The sown area of winter wheat in the Huang-Huai-Hai（HHH） Plain accounts for over 65% of the total sown area of winter wheat in China. Thus, it is important to monitor the winter wheat growth condition and reveal the...The sown area of winter wheat in the Huang-Huai-Hai（HHH） Plain accounts for over 65% of the total sown area of winter wheat in China. Thus, it is important to monitor the winter wheat growth condition and reveal the main factors that influence its dynamics. This study assessed the winter wheat growth condition based on remote sensing data, and investigated the correlations between different grades of winter wheat growth and major meteorological factors corresponding. First, winter wheat growth condition from sowing until maturity stage during 2011–2012 were assessed based on moderate-resolution imaging spectroradiometer（MODIS） normalized difference vegetation index（NDVI） time-series dataset. Next, correlation analysis and geographical information system（GIS） spatial analysis methods were used to analyze the lag correlations between different grades of winter wheat growth in each phenophase and the meteorological factors that corresponded to the phenophases. The results showed that the winter wheat growth conditions varied over time and space in the study area. Irrespective of the grades of winter wheat growth, the correlation coefficients between the winter wheat growth condition and the cumulative precipitation were higher than zero lag（synchronous precipitation） and one lag（pre-phenophase precipitation） based on the average values of seven phenophases. This showed that the cumulative precipitation during the entire growing season had a greater effect on winter wheat growth than the synchronous precipitation and the pre-phenophase precipitation. The effects of temperature on winter wheat growth varied according to different grades of winter wheat growth based on the average values of seven phenophases. Winter wheat with a better-than-average growth condition had a stronger correlation with synchronous temperature, winter wheat with a normal growth condition had a stronger correlation with the cumulative temperature, and winter wheat with a worse-than-average growth condition had a stronger correlation with the pre-phenophase temperature. This study may facilitate a better understanding of the quantitative correlations between different grades of crop growth and meteorological factors, and the adjustment of field management measures to ensure a high crop yield.展开更多
Crop consumptive water use is recognized as a key element to understand regional water management performance. This study documents an attempt to apply a regional evapotranspiration model（SEBAL） and crop information...Crop consumptive water use is recognized as a key element to understand regional water management performance. This study documents an attempt to apply a regional evapotranspiration model（SEBAL） and crop information for assessment of regional crop（summer maize and winter wheat） actual evapotranspiration（ET a） in Huang-Huai-Hai（3H） Plain, China. The average seasonal ET a of summer maize and winter wheat were 354.8 and 521.5 mm respectively in 3H Plain. A high-ET a belt of summer maize occurs in piedmont plain, while a low ET a area was found in the hill-irrigable land and dry land area. For winter wheat, a high-ET a area was located in the middle part of 3H Plain, including low plain-hydropenia irrigable land and dry land, hill-irrigable land and dry land, and basin-irrigable land and dry land. Spatial analysis demonstrated a linear relationship between crop ET a, normalized difference vegetation index（NDVI）, and the land surface temperature（LST）. A stronger relationship between ET a and NDVI was found in the metaphase and last phase than other crop growing phase, as indicated by higher correlation coefficient values. Additionally, higher correlation coefficients were detected between ET a and LST than that between ET a and NDVI, and this significant relationship ran through the entire crop growing season. ET a in the summer maize growing season showed a significant relationship with longitude, while ET a in the winter wheat growing season showed a significant relationship with latitude. The results of this study will serve as baseline information for water resources management of 3H Plain.展开更多
