Restricted accessResearch articleFirst published online 2022-06
Evaluating the Impact of Summer Drought on Vegetation Growth Using Space-Based Solar-Induced Chlorophyll Fluorescence Across Extensive Spatial Measures
Drought is the primary and dominant natural cause of stress on vegetation, and thus, it needs our full attention. Current understanding of drought across extensive spatial measures, around the world, is considerably limited. As case studies to evaluate the feasibility of utilizing space-based solar-induced chlorophyll fluorescence (SIF) across extensive spatial measures, here, we have used data from 2007 to 2017 in Heilongjiang and Jiangsu provinces of China. The onset of the 2015 drought was accompanied by a substantial response of SIF from vegetation in both the provinces; these data were associated with changes in soil moisture, standardized precipitation evapotranspiration index, and emissivity. Our findings suggest that SIF can effectively provide the spatial and temporal progress of drought, as inferred through substantial associations with SIF normalized by absorbed photosynthetically active radiation (related to ΦF) and by photosynthetically active radiation (SIFpar). For the depiction of onset to drought, SIF, ΦF, and SIFpar provide a significant association and a quicker response than the leaf area index and the normalized difference vegetation index. Furthermore, we found that the correlation between gross primary productivity and SIF is highly substantial in both Heilongjiang (R2 = 0.85, p < 0.001) and Jiangsu (R2 = 0.75, p < 0.001) during the drought period. Our results indicate that continuing evaluation from space-based SIF can indeed provide an understanding of the seasonal differences in vegetation for evaluating the impact of drought across extensive spatial measures.
Get full access to this article
View all access options for this article.
References
1.
DiazV, PerezGAC, Van LanenHAJ, et al.An approach to characterise spatio-temporal drought dynamics. Adv Water Resour. 2020; 137:103512.
2.
Vicente-SerranoSM, QuiringSM, Peña-GallardoM, et al.A review of environmental droughts: Increased risk under global warming?. Earth-Sci Rev. 2020; 201:102953.
3.
PareekA, SoporySK, BohnertHJ, et al. Abiotic stress adaptation in plants: Physiological, molecular and genomic foundation. Dordrecht: Springer. 2020. p. 526. ISBN 978-90-481-3111-2; Library of Congress Control Number: 2009941298.
4.
LiuL, YangX, ZhouH, et al.Evaluating the utility of solar-induced chlorophyll fluorescence for drought monitoring by comparison with NDVI derived from wheat canopy. Sci Total Environ. 2018; 625:1208–1217.
5.
BaiX, ShenW, WuX, et al.Applicability of long-term satellite-based precipitation products for drought indices considering global warming. J Environ Manage. 2020; 255:109846.
6.
NguvavaM, AbiodunBJ, OtienoF. Projecting drought characteristics over East African basins at specific global warming levels. Atmos Res. 2019; 228:41–54.
7.
WangP, ZhangLM, XieYZY, et al.Heat waves in China: Definitions, leading patterns, and connections to large-scale atmospheric circulation and SSTs. Adv Sci. 2017; 4:10.
8.
StewartIT, RogersJ, GrahamA. Water security under severe drought and climate change: Disparate impacts of the recent severe drought on environmental flows and water supplies in Central California. J Hydrol X. 2020; 7:100054.
9.
WangF, WangZ, YangH, et al.Utilizing GRACE-based groundwater drought index for drought characterization and teleconnection factors analysis in the North China Plain. J Hydrol. 2020; 585:124849.
10.
SchuldtB, BurasA, ArendM, et al.A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl Ecol. 2020; 45:86.
11.
Steele-DunneSC, HahnS, WagnerW, et al.Investigating vegetation water dynamics and drought using Metop ASCAT over the North American Grasslands. Remote Sens Environ. 2019; 224:219–235.
12.
QianX, QiuB, ZhangY. Widespread decline in vegetation photosynthesis in Southeast Asia due to the prolonged drought during the 2015/2016 El Niño. Remote Sens. 2019; 11:910.
13.
ZhangY, JoinerJ, GentineP, et al.Reduced solar-induced chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset artifacts. Glob Chang Biol. 2018; 24):2229–2230.
14.
YuanW, CaiW, ChenY, et al.Severe summer heatwave and drought strongly reduced carbon uptake in Southern China. Sci Rep. 2016; 6:18813.
15.
