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Because of the limited spatiotemporal resolutions in vegetation index (VI) products, land surface phenology (LSP) results may not well capture ground-based phenological changes. This is likely the result of the mixed pixel effect: (1) a pixel in VI products may contain an unknown composition of vegetation species or land cover types; and (2) these species differ in their sensitivity to climatic variations. The mixed pixel effect has induced inconsistent findings in LSP with in situ observations of spring phenology. To this end, this study has designed a series of simulation experiments to initiate the methodological exploration of how the green-up date (GUD) of a mixed pixel could be altered by the endmember GUDs and different non-GUD variables, including the endmember composition, minimum and maximum normalized difference vegetation index (NDVI), and the length of the growth period. The study has also compared the sensitivity of two generally adopted GUD identification methods, the relative threshold method and the curvature method (also known as the inflection-point method). The simulations with two endmembers show that even if there is no change in the endmember GUDs, the GUD of the mixed pixel could be substantially altered by the changes in non-GUD variables. In addition, the study has also developed a simulation toolkit for the GUD identification with cases of three or more endmembers. The results of the study provide insights into effective strategies for analyzing spring phenology using VI products: the mixed pixel effect can be alleviated by selecting pixels that are relatively stable in the land cover or species composition. This simulation study calls for in situ phenological observations to validate the LSP, such as conducting climate-controlled experiments on few mixed species at a small spatial scale. The paper also argues for the necessity of isolating GUD trends caused by non-phenological changes in the study of spring phenology.
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7State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
8Department of Emergency Management, Arkansas Tech University, Russellville, AR 72801, USA
9Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China
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1. Introduction
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UNCORRECTED PROOF
X. Chen et al. Remote Sensing of Environment xxx (2018) xxx-xxx
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2. Method
