Combined with remote sensing data and meteorological data, cold damage risk was assessed for planting area of double cropping rice (DCR) in Hunan Province, China. A new methodology of cold damage risk assessment was b...Combined with remote sensing data and meteorological data, cold damage risk was assessed for planting area of double cropping rice (DCR) in Hunan Province, China. A new methodology of cold damage risk assessment was built that apply to grid and have clear hazard-affected body. Each station cold damage annual frequency and average annual intensity of cold damage was calculated by using 1951-2010 station daily mean temperature and simple cold damage identification index. On this basis, average annual cold damage risk index was obtained by their product. The spatial analysis models of cold damage risk index about double-season early rice (DSER) and double-season later rice (DSLR) were established respectively by the relation of average annual cold damage risk index and its geographic factors. Critical threshold of level of average annual cold damage risk index for DSER and DSLR were respectively divided by the correlative equation of cold damage annual frequency and average annual intensity of cold damage. 2001-2010 planting area of DCR, acquired by time series analysis of MOD09A1 8-d composite land surface reflectance product, was as target of assessment. The results show average annual intensity of cold damage is exponential function of cold damage annual frequency, average annual cold damage risk index is directly proportional to cold damage cumulant and cold damage annual frequency, and is inversely proportional to happen times of cold damage and the square of statistical time sequence length. Cold damage risk of DSER is higher than DSLR in Hunan Province. In the 10-yr stacking map, DCR planting in low risk area accounted for 11.92% of total extraction area, in moderate risk area accounted for 69.62%, in high risk area accounted for 18.46%. According to the cold damage risk assessment result, DCR production can be guided to reduce cold damage losses.展开更多
The radial basis function (RBF) emerged as a variant of artificial neural network. Generalized regression neural network (GRNN) is one type of RBF, and its principal advantages are that it can quickly learn and rapidl...The radial basis function (RBF) emerged as a variant of artificial neural network. Generalized regression neural network (GRNN) is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reffectance (350 to 2500 nm) data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density (45 plants m-2). Stepwise multivariable regression model (SMR) and RBF were used to compare their predictability for the leaf area index (LAI) and green leaf chlorophyll density (GLCD) of rice based on reffectance (R) and its three different transformations, the first derivative reffectance (D1), the second derivative reffectance (D2) and the log-transformed re?ectance (LOG). GRNN based on D1 was the best model for the prediction of rice LAI and GLCD. The relationships between different transformations of reffectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.展开更多
The normalized difference vegetation index (NDVI) has proven to be typically employed to assess terrestrial vegetation conditions. However, one limitation of NDVI for drought monitoring is the apparent time lag betwee...The normalized difference vegetation index (NDVI) has proven to be typically employed to assess terrestrial vegetation conditions. However, one limitation of NDVI for drought monitoring is the apparent time lag between rainfall deficit and NDVI response. To better understand this relationship, time series NDVI (2000-2010) during the growing season in Sichuan Province and Chongqing City were analyzed. The vegetation condition index (VCI) was used to construct a new drought index, time-integrated vegetation condition index (TIVCI), and was then compared with meteorological drought indices-standardized precipitation index (SPI), a multiple-time scale meteorological-drought index based on precipitation, to examine the sensitivity on droughts. Our research findings indicate the followings: (1) farmland NDVI sensitivity to precipitation in study area has a time lag of 16-24 d, and it maximally responds to the temperature with a lag of about 16 d. (2) We applied the approach to Sichuan Province and Chongqing City for extreme drought monitoring in 2006 and 2003, and the results show that the monitoring results from TIVCI are closer to the published China agricultural statistical data than VCI. Compared to VCI, the best results from TIVCI3 were found with the relative errors of -4.5 and 6.36% in 2006 for drought affected area and drought disaster area respectively, and 5.11 and -5.95% in 2003. (3) Compared to VCI, TIVCI has better correlation with the SPI, which indicates the lag and cumulative effects of precipitation on vegetation. Our finding proved that TIVCI is an effective indicator of drought detection when the time lag effects between NDVI and climate factors are taken into consideration.展开更多
