为了促进贵州省农业和生态环境可持续发展,基于作物缺水指数(Crop Water Stress Index,CWSI),结合气象、植被指数等数据,采用Theil-Sen Median趋势分析、Mann-Kendall检验、变异系数和相关性分析等方法,对贵州省2000—2019年的干旱时空...为了促进贵州省农业和生态环境可持续发展,基于作物缺水指数(Crop Water Stress Index,CWSI),结合气象、植被指数等数据,采用Theil-Sen Median趋势分析、Mann-Kendall检验、变异系数和相关性分析等方法,对贵州省2000—2019年的干旱时空变化特征、趋势及影响因素进行了分析。研究表明:(1)贵州省CWSI多年均值为0.43,整体处于轻旱等级,空间分布为东南湿润,西北干旱,多年旱情变化呈缓解趋势;(2)从地貌类型来看,非喀斯特地貌CWSI多年均值为0.37,整体处于无旱等级,喀斯特地貌均值为0.47,处于轻旱状态;(3)从植被类型来看,除针叶林整体处于无旱状态外,其他植被类型都处于轻旱等级,且针叶林的变异系数(CV)值较其他林地高,说明其对气候因子的敏感性高,抗旱能力强;(4)贵州省CWSI与降水和气温均呈负相关,负相关面积占比为95%和54%,说明降水对CWSI的影响较大。综合分析得出,贵州省东南部湿润,西北地区干旱,全省干旱受喀斯特地貌、降水的影响较大。展开更多
Temperature vegetation dryness index （TVDI） and crop water stress index （CWSI） are two commonly used remote sens- ing-based agricultural drought indicators. This study explored the applicability of monthly moderat...Temperature vegetation dryness index （TVDI） and crop water stress index （CWSI） are two commonly used remote sens- ing-based agricultural drought indicators. This study explored the applicability of monthly moderate resolution imaging spectroradiometer （MODIS） normalized difference vegetation index （NDVI） and land surface temperature （LST） data for agricultural drought monitoring in the Guanzhong Plain, China in 2003. The data were processed using TVDI, calculated by parameterizing the relationship between the MODIS NDVI and LST data. We compared the effectiveness of TVDI against CWSI, derived from the MOD16 products, for drought monitoring. In addition, the surface soil moisture and monthly pre- cipitation were collected and used for verification of the results. Results from the study showed that： （1） drought conditions measured by TVDI and CWSI had a number of similarities, which indicated that both CWSI and TVDI can be used for drought monitoring, although they had some discrepancies in the spatiotemporal characteristics of drought intensity of this region; and （2） both standardized precipitation index （SPI） and SM contents at the depth of 10 and 20 cm had better correlations to CWSI than to TVDI, indicating that there were more statistically significant relationships between CWSI and SPI/SM, and that CWSI is a more reliable indicator for assessing and monitoring droughts in this region.展开更多
基于MOD16数据,计算作物缺水指数(Crop Water Stress Index,CWSI),结合汾河流域气象站点数据、植被指数数据和土地利用数据,采用差值法、线性趋势法和相关分析法,分析汾河流域2000—2021年干旱时空变化特征。结果表明:(1)CWSI能有效监...基于MOD16数据,计算作物缺水指数(Crop Water Stress Index,CWSI),结合汾河流域气象站点数据、植被指数数据和土地利用数据,采用差值法、线性趋势法和相关分析法,分析汾河流域2000—2021年干旱时空变化特征。结果表明:(1)CWSI能有效监测汾河流域旱情,其与10 cm土壤相对湿度呈显著负相关。(2)汾河流域CWSI空间分布差异明显,呈现出南湿北旱的特点。(3)汾河流域CWSI年际变化较为平稳,而月变化波动较大,5月CWSI达到年内峰值。(4)汾河流域不同生长期内干旱情况差异显著,生长季前期(4—5月)特旱区占汾河流域总面积的48.55%;生长季中期(6—8月)基本全域无旱;生长季后期(9—10月),仅11.17%的地区发生干旱。(5)不同土地利用类型干旱程度不同,CWSI从小到大依次为:林地(0.686)<草地(0.749)<耕地(0.751)<未利用地(0.758)<城镇(0.765)。本研究结果可为汾河流域旱情监测和抗旱决策的制定提供科学数据支撑。展开更多
Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture.Utilizing unmanned aerial vehicle(UAV)remote sensing,we explored the applicability of an empirical cr...Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture.Utilizing unmanned aerial vehicle(UAV)remote sensing,we explored the applicability of an empirical crop water stress index(CWSI)based on canopy temperature and three-dimensional drought indices(TDDI)constructed from surface temperature(T_(s)),air temperature(T_(a))and five vegetation indices(VIs)for monitoring the moisture status of dryland crops.Three machine learning algorithms(random forest regression(RFR),support vector regression,and partial least squares regression)were used to compare the performance of the drought indices for vegetation moisture content(VMC)estimation in sorghum and maize.The main results of the study were as follows:(1)Comparative analysis of the drought indices revealed that T_(s)-T_(a)-normalized difference vegetation index(TDDIn)and T_(s)-T_(a)-enhanced vegetation index(TDDIe)were more strongly correlated with VMC compared with the other indices.The indices exhibited varying sensitivities to VMC under different irrigation regimes;the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment(r=-0.93).(2)Regarding spatial and temporal characteristics,the TDDIn,TDDIe and CWSI indices showed minimal differences Over the experimental period,with coefficients of variation were 0.25,0.18 and 0.24,respectively.All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops,but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event.(3)For prediction of the moisture content of single crops,RFR models based on TDDIn and TDDIe estimated VMC most accurately(R^(2)>0.7),and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples,with R^(2)and RMSE of 0.62 and 14.26%,respectively.Thus,TDDI proved more effective than the CWSI in estimating crop water content.展开更多