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UNCORRECTED PROOF
X. Chen et al. Remote Sensing of Environment xxx (2018) xxx-xxx
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2.3. Simulation experiments
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3. Results
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Table 1
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UNCORRECTED PROOF
X. Chen et al. Remote Sensing of Environment xxx (2018) xxx-xxx
Fig. 2. #BBKIJH7J?ED E< J>; I?CKB7J?ED I9;D7H?EI 7 9>7D=?D= fX7 <HEC  JE  7D: J>; (0#I E< 8EJ> ;D:C;C8;HI H;C7?D KD9>7D=;: 8 :;9H;7I?D= J>; (0#XC7N E< ;D:C;C8;H  <HEC
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J>; C7JKH?JO :7J; > 7:L7D9?D= J>; !/ E< ;D:C;C8;H  8O :7OI 7D: ? :;B7O?D= J>; !/ E< ;D:C;C8;H  8O :7OI
8O 7 C7N?CKC E< :7OI J>; !/XC?N ?I D;7HBO KD7<<;9J;: fX8  EH
 ?= 9 .>?I H;IKBJ ?I H;7IED78B; ;L?:;D9; E< J>; C?N;: F?N;B ;<
<;9J J>; !/ 9>7D=; ED J>; F?N;B B;L;B C7O DEJ 8; 7:;GK7J;BO ?:;DJ?U;:
M>;D KD9;HJ7?DJ?;I ;N?IJ ?D 8EJ> J>; fXC 7D: J>; IF;9?;I !/ JH;D:I
4. Discussion and conclusions
.>?I F7F;H :;BL;I ?DJE J>; C;J>E:EBE=?97B KD9;HJ7?DJO E< J>; !/
?:;DJ?U97J?ED 7 FHE8B;C ?D:K9;: 8O J>; C?N;: F?N;B ;<<;9J ?D 0# FHE:
UNCORRECTED PROOF
X. Chen et al. Remote Sensing of Environment xxx (2018) xxx-xxx
Fig. 3. >7D=;I ?D !/XC?N Δ!/XC?N KD:;H -9;D7H?E # 8O 7 J>; H;B7J?L; J>H;I>EB: C;J>E: 7D: 8 J>; 9KHL7JKH; C;J>E:
Fig. 4. >7D=;I ?D !/XC?N Δ!/XC?N KD:;H 78 -9;D7H?EI ## 9>7D=?D= J>; (0#XC7N 9: ## 9>7D=?D= J>; (0#XC?D 7D: ;< ## 9>7D=?D= J>; B;D=J> E< J>; =HEMJ> F;H?E:
K9JI BJ>EK=> J>; C?N;: F?N;B ;<<;9J >7I 8;;D H;9E=D?P;: ?D F7IJ B?J;H
7JKH; ;= 7:;9A ;J 7B  ";BC7D  ->;D ;J 7B  J>?I
F?BEJ IJK:O ?I 7CED= J>; UHIJ JE ;N7C?D; J>; I;DI?J?L?JO E< J>; !/ J>7J
C7O 8; 9ED<EKD:;: 8O J>; KD9;HJ7?D 9EDIJ?JK;DJI ?D J>; ?DJ;=H7J?ED E<
IF;9JH7B <;7JKH;I .>HEK=> J>; I;DI?J?L?JO J;IJ KD:;H UL; I9;D7H?EI 7D:
9ECF7H?D= JME !/ ?:;DJ?U97J?ED C;J>E:I J>; I?CKB7J?ED 7FFHE79>
EKJB?D;I J>; FEJ;DJ?7B B?C?J7J?EDI E< KI?D= I7J;BB?J;87I;: =H;;DD;II FHEN
?;I JE ;IJ?C7J; J>; F?N;B87I;: L;=;J7J?ED !/ 9>7D=;I ?D J>; 9EDJ;NJ E<
9B?C7J; 9>7D=;
.>; C7@EH 9EDJH?8KJ?ED E< J>?I H;I;7H9> ?I JE >?=>B?=>J J>; ?D<;H;D
J?7B ;HHEHI 8;JM;;D I7J;BB?J;:;H?L;: &-* 7D: ?D I?JK F>;DEBE=?97B E8
I;HL7J?EDI ED B?C?J;: ?D:?L?:K7BI E< FB7DJ IF;9?;I BJ>EK=> J>; L;=;
J7J?ED H;IFEDI; JE 9B?C7J; 9>7D=; ?DLEBL;I 9ECFB;N C;9>7D?ICI J>7J
I>7F; J>; F>;DEBE=?97B 9O9B; ?D L7H?EKI M7OI ?J H;C7?DI KD9B;7H J>7J
?< IK9> H;IFEDI;I 97D 8; 799KH7J;BO :;J;9J;: 7D: CED?JEH;: 8O I7J;BB?J;
