Rapid and accurate access to large-scale,high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development.Due to the limitations of remote sensing image qua...Rapid and accurate access to large-scale,high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development.Due to the limitations of remote sensing image quality and data processing capabilities,large-scale crop classification is still challenging.This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine(GEE)and Sentinel-1 and Sentinel-2 images.We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018(May to September),combined monthly composite images of reflectance bands,vegetation indices and polarization bands as input features,and then performed crop classification using a Random Forest(RF)classifier.The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area,and the overall accuracy(OA)reached 89.75%.Through experiments,we also found that the classification performance using time-series images is significantly better than that using single-period images.Compared with the use of traditional bands only(i.e.,the visible and near-infrared bands),the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly,followed by the addition of red-edge bands.Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%,respectively,compared to using only the Sentinel-2 reflectance bands.The analysis of timeliness revealed that when the July image is available,the increase in the accuracy of crop classification is the highest.When the Sentinel-1 and Sentinel-2 images for May,June,and July are available,an OA greater than 80%can be achieved.The results of this study are applicable to large-scale,high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas.展开更多
This paper introduces ENVISAT ASAR data application on rice field mapping in the Fuzhou area, using multi-temporal ASAR dual polarization data acquired in 2005. The procedure for ASAR data processing here includes dat...This paper introduces ENVISAT ASAR data application on rice field mapping in the Fuzhou area, using multi-temporal ASAR dual polarization data acquired in 2005. The procedure for ASAR data processing here includes data calibration, image registration, speckle reduction and conversion of data format from amplitude to dB for backscatter. The backscatter of rice increases with the rice growing stages, which was much different from other land covers. Based on image difference techniques, 6 schemes were designed with ASAR different temporal and polarization data for rice field mapping. Difference images between images in the early period of rice crop and growing or ripening period, are more suitable for rice extraction than those difference images between different polarizations in the same date. The most accurate result of late rice extraction was achieved based on the difference of HH polarization data acquired in October and August. Therefore, for rice field mapping, the temporal information is more important than polarization information. The data during the early growing season of rice is very important for high accuracy rice mapping.展开更多
The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Be...The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei＇an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest （RF） algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can im- prove the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.展开更多
Agricultural monitoring is essential for adequate management of food production and distribution.Crop land and crop type classification,using remote sensing time series,form an important tool to capture the agricultur...Agricultural monitoring is essential for adequate management of food production and distribution.Crop land and crop type classification,using remote sensing time series,form an important tool to capture the agricultural production information.The recently launched Sentinel-2 satellites provide unprecedented monitoring capacities in terms of spatial resolution,swath width,and revisit frequency.The Sentinel-2 for Agriculture(Sen2-Agri)system has been developed to fully exploit those capacities,by providing four relevant earth observation products for agricultural monitoring.Under the Dragon 4 Program,the crop mapping with various satellite images and a specific focus on the Yellow River irrigated agricultural area in the Ningxia Hui Autonomous Region in China was carried out with the Sentinel-2 for Agriculture system(Sent2Agri).9 types of crops were classified and the crop type map in 2017 was produced based on 35 scenes Sentinel 2A/B images.The overall accuracy computed from the error confusion matrix is 88%,which includes the cropped and uncropped types.After the removal of the uncropped area,the overall accuracy for a cropped decrease to 73%.In order to further improve the crop classification accuracy,the training dataset should be further improved and tuned.展开更多
To analyze the efficiency of area estimations(i.e.estimation accuracy and variation of estimation)impacted by crop mapping error,we simulated error at eight levels for thematic maps using a stratified sampling estimat...To analyze the efficiency of area estimations(i.e.estimation accuracy and variation of estimation)impacted by crop mapping error,we simulated error at eight levels for thematic maps using a stratified sampling estimation methodology.The results show that the estimation efficiency is influenced by the combination of the sample size and the error level.Evaluating the trade-offs between sample size and error level showed that reducing the crop mapping error level provides the most benefit(i.e.higher estimation efficiency).Further,sampling performance differed based on the heterogeneity of the crop area.The results demonstrated that the influence of increasing the error level on estimation efficiency is more detrimental in heterogeneous areas than in homogeneous ones.Therefore,to obtain higher estimation efficiency,a larger sample size and lower error level or both are needed,especially in heterogeneous areas.We suggest that existing land-cover maps should first be used to determine the heterogeneity of the area.The appropriate sample size for these areas then can be determined according to all three factors:heterogeneity,expected estimation efficiency,and sampling budget.Overall,extending our understanding of the impacts of crop mapping error is necessary for decision making to improve our ability to effectively estimate crop area.展开更多
基金funded by the National Key R&D Program of China(2017YFD0201803)the Talent Recruitment Project of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences.
