Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while r...Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.展开更多
Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land managem...Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land management,agricultural activities,water quality,and sustainable development.The remote sensing images taken by the synthetic aperture radar(SAR)Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area;however,there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization.Therefore,in this study,we used topography indices derived from digital elevation model(DEM),SAR indices generated by Sentinel-1,and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China,and evaluated the potential of multi-source sensors to predict soil salinity.Using the soil electrical conductivity(EC)values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable,we constructed three soil salinity inversion models based on classification and regression tree(CART),random forest(RF),and extreme gradient boosting(XGBoost).Then,we evaluated the prediction ability of different models through the five-fold cross validation.The prediction accuracy of XGBoost model is better than those of CART and RF,and soil salinity predicted by the three models has similar spatial distribution characteristics.Compared with the combination of topography indices and vegetation indices,the addition of SAR indices effectively improves the prediction accuracy of the model.In general,the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor.In addition,SAR indices,vegetation indices,and topography indices are all effective variables for soil salinity prediction.Weighted Difference Vegetation Index(WDVI)is designated as the most important variable in these variables,followed by DEM.The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.展开更多
Large-scale crop mapping using remote sensing data is of great significance for agricultural production, food security and the sustainable development of human societies. Winter rapeseed is an important oil crop in Ch...Large-scale crop mapping using remote sensing data is of great significance for agricultural production, food security and the sustainable development of human societies. Winter rapeseed is an important oil crop in China that is mainly distributed in the Yangtze River Valley. Traditional winter rapeseed mapping practices are insufficient since they only use the spectral characteristics during the critical phenological period of winter rapeseed, which are usually limited to a small region and cannot meet the needs of large-scale applications. In this study, a novel phenology-based winter rapeseed index(PWRI) was proposed to map winter rapeseed in the Yangtze River Valley. PWRI expands the date window for distinguishing winter rapeseed and winter wheat, and it has good separability throughout the flowering period of winter rapeseed. PWRI also improves the separability of winter rapeseed and winter wheat, which traditionally have been two easily confused winter crops. A PWRI-based method was applied to the Middle Reaches of the Yangtze River Valley to map winter rapeseed on the Google Earth Engine platform. Time series composited Sentinel-2 data were used to map winter rapeseed with 10 m resolution. The mapping achieved a good result with overall accuracy and kappa coefficients exceeding 92% and 0.85, respectively. The PWRI-based method provides a new solution for high spatial resolution winter rapeseed mapping at a large scale.展开更多
The small size of agricultural plots is the main difficulty for crops mapping with remote sensing data in the Sahelian region of Africa. The study aims to combine Sentinel-1 (radar) and Sentinel-2 (Optic) data to disc...The small size of agricultural plots is the main difficulty for crops mapping with remote sensing data in the Sahelian region of Africa. The study aims to combine Sentinel-1 (radar) and Sentinel-2 (Optic) data to discriminate millet, maize and peanut crops. Training plots were used in order to analyse temporal variation of the three crops’ signals. T<span style="font-family:Verdana;">he NDVI (Normalized Difference Vegetation Index) was able to differentiate crops only at the end of the rainy season (October). </span><span style="font-family:Verdana;">The optical data as well as the radar ones could not easily discriminate the three crops during the growing season, because in that period vegetation cover is low, and soil contribution to the signals (due to roughness and moisture) was more important than that of real vegetation. However, the ratio of VH/VV (VH: incident signal in vertical polarization and reflected signal in horizontal polarization;VV: incident signal in vertical polarization and reflected signal in horizontal polarization) gave a difference between millet and the two other crops at the beginning cultural season (July 11). Difference appears from the second third of September when the harvest of cereals crops (millet and maize) began. From middle of October, the peanut signal dropped sharply thus facilitating the differentiation of peanut from the two other crops. This analysis led to the identification of data that have could be used to discriminate these crops (useful data). Classification of the combined useful data gave an overall high accuracy of 82%, with 96%, 61% and 65% for peanut, maize and millet, respectively. The non-agricultural areas (water, natural vegetation, habit, bare soil) were well classified with an accuracy greater than 90%.</span>展开更多
Soil is the largest carbon pool on the Earth's surface.With the application of remote sensing technology,Soil Organic Carbon(SOC)estimation has become a hot topic in digital soil mapping.However,the heterogeneity ...Soil is the largest carbon pool on the Earth's surface.With the application of remote sensing technology,Soil Organic Carbon(SOC)estimation has become a hot topic in digital soil mapping.However,the heterogeneity of geomorphology can affect the performance of remote sensing in determining soil organic carbon.In the Bosten Lake Watershed in northwestern China,we collected 116 soil samples from farm land,uncultivated land,and woodland.To establish an SOC prediction model,we produced 16 optical remote sensing variables and 9 environmental covariates.Three types of land use were studied:farm land,uncultivated land,and woodland.Five machine learning models were used for these land use types:gradient Tree(ET),Support Vector Machine(SVM),Random Forest(RF),Adaptive gradient Boosting(AdaBoost),and extreme Gradient Boosting(XGBoost).The main driving variables for changes in organic carbon content across the entire sample area were Enhanced Vegetation Index(EVI),Enhanced Vegetation Index 2(EVI2),Soil-Adjusted Vegetation Index(SAVI);for farm land,it was Clay Index(CI2);for farm land and woodland,it was Color Index(CI).The results showed that in terms of prediction accuracy,RF and XGBoost outperformed SVM.In terms of simulation precision,the ET model's woodland model(R^(2)=0.86,RMSE=7.72),the ET model's farm land model(R^(2)=0.82,RMSE=6.66),and the uncultivated land model of the RF model(R^(2)=0.81,RMSE=1.09)performed best.Compared to global modeling,establishing SOC estimation models based on different land use types yielded more ideal results in this study.These findings provide new insights into high-precision estimation of organic carbon content.展开更多