文摘为了促进贵州省农业和生态环境可持续发展,基于作物缺水指数(Crop Water Stress Index,CWSI),结合气象、植被指数等数据,采用Theil-Sen Median趋势分析、Mann-Kendall检验、变异系数和相关性分析等方法,对贵州省2000—2019年的干旱时空变化特征、趋势及影响因素进行了分析。研究表明:(1)贵州省CWSI多年均值为0.43,整体处于轻旱等级,空间分布为东南湿润,西北干旱,多年旱情变化呈缓解趋势;(2)从地貌类型来看,非喀斯特地貌CWSI多年均值为0.37,整体处于无旱等级,喀斯特地貌均值为0.47,处于轻旱状态;(3)从植被类型来看,除针叶林整体处于无旱状态外,其他植被类型都处于轻旱等级,且针叶林的变异系数(CV)值较其他林地高,说明其对气候因子的敏感性高,抗旱能力强;(4)贵州省CWSI与降水和气温均呈负相关,负相关面积占比为95%和54%,说明降水对CWSI的影响较大。综合分析得出,贵州省东南部湿润,西北地区干旱,全省干旱受喀斯特地貌、降水的影响较大。
基金support of the National Natural Science Foundation of China (41171310)
文摘Temperature vegetation dryness index （TVDI） and crop water stress index （CWSI） are two commonly used remote sens- ing-based agricultural drought indicators. This study explored the applicability of monthly moderate resolution imaging spectroradiometer （MODIS） normalized difference vegetation index （NDVI） and land surface temperature （LST） data for agricultural drought monitoring in the Guanzhong Plain, China in 2003. The data were processed using TVDI, calculated by parameterizing the relationship between the MODIS NDVI and LST data. We compared the effectiveness of TVDI against CWSI, derived from the MOD16 products, for drought monitoring. In addition, the surface soil moisture and monthly pre- cipitation were collected and used for verification of the results. Results from the study showed that： （1） drought conditions measured by TVDI and CWSI had a number of similarities, which indicated that both CWSI and TVDI can be used for drought monitoring, although they had some discrepancies in the spatiotemporal characteristics of drought intensity of this region; and （2） both standardized precipitation index （SPI） and SM contents at the depth of 10 and 20 cm had better correlations to CWSI than to TVDI, indicating that there were more statistically significant relationships between CWSI and SPI/SM, and that CWSI is a more reliable indicator for assessing and monitoring droughts in this region.
文摘基于MOD16数据,计算作物缺水指数(Crop Water Stress Index,CWSI),结合汾河流域气象站点数据、植被指数数据和土地利用数据,采用差值法、线性趋势法和相关分析法,分析汾河流域2000—2021年干旱时空变化特征。结果表明:(1)CWSI能有效监测汾河流域旱情,其与10 cm土壤相对湿度呈显著负相关。(2)汾河流域CWSI空间分布差异明显,呈现出南湿北旱的特点。(3)汾河流域CWSI年际变化较为平稳,而月变化波动较大,5月CWSI达到年内峰值。(4)汾河流域不同生长期内干旱情况差异显著,生长季前期(4—5月)特旱区占汾河流域总面积的48.55%;生长季中期(6—8月)基本全域无旱;生长季后期(9—10月),仅11.17%的地区发生干旱。(5)不同土地利用类型干旱程度不同,CWSI从小到大依次为:林地(0.686)<草地(0.749)<耕地(0.751)<未利用地(0.758)<城镇(0.765)。本研究结果可为汾河流域旱情监测和抗旱决策的制定提供科学数据支撑。
基金supported by the National Key Research and Development Program of China(2022YFD1901500/2022YFD1901505)the Key Laboratory of Molecular Breeding for Grain and Oil Crops in Guizhou Province,China(Qiankehezhongyindi(2023)008)the Key Laboratory of Functional Agriculture of Guizhou Provincial Higher Education Institutions,China(Qianjiaoji(2023)007)。
文摘Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture.Utilizing unmanned aerial vehicle(UAV)remote sensing,we explored the applicability of an empirical crop water stress index(CWSI)based on canopy temperature and three-dimensional drought indices(TDDI)constructed from surface temperature(T_(s)),air temperature(T_(a))and five vegetation indices(VIs)for monitoring the moisture status of dryland crops.Three machine learning algorithms(random forest regression(RFR),support vector regression,and partial least squares regression)were used to compare the performance of the drought indices for vegetation moisture content(VMC)estimation in sorghum and maize.The main results of the study were as follows:(1)Comparative analysis of the drought indices revealed that T_(s)-T_(a)-normalized difference vegetation index(TDDIn)and T_(s)-T_(a)-enhanced vegetation index(TDDIe)were more strongly correlated with VMC compared with the other indices.The indices exhibited varying sensitivities to VMC under different irrigation regimes;the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment(r=-0.93).(2)Regarding spatial and temporal characteristics,the TDDIn,TDDIe and CWSI indices showed minimal differences Over the experimental period,with coefficients of variation were 0.25,0.18 and 0.24,respectively.All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops,but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event.(3)For prediction of the moisture content of single crops,RFR models based on TDDIn and TDDIe estimated VMC most accurately(R^(2)>0.7),and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples,with R^(2)and RMSE of 0.62 and 14.26%,respectively.Thus,TDDI proved more effective than the CWSI in estimating crop water content.