北部冬麦区是我国重要的小麦主产区之一,对该麦区历年国审小麦品种进行回溯分析有助于小麦品种资源的合理利用。本研究基于产量与熟期、穗数、穗粒数、千粒重、容重、品质指数、抗病指数和抗寒指数等性状的组合,采用品种-产量×性...北部冬麦区是我国重要的小麦主产区之一,对该麦区历年国审小麦品种进行回溯分析有助于小麦品种资源的合理利用。本研究基于产量与熟期、穗数、穗粒数、千粒重、容重、品质指数、抗病指数和抗寒指数等性状的组合,采用品种-产量×性状组合(GYT,genotype by yield×trait)双标图方法对2003-2023年期间北部冬麦区47个国审小麦品种进行了综合分析和分类评价。结果表明,47个国审小麦品种可划分为4个特征显著的品种类型。其中,Ⅰ型品种综合表现优秀,在产量与早熟性、抗病性、抗寒性、千粒重和容重等性状组合上表现突出,在产量与穗数、穗粒数和品质指数组合上表现优良,在生产上推广应用价值最高,主要包括京麦179、京农16和津麦3118等8个品种。Ⅱ型品种综合表现优良,在产量与品质指数、穗数组合上表现突出,在产量与抗病指数、抗寒指数组合上表现稍差,在生产上推广应用价值较高,但应注意生产安全,主要包括京麦202、京农19和轮选158等13个品种。Ⅲ型品种的产量与抗病和抗寒指数组合最好,但在其余性状组合上表现差,综合生产应用价值有限,可作为抗性亲本。Ⅳ型品种综合表现较差,可选择单性状表现优良的品种作为育种亲本应用。根据各品种在GYT双标图ATA轴上的投影位置,筛选出综合表现优良的京麦179、京农16、津麦3118、京麦189、京麦202、京花12号、京农19、轮选158和中麦623等品种。本研究采用GYT双标图分析方法基于“产量-性状”组合水平对北部冬麦区小麦品种进行综合评价和分类研究,为其他作物和地区的类似研究提供了参考。展开更多
To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing classification.Google ...To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing classification.Google Earth Engine(GEE),a cloud computing analysis platform based on global geospatial analysis,provides a new opportunity for rapid analysis of remote sensing data.In this study,high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in China.The random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020,and Sentinel-2 imagery was used to verify the results.The spectral indices,texture,and terrain features of the image were derived,and their contribution to the classification accuracy of winter wheat was evaluated by scoring.Then the top nine features were selected to form an optimal feature subset.Comparing the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier,the results show that the accuracy difference between the two feature classification schemes is small,but the classification effect of all feature sets is slightly better than the optimal feature subset.The overall classification accuracy of sample plots verification was 86%-95%,the Kappa coefficient was between 0.7 and 0.85,and the percentage error of the total area was 5.42%.The research demonstrates a reliable method for mapping a wide range of winter wheat planting area,and provides a good prospect for exploring the precise mapping of other crops,which is of great significance to crop monitoring and agricultural development.展开更多
基金financially supported by the National Nonprofit Institute Research Grant of Chinese Academy of Agricultural Sciences(IARRP-2015-8)the European Union seventh framework"MODEXTREME"(modelling vegetation response to extreme events)programme(613817)
文摘The sown area of winter wheat in the Huang-Huai-Hai（HHH） Plain accounts for over 65% of the total sown area of winter wheat in China. Thus, it is important to monitor the winter wheat growth condition and reveal the main factors that influence its dynamics. This study assessed the winter wheat growth condition based on remote sensing data, and investigated the correlations between different grades of winter wheat growth and major meteorological factors corresponding. First, winter wheat growth condition from sowing until maturity stage during 2011–2012 were assessed based on moderate-resolution imaging spectroradiometer（MODIS） normalized difference vegetation index（NDVI） time-series dataset. Next, correlation analysis and geographical information system（GIS） spatial analysis methods were used to analyze the lag correlations between different grades of winter wheat growth in each phenophase and the meteorological factors that corresponded to the phenophases. The results showed that the winter wheat growth conditions varied over time and space in the study area. Irrespective of the grades of winter wheat growth, the correlation coefficients between the winter wheat growth condition and the cumulative precipitation were higher than zero lag（synchronous precipitation） and one lag（pre-phenophase precipitation） based on the average values of seven phenophases. This showed that the cumulative precipitation during the entire growing season had a greater effect on winter wheat growth than the synchronous precipitation and the pre-phenophase precipitation. The effects of temperature on winter wheat growth varied according to different grades of winter wheat growth based on the average values of seven phenophases. Winter wheat with a better-than-average growth condition had a stronger correlation with synchronous temperature, winter wheat with a normal growth condition had a stronger correlation with the cumulative temperature, and winter wheat with a worse-than-average growth condition had a stronger correlation with the pre-phenophase temperature. This study may facilitate a better understanding of the quantitative correlations between different grades of crop growth and meteorological factors, and the adjustment of field management measures to ensure a high crop yield.