C;7IKH;C;DJI 7:;9A ;J 7B  .>; B79A E< 9EDI?IJ;D9O 8;JM;;D
H;CEJ;BO I;DI;: :7J7I;JI 7D: =HEKD:87I;: E8I;HL7J?EDI 1>?J; ;J 7B
UNCORRECTED PROOF
X. Chen et al. Remote Sensing of Environment xxx (2018) xxx-xxx
Fig. 5. >7D=;I ?D !/XC?N Δ!/XC?N M?J> H;IF;9J JE 9>7D=;I ?D !/ Δ!/ E< 7 8 ;D:C;C8;H  7D: 9 : ;D:C;C8;H  KD:;H -9;D7H?E ### M?J> :?<<;H;DJ fXC
 9EKB: 8; 7 H;IKBJ E< JME :?<<;H;DJ IEKH9;I E< ?D<;H;DJ?7B ;HHEHI ?D
:K9;: 8O J>; C?N;: F?N;B ;<<;9J 7I ?BBKIJH7J;: ?D .78B;  ?HIJ J>;H; ?I
7 FEII?8?B?JO J>7J =HEKD:87I;: !/ E8I;HL7J?EDI ED B?C?J;: ?D:?L?:K7BI
E< FB7DJ IF;9?;I C7O DEJ >7L; 9>7D=;: 7I >OFEJ>;I?P;: 8KJ J>; 7==H;
=7J; 0# JH7@;9JEHO C7O C?I?DJ;HFH;J J>; <79J 8;97KI; E< J>; B7D: 9EL;H
9>7D=; EH 7BJ;H;: IF;9?;I 9ECFEI?J?ED -9;D7H?E # EH 9>7D=;I E< EJ>;H
DED!/ L7H?78B;I -9;D7H?E ## .>?I C?I?DJ;HFH;J7J?ED ?I I?C?B7H JE J>;
.OF;  ;HHEH ?D >OFEJ>;I?I J;IJ?D= M>;H; J>; !/ I>?<J ?I ;HHED;EKIBO
F;H9;?L;: -;9ED: ?J ?I 7BIE FEII?8B; J>7J J>; =HEKD:87I;: !/ 9>7D=;
?I E8I;HL;: 8KJ J>; &-* <7?BI JE ?:;DJ?<O J>; JH;D: EH KD:;H;IJ?C7J; J>;
C7=D?JK:; 8;97KI; 9>7D=;I E< L7H?78B;I ?D EFFEI?J; :?H;9J?EDI 97D EW
I;J J>; ;<<;9J -9;D7H?E ### .>?I JOF; E< C?I?DJ;HFH;J7J?ED ;GK?L7B;DJ JE
J>; .OF; ## ;HHEH ?D J>; F7HB7D9; E< >OFEJ>;I?I J;IJ?D= 9EKB: E8<KI97J;
J>; JHK; ?CF79J E< 9B?C7J; 9>7D=;
.>; I?CKB7J?ED H;IKBJI FHEL?:; ?DI?=>JI ?DJE ;<<;9J?L; IJH7J;=?;I <EH
7D7BOP?D= IFH?D= F>;DEBE=O KI?D= 0# FHE:K9JI ?HIJ J>; fXC E< 7D ;D:
C;C8;H ?D 7 C?N;: F?N;B >7I C7HA;: ?DVK;D9;I ED J>; ?:;DJ?U97J?ED
E< J>; !/XC?N 9ECF7H?D= JE EJ>;H L7H?78B;I .>KI M>;D ;NJH79J?D= &-*
C;JH?9I J>; >ECE=;D;?JO E< J>; B7D:I97F; CKIJ 8; ;DIKH;: 8O I;B;9J
?D= F?N;BI J>7J 7H; H;B7J?L;BO IJ78B; ?D J>; B7D: 9EL;H EH IF;9?;I 9ECFE
I?J?ED EL;H J>; J?C; F;H?E: E< E8I;HL7J?ED EH ;N7CFB; 7I I>EMD 8O
K9>;C?D ;J 7B  7D: &?7D= ;J 7B  J>; 9E>;I?ED 8;JM;;D
J>; &-* 7D: J>; =HEKD: E8I;HL7J?ED 97D 8; B7H=;BO ?CFHEL;: ?< J>;
IF;9?;I 9ECFEI?J?ED EH J>; ;N79J FB7DJ <KD9J?ED JOF;I 8;9EC; ADEMD
-;9ED: H;9;DJBO IEC; U;B: ;NF;H?C;DJI >7L; H;IEHJ;: JE J>; E8I;HL7
Table 2
#D<;H;DJ?7B ;HHEHI ?D J>; ?:;DJ?U97J?ED E< !/ JH;D: ?D:K9;: 8O J>; C?N;: F?N;B ;<<;9J
!HEKD:87I;:!/JH;D: ,;CEJ;BOI;DI;:!/JH;D:
"7II>?<J;: (EI>?<J
"7II>?<J;: Correct inference Type 2 error:
-9;D7H?E###
(EI>?<J Type 1 error:
-9;D7H?E#
-9;D7H?EI##
Correct inference
J?EDI E< ?D:?L?:K7B FB7DJ IF;9?;I <EH L7B?:7J?D= J>; &-* ;= 3K ;J 7B
 1>?J; ;J 7B  4>7D= ;J 7B  )KH I?CKB7J?ED H;IKBJI
KH=; 97KJ?ED ED IK9> 7FFHE79>;I J>; L7B?:7J?ED I>EKB: 8; 9ED:K9J;:
?D U;B:I M?J> 7 I?D=B; IF;9?;I EH <;M IF;9?;I M?J> 7 IJ78B; 9ECFEI?
