Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical st...Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricu展开更多
As illustrated by the case of Xuyi County, Jinhu County and Hongze County in Jiangsu Province, China, monitoring and forecasting of rice production were carried out by using HJ-1A satellite remote sensing images. The ...As illustrated by the case of Xuyi County, Jinhu County and Hongze County in Jiangsu Province, China, monitoring and forecasting of rice production were carried out by using HJ-1A satellite remote sensing images. The handhold GPS machines were used to measure the geographical position and some other information of these samples such as area shape. The GPS data and the interpretation marks were used to correct H J-1 image, assist human-computer interactive interpretation, and other operations. The test data had been participated in the whole classification process. The accuracy of interpreted information on rice planting area was more than 90% By using the leaf area index from the normalized difference vegetation index inversion, the biomass from the ratio vegetation index inversion, and combined with the rice yield estimation model, the rice yield was estimated. Further, the thematic map of rice production classification was made based on the rice yield data. According to the comparison results between measured and fitted values of yields and areas of sampling sites, the accuracy of the yield estimation was more than 85%. The results suggest that HJ-A/B images could basically meet the demand of rice growth monitoring and yield forecasting, and could be widely applied to rice production monitoring.展开更多
Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security.While the data-driven deep learn...Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security.While the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown.In this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in 2012.We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012.Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.展开更多
In current critical area models, it is generally assumed the defect outlines are circular and the conductors to be rectangle or the merger of rectangles. However, real defects and conductors associated with optimal la...In current critical area models, it is generally assumed the defect outlines are circular and the conductors to be rectangle or the merger of rectangles. However, real defects and conductors associated with optimal layout design exhibit a great variety of shapes. Based on mathematical morphology, a new critical area model is presented, which can be used to estimate the critical area of short circuit, open circuit and pinhole. Based on the new model, the efficient validity check algorithms are explored to extract critical areas of short circuit, open circuit and pinhole from layouts. The results of experiment on an approximate layout of 4 × 4 shifts register show that the new model predicts the critical areas accurately. These results suggest that the proposed model and algorithm could provide new approaches for yield prediction.展开更多
In the existing models of estimating the yield and critical area, the defect outline is usually assumed to be circular, but the observed real defect outlines are irregular in shape. In this paper, estimation of the yi...In the existing models of estimating the yield and critical area, the defect outline is usually assumed to be circular, but the observed real defect outlines are irregular in shape. In this paper, estimation of the yield and critical area is made using the Monte Carlo technique and the relationship between the errors of yield estimated by circular defect and the rectangle degree of the defect is analysed. The rectangular model of a real defect is presented, and the yield model is provided correspondingly. The models take into account an outline similar to that of an original defect, the characteristics of two-dimensional distribution of defects, the feature of a layout routing, and the character of yield estimation. In order to make the models practicable, the critical area computations related to rectangular defect and regular （vertical or horizontal） routing are discussed. The critical areas associated with rectangular defect and non- regular routing are developed also, based on the mathematical morphology. The experimental results show that the new yield model may predict the yield caused by real defects more accurately than the circular model. It is significant that the yield is accurately estimated using the proposed model for IC metals.展开更多
为进一步促进机器学习技术在玉米单产估测中的应用,以河北中部平原为研究区域,选取与玉米长势和产量密切相关的条件植被温度指数(Vegetation temperature condition index,VTCI)和叶面积指数(Leaf area index,LAI)为特征变量,通过极限...为进一步促进机器学习技术在玉米单产估测中的应用,以河北中部平原为研究区域,选取与玉米长势和产量密切相关的条件植被温度指数(Vegetation temperature condition index,VTCI)和叶面积指数(Leaf area index,LAI)为特征变量,通过极限梯度提升(Extreme gradient boosting,XGBoost)算法和随机森林(Random forest,RF)算法分别对玉米单产进行估测。基于组合预测思想与Shapley值理论,分别确定组合预测模型中XGBoost与RF模型权重,进而得到组合预测模型,结果表明,基于Shapley值确定的组合估产模型精度较高(R^(2)=0.32),达极显著水平(P<0.001)。同时将组合预测模型应用于河北中部平原2012年各县(区)玉米的单产估测,结果表明,模型精度较高(R^(2)=0.52),玉米估测单产与实际单产的平均相对误差和均方根误差分别为9.86%、831.14 kg/km^(2),达到极显著水平(P<0.001),且组合预测模型的精度均优于单一估测模型。研究发现,河北中部平原玉米估测单产随年份发生波动变化,呈先降低后升高的趋势。玉米估测单产以西部地区最高,其次是北部和南部地区,东部地区最低。展开更多
The yield monitors use a constant delay time to match the grain flow with location.Therefore,mixing and smoothing effects on the grain flow are neglected.Although constant time delay compensates for time mismatch,actu...The yield monitors use a constant delay time to match the grain flow with location.Therefore,mixing and smoothing effects on the grain flow are neglected.Although constant time delay compensates for time mismatch,actual grain flow at a combine harvester head is not equal to the grain flow measured by a sensor due to the dynamics effects.In order to eliminate the dynamics effects,a new method for estimating actual grain flow,called proportional distribution(PD),is proposed.This method assumes that actual grain flow is directly proportional to the feedrate.Based on this assumption,the actual grain flow results from redistributing accumulated grain mass over a certain time according to the profile of the feedrate.The PD can avoid the dynamics effects because the feedrate is measured at a combine harvester’s head.Compared with constant time delay,the proposed method can effectively estimate actual grain flow and be applied to improve the accuracy of yield maps.展开更多
基金supported in part by the National Natural Science Foundation of China (61661136006 and 41371396)
文摘Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricu
文摘As illustrated by the case of Xuyi County, Jinhu County and Hongze County in Jiangsu Province, China, monitoring and forecasting of rice production were carried out by using HJ-1A satellite remote sensing images. The handhold GPS machines were used to measure the geographical position and some other information of these samples such as area shape. The GPS data and the interpretation marks were used to correct H J-1 image, assist human-computer interactive interpretation, and other operations. The test data had been participated in the whole classification process. The accuracy of interpreted information on rice planting area was more than 90% By using the leaf area index from the normalized difference vegetation index inversion, the biomass from the ratio vegetation index inversion, and combined with the rice yield estimation model, the rice yield was estimated. Further, the thematic map of rice production classification was made based on the rice yield data. According to the comparison results between measured and fitted values of yields and areas of sampling sites, the accuracy of the yield estimation was more than 85%. The results suggest that HJ-A/B images could basically meet the demand of rice growth monitoring and yield forecasting, and could be widely applied to rice production monitoring.