YuX, WangY, YuS, et al.Synchronous droughts and floods in the Southern Chinese Loess Plateau since 1646 CE in phase with decadal solar activities. Glob Planet Change. 2019; 183:103033.
16.
ZhangQ, YuH, SunP, et al.Multisource data based agricultural drought monitoring and agricultural loss in China. Glob Planet Change. 2019; 172:298–306.
17.
ChiognaG, SkrobanekP, NaranyTS, et al.Effects of the 2017 drought on isotopic and geochemical gradients in the Adige catchment, Italy. Sci Total Environ. 2018; 645:924–936.
18.
YangW, TanB, HuangD, et al.MODIS leaf area index products: From validation to algorithm improvement. IEEE Trans Geosci Remote Sens. 2006; 44:1885–1898.
19.
NanzadL, ZhangJ, TuvdendorjB, et al.NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016. J Arid Environ. 2019; 164:69–77.
20.
SweetSK, WolfeDW, DeGaetanoA, et al.Anatomy of the 2016 drought in the Northeastern United States: Implications for agriculture and water resources in humid climates. Agric For Meteorol. 2017; 247:571–581.
21.
AnwaarHA, PerveenR, ManshaMZ, et al.Assessment of grain yield indices in response to drought stress in wheat (Triticum aestivum L.).Saudi J Biol Sci.2020; 27:1818–1823.
22.
StephensonNL, DasAJ, AmperseeNJ, et al.Patterns and correlates of giant sequoia foliage dieback during California's 2012–2016 hotter drought. For Ecol Manage. 2018; 419–420:268–278.
23.
ParkS, KangD, YooC, et al.Recent ENSO influence on East African drought during rainy seasons through the synergistic use of satellite and reanalysis data. ISPRS J Photogramm Remote Sens. 2020; 162:17–26.
24.
YoshidaY, JoinerJ, TuckerC, et al.The 2010 Russian drought impact on satellite measurements of solar-induced chlorophyll fluorescence: Insights from modeling and comparisons with parameters derived from satellite reflectances. Remote Sens Environ. 2015; 166:163–177.
25.
GonçalvesNB, LopesAP, DalagnolR, et al.Both near-surface and satellite remote sensing confirm drought legacy effect on tropical forest leaf phenology after 2015/2016 ENSO drought. Remote Sens Environ. 2020; 237:111489.
26.
WestH, QuinnN, HorswellM. Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities. Remote Sens Environ. 2019; 232:111291.
27.
MohammedGH, ColomboR, MiddletonEM, et al.Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. Remote Sens Environ. 2019; 231:111177.
28.
LiZ, ZhangQ, LiJ, et al.Solar-induced chlorophyll fluorescence and its link to canopy photosynthesis in maize from continuous ground measurements. Remote Sens Environ. 2020; 236:111420.
29.
LiuW, AthertonJ, MõttusM, et al.Simulating solar-induced chlorophyll fluorescence in a boreal forest stand reconstructed from terrestrial laser scanning measurements. Remote Sens Environ. 2019; 232:111274.
30.
BiriukovaK, CelestiM, EvdokimovA, et al.Effects of varying solar-view geometry and canopy structure on solar-induced chlorophyll fluorescence and PRI. Int J Appl Earth Obs Geoinf. 2020; 89:102069.
31.
PengB, GuanK, ZhouW, et al.Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction. Int J Appl Earth Obs Geoinf. 2020; 90:102126.
32.
FuP, Meacham-HensoldK, SiebersMH, et al.The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: Benefits for field phenotyping. J Exp Bot. 2021; 72:1295–1306.
33.
JoinerJ, GuanterL, LindstrotR, et al.Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: Methodology, simulations, and application to GOME-2. Atmos Meas Tech. 2013; 6:2803–2823.
34.
SomkutiP, BöschH, FengL, et al.A new space-borne perspective of crop productivity variations over the US Corn Belt. Agric For Meteorol. 2020; 281:107826.
35.
FrankenbergC, FisherJB, WordenJ, et al.New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity. Geophys Res Lett. 2011; 38: L17706.
36.
SunY, FrankenbergC, JungM, et al.Overview of solar-induced chlorophyll fluorescence (SIF) from the orbiting carbon observatory-2: Retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sens Environ. 2018; 209:808–823.
37.
MeroniM, AtzbergerC, VancutsemC, et al.Evaluation of agreement between space remote sensing SPOT-VEGETATION fAPAR time series. IEEE Trans Geosci Remote Sens. 2013; 51:1951–1962.