J?ED .>?H: M; <EKD: J>7J ?J ?I B?A;BO J>7J 8EJ> J>; H;B7J?L; J>H;I>EB:
C;J>E: 7D: J>; 9KHL7JKH; C;J>E: M?BB ;HHED;EKIBO :;J;9J J>; 9>7D=;
E< J>; F?N;B !/ ;L;D ?< J>; ;D:C;C8;H !/I :E DEJ I>?<J .>?I H;IKBJ
IK==;IJI J>7J J>; !/ ?:;DJ?U97J?ED 87I;: EDBO ED J>; J>H;I>EB: EH J>;
:;H?L7J?L; E< J>; C?N;: 0# J;CFEH7B FHEUB; C7O DEJ 7:;GK7J;BO 97FJKH;
J>; EDI;J E< =H;;DD;II 7I J>;H; ?I 7 9EDI?:;H78B; :;=H;; E< KD9;HJ7?DJO
M?J>?D J>; B7D: IKH<79; 9ED:?J?EDI 7I M;BB 7I J>; 0# FHE:K9JI 8;?D= KI;:
";BC7D 
.>; IJK:O ;CFBEOI J>; I?CKB7J?ED 7FFHE79> JE ;NFBEH; J>; I;DI?J?L?JO
E< H;CEJ;BO I;DI;: !/ JE J>; C?N;: F?N;B ;<<;9J 1>?B; EL;H9EC?D= J>;
B78EH ?DJ;DIJ?L; D7JKH; E< U;B: MEHA J>; I?CKB7J?ED 7FFHE79> 97D 9ED
JHEB F>;DEBE=?97B L7H?78B;I M?J>?D 7 :;I?H78B; H7D=; .E EKH ADEMB;:=;
?J ?I H;B7J?L;BO 9>7BB;D=?D= JE ?:;DJ?<O =HEKD:87I;: !/I E< 9E;N?IJ?D=
IF;9?;I EL;H CKBJ?FB; O;7HI 7D: 9EDJHEBB?D= <EH J>;?H DED!/ L7H?78B;I
IK9> 7I J>; IF;9?;I 9ECFEI?J?ED J>; IE?B 879A=HEKD: J>; C7N?CKC
C?D?CKC (0# 7D: J>; =HEMJ> F;H?E: ?I D;7HBO ?CFEII?8B; 1>?B; J>;
I?CKB7J?ED 7FFHE79> 97D ?D9EHFEH7J; J>;I; 7IIKCFJ?EDI 7::?J?ED7B FH;
97KJ?EDI 7H; D;;:;: ?HIJ J>; F7F;H ;N7C?D;I EDBO JME ;D:C;C8;HI 7I
?J ?I J>; I?CFB?;IJ 97I; E< 9E;NI?J?D= IF;9?;I ?D 7D ;9EIOIJ;C .E <79?B?
J7J; <KHJ>;H ;NFBEH7J?ED E< J>; FHE8B;C M; >7L; :;I?=D;: 7 I?CKB7J?ED
JEEBA?J JE ?D9BK:; 97I;I E< J>H;; EH CEH; ;D:C;C8;HI 7I I>EMD ?D J>;
-KFFB;C;DJ7HO 7J7 -;9ED: ?J I>EKB: 8; DEJ;: J>7J J>; 9ED9BKI?EDI
:H7MD <HEC J>; I?CKB7J?ED H;IKBJI 97DDEJ 8; <KBBO M7HH7DJ;: M?J>EKJ
U;B: ;NF;H?C;DJI *EII?8B; C;J>E:I JE L7B?:7J; EKH UD:?D=I ?D9BK:; :;
J;9J?D= F>;DEBE=?97B 9>7D=;I M?J> <;M C?N;: IF;9?;I 7J 7 IC7BB IF7J?7B
I97B; 7D: 9ED:K9J?D= 9B?C7J;9EDJHEBB;: ;NF;H?C;DJI 1EBAEL?9> ;J 7B
 #D J>?I H;IF;9J J>; H;IKBJI :;H?L;: <HEC EKH 7D7BOJ?9 <H7C;MEHA
7D: J>; I?CKB7J?ED JEEBA?J M?BB <79?B?J7J; J>; :;I?=D E< IK9> U;B: ;NF;H
?C;DJI .>?H: J>;H; 7H; EJ>;H IEKH9;I E< C;7IKH;C;DJ KD9;HJ7?DJ?;I ?D
J>; FHE:K9J?ED E< 0# FHE:K9JI IK9> 7I J>; J?C; 9ECFEI?J?D= J>; E8I;H
L7J?ED =;EC;JHO 7D: J>; =H?::?D= 7HJ?<79JI -K9> KD9;HJ7?DJ?;I I>EKB:
8; 7D7BOP;: ?D <KJKH; 7JJ;CFJI
UNCORRECTED PROOF
X. Chen et al. Remote Sensing of Environment xxx (2018) xxx-xxx
Acknowledgements