文摘Rapid and accurate access to large-scale,high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development.Due to the limitations of remote sensing image quality and data processing capabilities,large-scale crop classification is still challenging.This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine(GEE)and Sentinel-1 and Sentinel-2 images.We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018(May to September),combined monthly composite images of reflectance bands,vegetation indices and polarization bands as input features,and then performed crop classification using a Random Forest(RF)classifier.The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area,and the overall accuracy(OA)reached 89.75%.Through experiments,we also found that the classification performance using time-series images is significantly better than that using single-period images.Compared with the use of traditional bands only(i.e.,the visible and near-infrared bands),the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly,followed by the addition of red-edge bands.Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%,respectively,compared to using only the Sentinel-2 reflectance bands.The analysis of timeliness revealed that when the July image is available,the increase in the accuracy of crop classification is the highest.When the Sentinel-1 and Sentinel-2 images for May,June,and July are available,an OA greater than 80%can be achieved.The results of this study are applicable to large-scale,high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas.
基金Supported by the Fujian Science and Technology Project(No.2006I0018,No.2009I0014)
文摘This paper introduces ENVISAT ASAR data application on rice field mapping in the Fuzhou area, using multi-temporal ASAR dual polarization data acquired in 2005. The procedure for ASAR data processing here includes data calibration, image registration, speckle reduction and conversion of data format from amplitude to dB for backscatter. The backscatter of rice increases with the rice growing stages, which was much different from other land covers. Based on image difference techniques, 6 schemes were designed with ASAR different temporal and polarization data for rice field mapping. Difference images between images in the early period of rice crop and growing or ripening period, are more suitable for rice extraction than those difference images between different polarizations in the same date. The most accurate result of late rice extraction was achieved based on the difference of HH polarization data acquired in October and August. Therefore, for rice field mapping, the temporal information is more important than polarization information. The data during the early growing season of rice is very important for high accuracy rice mapping.
基金financially supported by the Opening Foundation of the Key Laboratory of Agricultural Information Technology,Ministry of Agriculture,China (2014009)the Non-Profit Research Grant of the National Administration of Surveying,Mapping and Geoinformation of China (201512028)+1 种基金the National Natural Science Fo undation of China (41271112)the Youth Foundation of Heilongjiang Academy of Agricultural Science,China (QN024)
文摘The successful launched Gaofen satellite no. 1 wide field-of-view （GF-1 WFV） camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei＇an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest （RF） algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can im- prove the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.
基金Natural Science Foundation project(No.41271429)FP7 Project(SIGMA)ESA Project(Dragon 4 and S2A)。
文摘Agricultural monitoring is essential for adequate management of food production and distribution.Crop land and crop type classification,using remote sensing time series,form an important tool to capture the agricultural production information.The recently launched Sentinel-2 satellites provide unprecedented monitoring capacities in terms of spatial resolution,swath width,and revisit frequency.The Sentinel-2 for Agriculture(Sen2-Agri)system has been developed to fully exploit those capacities,by providing four relevant earth observation products for agricultural monitoring.Under the Dragon 4 Program,the crop mapping with various satellite images and a specific focus on the Yellow River irrigated agricultural area in the Ningxia Hui Autonomous Region in China was carried out with the Sentinel-2 for Agriculture system(Sent2Agri).9 types of crops were classified and the crop type map in 2017 was produced based on 35 scenes Sentinel 2A/B images.The overall accuracy computed from the error confusion matrix is 88%,which includes the cropped and uncropped types.After the removal of the uncropped area,the overall accuracy for a cropped decrease to 73%.In order to further improve the crop classification accuracy,the training dataset should be further improved and tuned.
基金the Major Project of High-Resolution Earth Observation System,China[grant number 09-20A05-9001-17/18]the New Hampshire Agricultural Experiment Station.This is Scientific Contribution Number 2728the USDA National Institute of Food and Agriculture McIntire Stennis Project#NH00077-M(Accession#1002519)。
文摘To analyze the efficiency of area estimations(i.e.estimation accuracy and variation of estimation)impacted by crop mapping error,we simulated error at eight levels for thematic maps using a stratified sampling estimation methodology.The results show that the estimation efficiency is influenced by the combination of the sample size and the error level.Evaluating the trade-offs between sample size and error level showed that reducing the crop mapping error level provides the most benefit(i.e.higher estimation efficiency).Further,sampling performance differed based on the heterogeneity of the crop area.The results demonstrated that the influence of increasing the error level on estimation efficiency is more detrimental in heterogeneous areas than in homogeneous ones.Therefore,to obtain higher estimation efficiency,a larger sample size and lower error level or both are needed,especially in heterogeneous areas.We suggest that existing land-cover maps should first be used to determine the heterogeneity of the area.The appropriate sample size for these areas then can be determined according to all three factors:heterogeneity,expected estimation efficiency,and sampling budget.Overall,extending our understanding of the impacts of crop mapping error is necessary for decision making to improve our ability to effectively estimate crop area.
