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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
Step 4: Elimination of false peaks in the NDVI curve based
on the definition of one-cropping and counting the number
of false peaks i N >
X, G f , G b of each wave packet can be calculated when all the
peaks and troughs were recognized in step 3, and then false
peaks were identified according to the definition of “one-
cropping” and the number of the false peaks (N) was counted .If
N is zero, the correcting of NDVI time-series will finish
(Figure.4(c)), and then do step5. Otherwise, replace the false
peaks by linearly interpolated values and return to step3.
time/(15 days)
108 120 132 144
time/(15 days)
Figure.4 Data processing of NDVI time series
Step 5: Exporting the cropping index of each pixel
The cropping index of ith pixel is “C7, =MX 100”, where M is
the number of “once cropping” waves.
Step 6: Calculating cropping index in administration units
Average Cl of certain administration unit can be calculated
following equation (3):
(3)
Where n in the above expression is the number of the total
arable land pixels in the administration unit.
3.3 The Method of Cropping Index Variation Extracting
Since cropping anomalies often occurred during 1982-2003, the
Least Absolute Deviation (LAD) linear regression was
employed instead of Ordinary Least Square (OLS) regression to
calculate the change trend of cropping index (VCI) during 1982-
2003. Comparing to OLS, LAD could reduce the sensitivity to
outliers effectively and provide a robust and plausible estimate.
The detailed information about LAD regression can be found in
Powell (1984).
4. RESULTS AND DISCUSSION
4.1 Precision Evaluation
Precision evaluation was carried out as the comparison between
the Cl extracted trough our new method and that calculated
from statistical data at province scale. As mentioned above,
cropping index is defined as the ratio of the total seeding area to
the arable land area. Annual seeding area of every year during
1982 to 2003 is available in “China Agriculture Information
Net” (http://www.agri.gov.cn/sjzl/ nongyety.htm). However,
there is no available data about arable land area of each year, so
the arable land area in 1996 could be approximately regarded as
the arable land area from 1996 to 2003 (also available in China
Agriculture Information Net). Based on the statistical data
above, the annual Cl of each province was calculated.
Generally, the remotely sensed cropping index shows high
accordance with statistical data at province scale (R 2 =0.9213,
/*<0.001, slope= 1.0775) (Figure.5), suggesting the reliability of
the proposed method.
statistical Cl
Figure. 5 Correlation of remotely sensed Cl and statistical Cl
4.2 Spatial Pattern of Cropping Index
The 22-year average Cl varied evidently among different
provinces (Table 1), with Hebei, Shandong, Henan, Jiangsu,
Anhui exhibiting high values, and Heilongjiang, inter-Mongolia,
Xinjiang, Gansu, Shanxi, Qinghai, Ningxia exhibiting relatively
lower values. As presented in Figure.6, the cropping index
shows an increasing trend from northeastern and northwestern
provinces (about 100) to southeastern ones (about 200). The
spatial distribution of cropping index extracted by the newly
proposed method was consistent with the actual Chinese
cropping system reported by Shen et al. (1983).
Province
Average cropping index from 1982 to
2003
Heilongjiang
88.99
inter-
73.61
Xinjiang
79.56
Jilin
90.43
Liaoning
91.88
Gansu
83.04
Hebei
123.34
Beijing
107.99
Shanxi
87.13
Tianjin
104.68
Qinghai
77.07
Shaanxi
95.32
Ningxia
73.78