.>?I IJK:O ?I IKFFEHJ;: 8O J>; KD: <EH H;7J?L; ,;I;7H9> !HEKFI
E< (7J?ED7B (7JKH7B -9?;D9; EKD:7J?ED E< >?D7 (E  J>;
%;O ,;I;7H9> *HE=H7C E< HEDJ?;H -9?;D9;I !H7DJ (E
+34--1+ E< J>; >?D;I; 97:;CO E< -9?;D9;I 7 =H7DJ <HEC
J>; (7J?ED7B (7JKH7B -9?;D9; EKD:7J?ED E< >?D7 (E  7D:
J>; 3EKJ> #DDEL7J?ED *HECEJ?ED IIE9?7J?ED E< J>; >?D;I; 97:;CO E<
-9?;D9;I (E  .>; 7KJ>EHI 7FFH;9?7J; &?9ED= &?K <EH 7II?IJ?D=
M?J> :;I?=D?D= J>; I?CKB7J?ED JEEBA?J
Appendix A. Supplementary data
-KFFB;C;DJ7HO :7J7 JE J>?I 7HJ?9B; 97D 8; <EKD: EDB?D; 7J >JJFI
:E?EH=@HI;
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>B  !EM;H -. KHHEMI -( ->787DEL (0 'OD;D? , %DO7P?A>?D 3 
'ED?JEH?D= IFH?D= 97DEFO F>;DEBE=O E< 7 :;9?:KEKI 8HE7:B;7< <EH;IJ KI?D= ')#-
,;CEJ; -;DI DL?HED   T
C7D  ,7D:H?7C7D7DJ;D7 "* *E:7?H;  HEK?D ,  /FI97B; ?DJ;=H7J?ED E<
DEHC7B?P;: :?<<;H;D9; L;=;J7J?ED ?D:;N J>; FHE8B;C E< IF7J?7B >;J;HE=;D;?JO #
.H7DI !;EI9? ,;CEJ; -;DI   T
:7CI $ -C?J> ') $E>DIED *  -F;9JH7B C?NJKH; CE:;B?D= 7 D;M 7D7BOI?I
E< HE9A 7D: IE?B JOF;I 7J J>; 0?A?D= &7D:;H  I?J; $EKHD7B E< !;EF>OI?97B ,;I;7H9>
-EB?: 7HJ>   T
7:;9A 1 ED:;7K  öJJ9>;H % EAJEH  &K9>J 1 -9>78;H $ -?J9> - 
,;IFEDI;I E< IFH?D= F>;DEBE=O JE 9B?C7J; 9>7D=; (;M *>OJEB   T
HEMD ' ; ;KHI %' '7HI>7BB '  !BE87B F>;DEBE=?97B H;IFEDI; JE 9B?C7J;
9>7D=; ?D 9HEF 7H;7I KI?D= I7J;BB?J; H;CEJ; I;DI?D= E< L;=;J7J?ED >KC?:?JO 7D: J;C
F;H7JKH; EL;H  O;7HI ,;CEJ; -;DI DL?HED  T
K9>;C?D  !EK8?;H $ EKHH?;H !  'ED?JEH?D= F>;DEBE=?97B A;O IJ7=;I 7D: 9O
9B; :KH7J?ED E< J;CF;H7J; :;9?:KEKI <EH;IJ ;9EIOIJ;CI M?J> ()0",, :7J7 ,;
CEJ; -;DI DL?HED   T
!7E 2 "K;J; , (? 1 '?KH7 .  )FJ?97B8?EF>OI?97B H;B7J?EDI>?FI E< L;=
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... Therefore, earlier leaf unfolding would also lead to the advance of programmed leaf life cycle and further result in different changes of V NDVI in early and late GUP. As our analyses were conducted based on grid NDVI product, the mixed pixel effect would make the changes of V NDVI more complicated (Chen et al., 2018). For instance, it is well known that increasing temperature can accelerate plant growth via temperature-dependent enzymatic catalytic reactions if temperature is lower than temperature optima (Atkinson & Porter, 1996). ...