38.
Amoros-LopezJ, Gomez-ChovaL, Vila-FrancesJ, et al.Evaluation of remote sensing of vegetation fluorescence by the analysis of diurnal cycles. Int J Remote Sens. 2008; 29:5423–5436.
39.
PapageorgiouGC, GovindjeeG. Chlorophyll a fluorescence: A signature of photosynthesis. Dordrecht: Kluwer (now Springer), 2004.
40.
GovindjeeG, AmeszJ, ForkDC. Light emission by plants and bacteria. Orlando: Academic Press, 1986.
41.
WangS, ZhangY, JuW, et al.Warmer spring alleviated the impacts of 2018 European summer heatwave and drought on vegetation photosynthesis. Agric For Meteorol. 2020; 295:108195.
42.
ChenS, HuangY, WangG. Detecting drought-induced GPP spatiotemporal variabilities with sun-induced chlorophyll fluorescence during the 2009/2010 droughts in China. Ecol Indic. 2021; 121:107092.
43.
WenJ, KöhlerP, DuveillerG, et al.A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF). Remote Sens Environ. 2020; 239:111644.
44.
WangN, SuomalainenJ, BartholomeusH, et al.Diurnal variation of sun-induced chlorophyll fluorescence of agricultural crops observed from a point-based spectrometer on a UAV. Int J Appl Earth Obs Geoinf. 2021; 96:102276.
45.
WangJ, WuC, ZhangC, et al.Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model. Ecol Indic. 2018; 88:332–340.
46.
FuX, TangC, ZhangX, et al.An improved indicator of simulated grassland production based on MODIS NDVI and GPP data: A case study in the Sichuan province, China. Ecol Indic. 2014; 40:102–108.
47.
GonsamoA, ChenJM, PriceDT, et al.Land surface phenology from optical satellite measurement and CO2 eddy covariance technique. J Geophys Res Biogeosciences. 2012; 117, DOI: 10.1029/2012JG002070.
48.
JeongS-J, SchimelD, FrankenbergC, et al.Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests. Remote Sens Environ. 2017; 190:178–187.
49.
CescattiA, MarcollaB, VannanSKS, et al.Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network. Remote Sens Environ. 2012; 121:323–334.
50.
DammA, GuanterL, VerhoefW, et al.Impact of varying irradiance on vegetation indices and chlorophyll fluorescence derived from spectroscopy data. Remote Sens Environ. 2015; 156:202–215.
51.
GuanterL, AbenI, TolP, et al.Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence. Atmos Meas Tech. 2015; 8:1337–1352.
52.
WagleP, XiaoX, SuykerAE. Estimation and analysis of gross primary production of soybean under various management practices and drought conditions. ISPRS J Photogramm Remote Sens. 2015; 99:70–83.
53.
PeiW, FuQ, LiuD, et al.Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province, China. Theor Appl Climatol. 2018; 133:151–164.
54.
WangJ, HuangX, ZhongT, et al.Climate change impacts and adaptation for saline agriculture in north Jiangsu Province, China. Environ Sci Policy. 2013; 25:83–93.
55.
LuE, LiuS, LuoY, et al.The atmospheric anomalies associated with the drought over the Yangtze River basin during spring 2011. J Geophys Res Atmos. 2014; 119:5881–5894.
56.
Vicente-SerranoSM, BegueríaS, López-MorenoJI. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J Clim. 2010; 23:1696–1718.
57.
JoinerJ, YoshidaY, GuanterL, et al.New methods for the retrieval of chlorophyll red fluorescence from hyperspectral satellite instruments: Simulations and application to GOME-2 and SCIAMACHY. Atmos Meas Tech. 2016; 9:3939–3967.
58.
FrankenbergC, BerryJ. Solar induced chlorophyll fluorescence: Origins, relation to photosynthesis and retrieval. Compr Remote Sens. 2018; 3:143–162.
59.
KöhlerP, GuanterL, JoinerJ. A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data. Atmos Meas Tech. 2015; 8:2589–2608.
60.
GuanterL, ZhangY, JungM, et al.Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc Natl Acad Sci. 2014; 111:e1327—E1333.
61.
LiX, XiaoJ, HeB, et al.Solar-induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO-2 and flux tower observations. Glob Chang Biol. 2018; 24:3990–4008.
62.
ZhangY, XiaoX, JinC, et al.Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America. Remote Sens Environ. 2016; 183:154–169.
