Supplemental materials
S1
Locations of the source regions in China
Fig. A.1. Locations of the source regions of the Yellow, Yangtze, and Lantsang rivers in China.
S2
Determination of variations in trends of mobile sandy lands
Trends in the development of mobile sandy lands were determined using a decision tree of
classifications, which was constructed via a series of binary decisions to place pixels into classes
(see S2-Fig. A.1 below). The decision tree was used to perform multistage classifications based on
a series of binary decisions to place pixels into classes, which is effective for clear spectrum
mechanism such as water bodies and land. The maximum likelihood classification assumes that
the statistics for each class in each band are normally distributed and calculates the probability that
a given pixel belongs to a specific class, which is effective for irregular spectrum mechanism. We
1
combined these two methods to extract information regarding the mobile sandy lands.
S2-Fig. A.1. Decision tree for mobile sandy land classification.
Note 1: Water Body and Land classifications
Water bodies show seasonal variations in spectral absorption rate, with a relatively high rate in the
dry season and a low rate in the flood season. In addition, suspended material, chlorophyll, and
other factors may disturb the normal spectrum. Water bodies are often confused with other classes
such as wetlands, wet fields, and shaded hillsides. Consequently, we used lower thresholds of
NDVI and the infrared band of the images to identify water bodies during drier periods.
Note 2: Vegetation and No Vegetation classifications
The spectral reflectance of vegetation has two peaks in the green and infrared bands, and plants
show obvious physiological variations including sprouting, growing, maturity, fallen leaves, and
dying/withered. Classifications for vegetated areas included forest, shrub, grassland, and artificial
vegetation (i.e., farmlands), while classifications for land with no vegetation cover included
buildings, bare land (i.e., unvegetated mountains), and glacier/permanent snow. Vegetation states
were classified using the higher threshold of accumulated NDVI, maximum NDVI, and the
infrared band during the growing season.
Note 3: Interpretation of mobile sandy lands
After land areas with no vegetation cover were identified, the supervised classification (maximum
likelihood) method was employed to interpret the buildings, glacier/permanent snow, and mobile
sandy land. For each class, training pixels (S2-Fig. A.2) were selected in the images, and the
maximum likelihood classification was carried out in ENVI4.7. Among the training pixels, the B
and C types are bare rock and bare land, respectively. However, during the field investigations we
2
found that mobile sand dunes had usually developed on their surfaces, and therefore, we classified
them as mobile sandy lands.
S2-Fig. A.2. Training pixels for identifying mobile sandy lands.
S3
Trend change detection in NDVI time series
Following previous studies (e.g., Holben 1986, Stowe et al. 1991), during analysis of the
temporal trends in the NDVI the maximum value composites (MVC), processed by month in
the growing season (May to September), were applied. To analyze the changes in NDVI, a
linear regression model was used to determine the change trend of every pixel. For each pixel,
the linear relationship between NDVI and YEAR was calculated using the ordinary least
squares (OLS) method:
NDVI = SLOPE × YEAR + b
n
SLOPE 
n
 i  NDVI i
i
1
n
n
i
i
1
n
n
i 1
n
i 1
 ( i )( NDVI i )
2
 ( i )2
i 1
Here n is the number of the monitored year, and i is a certain year between 1 and n.
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The value of SLOPE for the NDVI indicates the trend in NDVI (e.g., Zhen et al. 2003),
and large values of SLOPE show that there were rapid changes in vegetation productivity. For
instance, positive values of SLOPE indicate an increasing trend in NDVI and that vegetation
rehabilitation had occurred in that particular region (e.g., Herrmann et al. 2005, Forkel et al.
2013), and that vegetation productivity had increased (e.g., Xiao and Moody 2005, Beck et al.
2006).
S4
Determination of actual NPP (net primary productivity) and potential NPPActual NPP
Actual NPP was calculated using the Carnegie–Ames–Stanford Approach (CASA) model,
determined from the product of absorbed photosynthetically active radiation (APAR) and light use
efficiency (ε):
NPP(x ,t )  APAR(x ,t )  (x ,t )
APAR(x,t)  FPAR(x,t)  PAR(x,t)
where x is the spatial location and t is the timescale.