基金the National Natural Science Foundation of China(32071894)and Zhejiang UniversityX.Wang acknowledges support from the National Natural Science Foundation of China(42171096).
文摘Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security.While the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown.In this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in 2012.We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012.Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.
基金National Basic Research Program of China (2009CB825101)Bingtuan Doctor Foundation (2010JC02)+2 种基金National Natural Science Foundation (110140101, 31060062)Shihezi University Program (No.Q9yy200814 No. SM05010)
文摘In current critical area models, it is generally assumed the defect outlines are circular and the conductors to be rectangle or the merger of rectangles. However, real defects and conductors associated with optimal layout design exhibit a great variety of shapes. Based on mathematical morphology, a new critical area model is presented, which can be used to estimate the critical area of short circuit, open circuit and pinhole. Based on the new model, the efficient validity check algorithms are explored to extract critical areas of short circuit, open circuit and pinhole from layouts. The results of experiment on an approximate layout of 4 × 4 shifts register show that the new model predicts the critical areas accurately. These results suggest that the proposed model and algorithm could provide new approaches for yield prediction.
文摘In the existing models of estimating the yield and critical area, the defect outline is usually assumed to be circular, but the observed real defect outlines are irregular in shape. In this paper, estimation of the yield and critical area is made using the Monte Carlo technique and the relationship between the errors of yield estimated by circular defect and the rectangle degree of the defect is analysed. The rectangular model of a real defect is presented, and the yield model is provided correspondingly. The models take into account an outline similar to that of an original defect, the characteristics of two-dimensional distribution of defects, the feature of a layout routing, and the character of yield estimation. In order to make the models practicable, the critical area computations related to rectangular defect and regular （vertical or horizontal） routing are discussed. The critical areas associated with rectangular defect and non- regular routing are developed also, based on the mathematical morphology. The experimental results show that the new yield model may predict the yield caused by real defects more accurately than the circular model. It is significant that the yield is accurately estimated using the proposed model for IC metals.
文摘为进一步促进机器学习技术在玉米单产估测中的应用,以河北中部平原为研究区域,选取与玉米长势和产量密切相关的条件植被温度指数(Vegetation temperature condition index,VTCI)和叶面积指数(Leaf area index,LAI)为特征变量,通过极限梯度提升(Extreme gradient boosting,XGBoost)算法和随机森林(Random forest,RF)算法分别对玉米单产进行估测。基于组合预测思想与Shapley值理论,分别确定组合预测模型中XGBoost与RF模型权重,进而得到组合预测模型,结果表明,基于Shapley值确定的组合估产模型精度较高(R^(2)=0.32),达极显著水平(P<0.001)。同时将组合预测模型应用于河北中部平原2012年各县(区)玉米的单产估测,结果表明,模型精度较高(R^(2)=0.52),玉米估测单产与实际单产的平均相对误差和均方根误差分别为9.86%、831.14 kg/km^(2),达到极显著水平(P<0.001),且组合预测模型的精度均优于单一估测模型。研究发现,河北中部平原玉米估测单产随年份发生波动变化,呈先降低后升高的趋势。玉米估测单产以西部地区最高,其次是北部和南部地区,东部地区最低。
基金supported by Nature Science Foundation of Liaoning Province,China(No.2015020128)。
文摘The yield monitors use a constant delay time to match the grain flow with location.Therefore,mixing and smoothing effects on the grain flow are neglected.Although constant time delay compensates for time mismatch,actual grain flow at a combine harvester head is not equal to the grain flow measured by a sensor due to the dynamics effects.In order to eliminate the dynamics effects,a new method for estimating actual grain flow,called proportional distribution(PD),is proposed.This method assumes that actual grain flow is directly proportional to the feedrate.Based on this assumption,the actual grain flow results from redistributing accumulated grain mass over a certain time according to the profile of the feedrate.The PD can avoid the dynamics effects because the feedrate is measured at a combine harvester’s head.Compared with constant time delay,the proposed method can effectively estimate actual grain flow and be applied to improve the accuracy of yield maps.