... However, the optimal temperature of different species in a same pixel may be various. The different temperature optima and asynchronous movements of growth cycles among different species would generate a nonlinear response of V NDVI to climate change at landscape or larger levels (Chen et al., 2018). ...
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Global vegetation greening has been widely confirmed in previous studies, yet the changes in the velocity of green‐up in each month of green‐up period (GUP) remains unclear. Here, we defined the velocity of vegetation green‐up as VNDVI (the monthly increase of Normalized Difference Vegetation Index (NDVI) during GUP) and further explored its response to climate change in middle‐high latitude Northern Hemisphere. We found that in early GUP, VNDVI generally showed positive trends from 1982 to 2015, while in late GUP, it showed negative trends in most areas. Such contrasting trends were mainly due to a positive temperature effect on VNDVI in early GUP, but this effect turned negative in late GUP. The increase of soil moisture also in part explained the accelerated vegetation green‐up, especially in the arid and semi‐arid ecosystems of inland areas. Our analyses also indicate that the first month of the GUP was the key stage impacting vegetation greenness in summer. Future warming may continuously speed up the early growth of vegetation, altering the seasonal trajectory of vegetation and its feedbacks to the Earth system.
... Remote sensing (RS) has been shown to have great potential and advantages in earth surface monitoring for several decades [1][2][3]. Most of the current satellites have low to moderate spatial resolution, meaning that one pixel usually contains a mixture of vegetation (tree and understory) and background (soil, shade, etc.), which is known as the mixed-pixel problem [4][5][6]. This issue is particularly significant for semi-arid and dryland ecosystems where the vegetation is usually sparse [7]. ...
... Approaches for solving this problem include reducing soil signals at low vegetation cover by adding soil brightness correction factors (SAVI, MSAVI2, OSAVI, GSAVI) [24][25][26][27] or enhancing vegetation signals at high vegetation cover by adding weighting coefficients (EVI, WDRVI, NIRv) [20,21,28]. However, these indices that are derived from most satellites still may not accurately capture surface phenological changes due to their limited spatial resolutions [6]. In addition, shade also causes biased NDVI values, thus significantly reducing land cover classification accuracy. ...
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Remote sensing (RS) for vegetation monitoring can involve mixed pixels with contributions from vegetation and background surfaces, causing biases in signals and their interpretations, especially in low-density forests. In a case study in the semi-arid Yatir forest in Israel, we observed a mismatch between satellite (Landsat 8 surface product) and tower-based (Skye sensor) multispectral data and contrasting seasonal cycles in near-infrared (NIR) reflectance. We tested the hypothesis that this mismatch was due to the different fractional contributions of the various surface components and their unique reflectance. Employing an unmanned aerial vehicle (UAV), we obtained high-resolution multispectral images over selected forest plots and estimated the fraction, reflectance, and seasonal cycle of the three main surface components (canopy, shade, and sunlit soil). We determined that the Landsat 8 data were dominated by soil signals (70%), while the tower-based data were dominated by canopy signals (95%). We then developed a procedure to resolve the canopy (i.e., tree foliage) normalized difference vegetation index (NDVI) from the mixed satellite data. The retrieved and corrected canopy-only data resolved the original mismatch and indicated that the spatial variations in Landsat 8 NDVI were due to differences in stand density, while the canopy-only NDVI was spatially uniform, providing confidence in the local flux tower measurements.
... For decades, a series of classifiers has been developed to interpret remote sensing images by allocating each pixel to a single-class label . However, the signal (i.e., spectrum composed of the reflectance of all spectral bands) of a pixel is generally composed of spectra from several land cover types, known as the "mixed-pixel problem" (Chen et al., 2018a). Moreover, the inherent point spread function effect of sensors can aggravate the mixed pixel problem (Wang et al., 2020b). ...