(1) PAR (photosynthetically active radiation)
Here, PAR is the solar energy from the visible bands (400–700 nm), which is about 45% to 50% of
R s (total surface solar radiation). For details see (Zotarelli et al.,2010).
(2) FPAR (fraction of absorbed photosynthetically active radiation)
FPAR 
(SR  SR min )  (FPAR max  FPAR min )
 FPAR min
SR max  SR min
SR 
NIR
1  NDVI

RED
1  NDVI
Where SRmin represents SR for unvegetated land areas and is 1.08 for all grid cells (Potter et al.,
1993). In dependent of vegetation, SRmax vary between 4.14 and 6.17. Here for evergreen
broadleaf forests is 4.14, for deciduous broadleaf forests and broadleaf and needleleaf mixed
forests are 6.17, for evergreen needleleaf forests and deciduous needleleaf forests are 5.43, and for
broadleaf shrubs, temperate grasslands, savannas, alpine meadows and tundra, deserts, and
cultivation are 5.13 (Piao et al., 2005 ).
(3) ε (Light use efficiency)
4
   0  T1  T2  W s
Here,  0 is the maximum light use efficiency (e.g., Wang et al. 2010) and it is 0.405 (Potter et
al., 1993). The values of  0 may alter NPP values and magnitude trends, but the relative trends
is not be changed. T 1 , T 2 , and W s are stress scalars that reduce  0 . T 1 and T 2 represent
monthly deviations from site-specific optimal temperature and from 20°C, respectively. W s is the
monthly relative soil moisture deficit and is based on the difference between actual and potential
evapotranspiration determined from the soil water balance.
T1  0.0005(Topt  20) 2  1
T2 
Where
1.1814
1

1  exp{0.2(Topt  10  T mon )} 1  exp{0.3(Topt  10  T mon )}
Topt (optimum temperature) is defined as the monthly temperature when the NDVI
reaches its maximum of the year, and T mon is monthly mean temperature (˚C).
W s  0.5  0.5 
EET(x ,t )
PET(x ,t )
Where EET is the estimated evapotranspiration (mm) generated by Soil Moisture SubModel, for
details see (Potter et al,.1993 & Saxton et al.,1986). PET is potential evapotranspiration (mm)
calculated by Thornthwaite index with a correction factor of day-length (CF), for details see
(Thornthwaite 1948 & Fang and Yoda, 1990).
Potential NPP
Potential NPP was calculated using the model in the same manner as CASA, except for the
calculations of FPAR, which was generated from vegetation and meteorological parameters as
follows:
FPAR  1  e  kLAI
where k = 0.5, and the leaf area index (LAI) was obtained by MOD15A2 (Xu et al.,2009).
S5
Significance of temporal trends in actual and potential NPPs
Slope of the potential NPP
Potential NPP refers to the NPP under conditions with no impacts from human activities and with
5
the outputs of NPP controlled by climate change (e.g., Prince et al. 2009). The slope of the
potential NPP is expressed as follows:
pNPP = SLOPE × YEAR + b
n
SLOPE 
n
 i  pNPPi
i 1
n
n
i
i
1
n
n
i 1
n
i 1
 ( i )( pNPPi )
2
 ( i )2
i 1
where n is the number of the monitored year, and i is a certain year between 1 and n. When the
value of the slope is positive, this indicates that climate change has led to an increase in vegetation
productivity (e.g., Beck et al. 2006), and vice versa.
Residual
The residual was defined as the difference between the potential NPP and the actual NPP (e.g., Xu
et al. 2009), and was used to determine the impacts of human activity and climate change on the
regional ecology and environments (e.g., Evans and Geerken 2004, Geerken and Ilaiwi 2004).
Positive values for the residuals indicate that human activity had a negative effect on vegetation
rehabilitation (e.g., Wessels et al. 2007), and negative residuals indicate that human activities (e.g.,
programs to return farmland to forests, and to return forests to grasslands in some arid, semiarid,
and semihumid regions) had a positive effect on vegetation rehabilitation.
In addition, as with the NDVI slope, the residual slope of NPP also indicated the
environmental and ecological impacts of human activities. For instance, positive values of the
slope show that over a relatively long period human activities had a negative effect on the
environment, and vice versa (e.g., Wessels et al. 2007).
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