基金supported by the National Key Technologies R&D Program of China during the 12th Five-Year Plan period (2012BAD09B01)the National Basic Research Program of China (973 Program, 2012CB955904)the National Science Foundation for Young Scientists of China (41401510)
文摘Crop consumptive water use is recognized as a key element to understand regional water management performance. This study documents an attempt to apply a regional evapotranspiration model（SEBAL） and crop information for assessment of regional crop（summer maize and winter wheat） actual evapotranspiration（ET a） in Huang-Huai-Hai（3H） Plain, China. The average seasonal ET a of summer maize and winter wheat were 354.8 and 521.5 mm respectively in 3H Plain. A high-ET a belt of summer maize occurs in piedmont plain, while a low ET a area was found in the hill-irrigable land and dry land area. For winter wheat, a high-ET a area was located in the middle part of 3H Plain, including low plain-hydropenia irrigable land and dry land, hill-irrigable land and dry land, and basin-irrigable land and dry land. Spatial analysis demonstrated a linear relationship between crop ET a, normalized difference vegetation index（NDVI）, and the land surface temperature（LST）. A stronger relationship between ET a and NDVI was found in the metaphase and last phase than other crop growing phase, as indicated by higher correlation coefficient values. Additionally, higher correlation coefficients were detected between ET a and LST than that between ET a and NDVI, and this significant relationship ran through the entire crop growing season. ET a in the summer maize growing season showed a significant relationship with longitude, while ET a in the winter wheat growing season showed a significant relationship with latitude. The results of this study will serve as baseline information for water resources management of 3H Plain.
文摘北部冬麦区是我国重要的小麦主产区之一,对该麦区历年国审小麦品种进行回溯分析有助于小麦品种资源的合理利用。本研究基于产量与熟期、穗数、穗粒数、千粒重、容重、品质指数、抗病指数和抗寒指数等性状的组合,采用品种-产量×性状组合(GYT,genotype by yield×trait)双标图方法对2003-2023年期间北部冬麦区47个国审小麦品种进行了综合分析和分类评价。结果表明,47个国审小麦品种可划分为4个特征显著的品种类型。其中,Ⅰ型品种综合表现优秀,在产量与早熟性、抗病性、抗寒性、千粒重和容重等性状组合上表现突出,在产量与穗数、穗粒数和品质指数组合上表现优良,在生产上推广应用价值最高,主要包括京麦179、京农16和津麦3118等8个品种。Ⅱ型品种综合表现优良,在产量与品质指数、穗数组合上表现突出,在产量与抗病指数、抗寒指数组合上表现稍差,在生产上推广应用价值较高,但应注意生产安全,主要包括京麦202、京农19和轮选158等13个品种。Ⅲ型品种的产量与抗病和抗寒指数组合最好,但在其余性状组合上表现差,综合生产应用价值有限,可作为抗性亲本。Ⅳ型品种综合表现较差,可选择单性状表现优良的品种作为育种亲本应用。根据各品种在GYT双标图ATA轴上的投影位置,筛选出综合表现优良的京麦179、京农16、津麦3118、京麦189、京麦202、京花12号、京农19、轮选158和中麦623等品种。本研究采用GYT双标图分析方法基于“产量-性状”组合水平对北部冬麦区小麦品种进行综合评价和分类研究,为其他作物和地区的类似研究提供了参考。
基金the National Key Research and Development Project of China(Grant No.2018YFC1507802)The Outstanding Young Talents Program in Colleges and Universities in Anhui Province(Grant No.GXYQ2020001)+1 种基金Anhui Provincial Major Science and Technology Projects(Grant No.18030701209)the National Natural Science Foundation of China(Grant No.41871352)。
文摘To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing classification.Google Earth Engine(GEE),a cloud computing analysis platform based on global geospatial analysis,provides a new opportunity for rapid analysis of remote sensing data.In this study,high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in China.The random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020,and Sentinel-2 imagery was used to verify the results.The spectral indices,texture,and terrain features of the image were derived,and their contribution to the classification accuracy of winter wheat was evaluated by scoring.Then the top nine features were selected to form an optimal feature subset.Comparing the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier,the results show that the accuracy difference between the two feature classification schemes is small,but the classification effect of all feature sets is slightly better than the optimal feature subset.The overall classification accuracy of sample plots verification was 86%-95%,the Kappa coefficient was between 0.7 and 0.85,and the percentage error of the total area was 5.42%.The research demonstrates a reliable method for mapping a wide range of winter wheat planting area,and provides a good prospect for exploring the precise mapping of other crops,which is of great significance to crop monitoring and agricultural development.