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Spatio-temporal subpixel mapping (STSPM) has shown great potential for monitoring land surfaces, by generating land cover maps with both fine spatial and temporal resolutions. Selecting cloud-free fine spatial resolution images as ancillary data for STSPM can ensure that the temporal dependence term is measured for all subpixels, as in all current STSPM methods. However, such images are generally limited by cloud contamination, thereby resulting in great land cover changes between the available clear image and the desired fine spatial resolution land cover map. This research proposes a cloud-independent STSPM (C-STSPM) method to reconstruct the fine spatial resolution land cover maps by using cloudy images directly, which are assumed to have fewer land cover changes than temporally distant clear images. Cloud-independent spatio-temporal dependence was proposed in the presence of cloudy pixels. Experiments were performed under various cloud conditions involving 21 × 21 pairs of simulated cloudy images. The results demonstrate that by utilizing land cover information of clear pixels in cloudy images, more accurate prediction can be produced by C-STSPM compared to directly discarding those cloudy images, even if the number of cloud pixels increases to 95%. The advantage of C-STSPM is more evident when the clouds are distributed sparsely, which benefits from the increased number of clear pixels at the edge of the cloudy areas. Furthermore, a negative linear correlation was detected between the prediction accuracy and the ratio of overlapping cloudy pixels in the cloudy images. Moreover, the C-STSPM method helps to deal with abrupt changes occurred in the temporally distant cloud-free images by utilizing the temporally adjacent cloudy images with gradual land cover changes. Overall, the C-STSPM method provides a completely new solution to make fuller use of the widely existing cloudy images in multi-scale time-series images.
... Wu et al. (2017) [19] compared different satellite-based methods for estimating the growing season onset, and all of them showed relatively low correlation with the observed results of eddy flux measured at 60 sites. Many reasons will lead to the deviation of prediction results [27,37], but the main issue, i.e., the scale-up vs. mixed-pixel problem, is hard to shift, but perhaps unavoidable [26,38]. Although the spatial resolution of satellite-derived vegetation index products has been improved in recent years, a given pixel may include a combination of different vegetation types that might result in significantly different phenology, which challenges the determination of a reliable onset date of spring phenological phases within a heterogeneous ecoregion. ...
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... The use of UAV observations and tower-mounted cameras can, to a certain extent, remediate the scale mismatch issue [27,28]. Meanwhile, considering the complexity of scale effects, computer simulations based on 3D radiative-transfer modelling can be used as a powerful tool to explore the scale effects or mixed image effects in vegetation phenology remote-sensing monitoring [29]. In addition, for low-and medium-resolution remote-sensing phenology products (e.g., MODIS/VIIRS), it is difficult even for UAVs or phenocams to provide validation data at the comparable pixel scale, in which case indirect "validation" can be performed using higher-spatial-resolution satellite data [30]. ...
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In the last two decades the availability of global remote sensing data sets has provided a new means of studying global patterns and dynamics in vegetation. The vast majority of previous work in this domain has used data from the Advanced Very High Resolution Radiometer, which until recently was the primary source of global land remote sensing data. In recent years, however, a number of new remote sensing data sources have become available that have significantly improved the capability of remote sensing to monitor global ecosystem dynamics. In this paper, we describe recent results using data from NASA's Moderate Resolution Imaging Spectroradiometer to study global vegetation phenology. Using a novel new method based on fitting piecewise logistic models to time series data from MODIS, key transition dates in the annual cycle(s) of vegetation growth can be estimated in an ecologically realistic fashion. Using this method we have produced global maps of seven phenological metrics at 1-km spatial resolution for all ecosystems exhibiting identifiable annual phenologies. These metrics include the date of year for (1) the onset of greenness increase (greenup), (2) the onset of greenness maximum (maturity), (3) the onset of greenness decrease (senescence), and (4) the onset of greenness minimum (dormancy). The three remaining metrics are the growing season minimum, maximum, and summation of the enhanced vegetation index derived from MODIS. Comparison of vegetation phenology retrieved from MODIS with in situ measurements shows that these metrics provide realistic estimates of the four transition dates identified above. More generally, the spatial distribution of phenological metrics estimated from MODIS data is qualitatively realistic, and exhibits strong correspondence with temperature patterns in mid- and high-latitude climates, with rainfall seasonality in seasonally dry climates, and with cropping patterns in agricultural areas.
