetation index for anthocyanin concentration, opening a new window to quantify vegetation senescence remotely. Satellite- and camera-based vegetation indices agreed well, suggesting that camera-based observations can be used as the ground validation for satellites. Using the high-temporal resolution dataset of leaf biochemical, biophysical, and spectral properties, our results show the strengths and potential uncertainties to use canopy color as the proxy of ecosystem functioning.
Huang, Qingxu, Xi Yang, Bin Gao, Yang Yang, and Yuanyuan Zhao. 2014. “Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review”. Remote Sensing 6 (8): 6844–6866. https://doi.org/10.3390/rs6086844.
Since the release of the digital archives of Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) nighttime light data in 1992, a variety of datasets based on this database have been produced and applied to monitor and analyze human activities and natural phenomena. However, differences among these datasets and how they have been applied may potentially confuse researchers working with these data. In this paper, we review the ways in which data from DMSP/OLS nighttime light images have been applied over the past two decades, focusing on differences in data processing, research trends, and the methods used among the different application areas. Five main datasets extracted from this database have led to many studies in various research areas over the last 20 years, and each dataset has its own strengths and limitations. The number of publications based on this database and the diversity of authors and institutions involved have shown promising growth. In addition, researchers have accumulated vast experience retrieving data on the spatial and temporal dynamics of settlement, demographics, and socioeconomic parameters, which are “hotspot” applications in this field. Researchers continue to develop novel ways to extract more information from the DMSP/OLS database and apply the data to interdisciplinary research topics. We believe that DMSP/OLS nighttime light data will play an important role in monitoring and analyzing human activities and natural phenomena from space in the future, particularly over the long term. A transparent platform that encourages data sharing, communication, and discussion of extraction methods and synthesis activities will benefit researchers as well as public and political stakeholders.