Based on the data from MOD17A2H GPP,28 meteorological stations,and agricultural statistical
yearbook from 2001 to 2012,and the methods of MK trend testing and regression analysis,etc.,this paper ana-
lyzed the relationships among the gross primary productivity (GPP),potential productivity and actual grain yield,
and their spatial-temporal distribution characteristics in the Jing Rever Basin. The results showed that: (1) The
GPP in the Jing River Basin had an increasing trend with an average value of 519.6 g·m
-2
·a
-1
. The spatial pat-
tern of GPP was generally consistent with the distribution of precipitation,and it gradually increased from the north-
west to the southeast. The northern part of the basin was a low-value area of GPP,the mountainous forest lands and
river valleys were high-value areas,and Changwu,Zhengning,Xunyi,and Binxian in the southern part were the
secondary high-value areas. (2) Spatially,the potential productivity of light and temperature was increasing from
the west to the east in the Jing River Basin,with a“saddle-shaped”distribution in the north-south direction,ran-
ging from 16 462~21 786 kg·hm
-2
; and the climatic potential productivity was increasing from the northwest tothe southeast with a range of 4 945~12 412 kg·hm
-2
. From 2001 to 2012,there were no significant trends of in-
terannual changes in the two potential productivity. (3) The crop grain yield in the Jing River Basin increased spa-
tially from the northwest to the southeast,and showed an increasing trend during the study period. The differences
between crop climatic potential productivity and its grain yield were above 6 000 kg·hm
-2
in most areas of the Jing
River Basin. (4) The spatial correlation coefficients between the crop grain yield,the climatic potential productivi-
ty,and GPP reached very significant level (P<0.01),respectively; and the temporal correlation coefficient be-
tween crop grain yield and GPP reached a significant level (P<0.05). The very significant correlation between the
crop yield and MODIS-GPP demonstrated the availability of estimating grain yield through remote sensing,which is
helpful to the prediction of regional grain yield.
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