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Regional phenology is important in ecosystem simulation models and coupled biosphere/atmosphere models. In the continental United States, the timing of the onset of greenness in the spring (leaf expansion, grass green-up) and offset of greenness in the fall (leaf abscission, cessation of height growth, grass brown-off) are strongly influenced by meteorological and climatological conditions. We developed predictive phenology models based on traditional phenology research using commonly available meteorological and climatological data. Predictions were compared with satellite phenology observations at numerous 20km×20km contiguous landcover sites. Onset mean absolute error was 7.2 days in the deciduous broadleaf forest (DBF) biome and 6.1 days in the grassland biome. Offset mean absolute error was 5.3 days in the DBF biome and 6.3 days in the grassland biome. Maximum expected errors at a 95% probability level ranged from 10 to 14 days. Onset was strongly associated with temperature summations in both grassland and DBF biomes; DBF offset was best predicted with a photoperiod function, while grassland offset required a combination of precipitation and temperature controls. A long-term regional test of the DBF onset model captured field-measured interannual variability trends in lilac phenology. Continental application of the phenology models for 1990-1992 revealed extensive interannual variability in onset and offset. Median continental growing season length ranged from a low of 129 days in 1991 to a high of 146 days in 1992. Potential uses of the models include regulation of the timing and length of the growing season in large-scale biogeochemical models and monitoring vegetation response to interannual climatic variability.
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Fall foliage coloration is a phenomenon that occurs in many deciduous trees and shrubs worldwide. Measuring the phenology of fall foliage development is of great interest for climate change, the carbon cycle, ecology, and the tourist industry; but little effort has been devoted to monitoring the regional fall foliage status using remotely-sensed data. This study developed an innovative approach to monitoring fall foliage status by means of temporally-normalized brownness derived from MODIS (Moderate Resolution Imaging Spectroradiometer) data. Specifically, the time series of the MODIS Normalized Difference Vegetation Index (NDVI) was smoothed and functionalized using a sigmoidal model to depict the continuous dynamics of vegetation growth. The modeled temporal NDVI trajectory during the senescent phase was further combined with the mixture modeling to deduce the temporally-normalized brownness index which was independent of the surface background, vegetation abundance, and species composition. This brownness index was quantitatively linked with the fraction of colored and fallen leaves in order to model the fall foliage coloration status. This algorithm was tested by monitoring the fall foliage coloration phase using MODIS data in northeastern North America from 2001 to 2004. The MODIS-derived timing of foliage coloration phases was compared with in-situ measurements, which showed an overall absolute mean difference of less than 5days for all foliage coloration phases and about 3days for near peak coloration and peak coloration. This suggested that the fall foliage coloration phase retrieved from the temporally-normalized brownness index was qualitatively realistic and repeatable.
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Understory vegetation dynamics was monitored throughout the 1998 growing season in four eastern Canadian forest types: sugar maple-American beech (Acer saccharum Marsh.-Fagus grandifolia Ehrh.); sugar maple-yellow birch (Betula alleghaniensis Britton); balsam fir (Abies balsamea [L.] Mill.); and black spruce-jack pine (Picea mariana [Mill.] BSP-Pinus banksiana Lamb.). Significant differences in biomass were obtained among species groups (herbaceous, woody, and mosses) within each site. However, biomass did not vary significantly throughout the growing season, except in the sugar maple-American beech site. The four sites differed in total biomass, but these differences could be explained mainly by the presence of some key species (e.g., Lycopodium lucidulum Michx.) and the light regime under the canopy. Species richness varied throughout the growing season on each site. However, peaks in richness did not occur at the same time in each site: richness peaked later with increasing latitude, from late May in the southernmost site to late September in the northernmost site. Average richness did not vary across sites. Aboveground nutrient concentrations for herbaceous species were greater than nutrient concentrations for stems of woody species. However, nutrient concentrations for woody species leaves were comparable to those of herbaceous species. Our results highlighted the importance of understory species in the cycling of nutrients and their capacity to keep nutrients within a site. Specific leaf area (SLA) for herbaceous species was greater than SLA for woody species, which indicated a greater capacity to acclimate to different light conditions than woody species. Increase in leaf area ratio with decrease in height for sugar maple and beech suggests that both species decreased their efficiency to produce biomass at the ground level as they were overtopped by higher surrounding vegetation.