基金funded by the Key Technologies R&D Program of China during the 12th Five-Year Plan period(2011BAD32B01)the National Natural Science Foundation of China(40875070)the Research Fund for Doctoral Program of Higher Education,China(20100101110035)
文摘Combined with remote sensing data and meteorological data, cold damage risk was assessed for planting area of double cropping rice (DCR) in Hunan Province, China. A new methodology of cold damage risk assessment was built that apply to grid and have clear hazard-affected body. Each station cold damage annual frequency and average annual intensity of cold damage was calculated by using 1951-2010 station daily mean temperature and simple cold damage identification index. On this basis, average annual cold damage risk index was obtained by their product. The spatial analysis models of cold damage risk index about double-season early rice (DSER) and double-season later rice (DSLR) were established respectively by the relation of average annual cold damage risk index and its geographic factors. Critical threshold of level of average annual cold damage risk index for DSER and DSLR were respectively divided by the correlative equation of cold damage annual frequency and average annual intensity of cold damage. 2001-2010 planting area of DCR, acquired by time series analysis of MOD09A1 8-d composite land surface reflectance product, was as target of assessment. The results show average annual intensity of cold damage is exponential function of cold damage annual frequency, average annual cold damage risk index is directly proportional to cold damage cumulant and cold damage annual frequency, and is inversely proportional to happen times of cold damage and the square of statistical time sequence length. Cold damage risk of DSER is higher than DSLR in Hunan Province. In the 10-yr stacking map, DCR planting in low risk area accounted for 11.92% of total extraction area, in moderate risk area accounted for 69.62%, in high risk area accounted for 18.46%. According to the cold damage risk assessment result, DCR production can be guided to reduce cold damage losses.
基金Project supported by the National Natural Science Foundation of China (No.40571115)the National High Tech-nology Research and Development Program (863 Program) of China (Nos.2006AA120101 and 2007AA10Z205)
文摘The radial basis function (RBF) emerged as a variant of artificial neural network. Generalized regression neural network (GRNN) is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reffectance (350 to 2500 nm) data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density (45 plants m-2). Stepwise multivariable regression model (SMR) and RBF were used to compare their predictability for the leaf area index (LAI) and green leaf chlorophyll density (GLCD) of rice based on reffectance (R) and its three different transformations, the first derivative reffectance (D1), the second derivative reffectance (D2) and the log-transformed re?ectance (LOG). GRNN based on D1 was the best model for the prediction of rice LAI and GLCD. The relationships between different transformations of reffectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.
基金supported by the National Key Technologies R&D Program of China (2011BAD32B01)the Ph D Programs Foundation of Ministry of Education of China (20100101110035)
文摘The normalized difference vegetation index (NDVI) has proven to be typically employed to assess terrestrial vegetation conditions. However, one limitation of NDVI for drought monitoring is the apparent time lag between rainfall deficit and NDVI response. To better understand this relationship, time series NDVI (2000-2010) during the growing season in Sichuan Province and Chongqing City were analyzed. The vegetation condition index (VCI) was used to construct a new drought index, time-integrated vegetation condition index (TIVCI), and was then compared with meteorological drought indices-standardized precipitation index (SPI), a multiple-time scale meteorological-drought index based on precipitation, to examine the sensitivity on droughts. Our research findings indicate the followings: (1) farmland NDVI sensitivity to precipitation in study area has a time lag of 16-24 d, and it maximally responds to the temperature with a lag of about 16 d. (2) We applied the approach to Sichuan Province and Chongqing City for extreme drought monitoring in 2006 and 2003, and the results show that the monitoring results from TIVCI are closer to the published China agricultural statistical data than VCI. Compared to VCI, the best results from TIVCI3 were found with the relative errors of -4.5 and 6.36% in 2006 for drought affected area and drought disaster area respectively, and 5.11 and -5.95% in 2003. (3) Compared to VCI, TIVCI has better correlation with the SPI, which indicates the lag and cumulative effects of precipitation on vegetation. Our finding proved that TIVCI is an effective indicator of drought detection when the time lag effects between NDVI and climate factors are taken into consideration.