Artifi cial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security.However,progress in this field is often limited by the lack of specialized,error-free lab...Artifi cial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security.However,progress in this field is often limited by the lack of specialized,error-free labeled datasets.This paper introduces DAClA5,a multispectral image dataset for agricultural crop identification,complemented with Sentinel-1 radar data.The dataset consists of 172 Sentinel-2 multispectral images(800×450 pixels)and 159 Sentinel-1 radar images,collected over Braov,Romania,from 2020 to 2024,with precise,in-situ verified labels.Additionally,6,454 Sentinel-2 and 5,995 Sentinel-1 rectangular patches(32 x 32 pixels)were extracted,exceeding 6 million pixels in total.The cropland parcels considered in our dataset are used for research and are owned and cultivated by the National Institute of Research and Development for Potato and Sugar Beet,ensuring error-free labeling.The labels in our dataset provide detailed information about crop types,offering insights into crop distribution,growth stages,and phenological events.Furthermore,we present a comprehensive dataset analysis and two key use cases:crop identifi cation based on a"past vs.present"approach and early crop identification during the agricultural season.展开更多
The red-edge bands and their derived vegetation indices play a crucial role in monitoring vegetation health.The Gaofen-6(GF-6)and Sentinel-2A satellites are equipped with two and three red-edge bands,respectively,thus...The red-edge bands and their derived vegetation indices play a crucial role in monitoring vegetation health.The Gaofen-6(GF-6)and Sentinel-2A satellites are equipped with two and three red-edge bands,respectively,thus making them invaluable for monit-oring forest health.To compare the performance of these two satellites’red-edge bands in monitoring forest health,this study selected forests in Liuyang City,Hunan Province and Tonggu County,Jiangxi Province and Hanzhong City,Shaanxi Province in China as study areas and used three commonly used red-edge indices and the Random Forest(RF)algorithm for the comparison.The three selected red-edge indices were the Normalized Difference Red-Edge Index 1(NDRE1),the Missouri emergency resource information system Ter-restrial Chlorophyll Index(MTCI),and the Inverted Red-Edge Chlorophyll Index(IRECI).Through training of sample regions,this study determined the spectral differences among three forest health levels and established classification criteria for these levels.The res-ults showed that GF-6 imagery provided higher accuracy in distinguishing forest health levels than Sentinel-2A,with an average accur-acy of 90.22%versus 76.55%.This difference is attributed to variations in the wavelengths used to construct the red-edge indices between GF-6 and Sentinel-2A.In the RF algorithm,this study employed three distinct band combinations for classification:all bands including red-edge bands,excluding red-edge bands,and only red-edge bands.The results indicated that GF-6 outperformed Sentinel-2A when using the first and second band combinations,yet slightly underperforming with the third.This outcome was closely associ-ated with the importance of each band’s contribution to classification accuracy reveled by the Gini importance score,their sensitivity in detecting forest health conditions,and the total number of bands employed in the classification process.Overall,the NDRE1 derived from GF-6 achieved the highest average accuracy(90.22%).This study provides a scientific basis for selecting appropriate remote sens-ing data and techniques for forest health monitoring,which is of significant importance for the future ecological protection of forests.展开更多
Soybean and maize are important raw materials for the production of food and livestock feed.Accurate mapping of these two crops is of great significance to crop management,yield estimation,and crop-damage control.In t...Soybean and maize are important raw materials for the production of food and livestock feed.Accurate mapping of these two crops is of great significance to crop management,yield estimation,and crop-damage control.In this study,two towns in Guoyang County,Anhui Province,China,were selected as the study area,and Sentinel-2 images were adopted to map the distributions of both crops in the 2019 growing season.The data obtained on August 18(early pod-setting stage of soybean)was determined to be the most applicable to soybean and maize mapping by means of the Jeffries-Matusita(JM)distance.Subsequently,three machine-learning algorithms,i.e.,random forest(RF),support vector machine(SVM)and back-propagation neural network(BPNN)were employed and their respective performance in crop identification was evaluated with the aid of 254 ground truth plots.It appeared that RF with a Kappa of 0.83 was superior to the other two methods.Furthermore,twenty candidate features containing the reflectance of ten spectral bands(spatial resolution at 10 m or 20 m)and ten remote-sensing indices were input into the RF algorithm to conduct an important assessment.Seven features were screened out and served as the optimum subset,the mapping results of which were assessed based on the ground truth derived from the unmanned aerial vehicle(UAV)images covering six ground samples.The optimum feature-subset achieved high-accuracy crop mapping,with a reduction of data volume by 65%compared with the total twenty features,which also overrode the performance of ten spectral bands.Therefore,feature-optimization had great potential in the identification of the two crops.Generally,the findings of this study can provide a valuable reference for mapping soybean and maize in areas with a fragmented landscape of farmland and complex planting structure.展开更多
基金supported by the National Natural Science Foundation of China(42271360 and 42271399)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(CAST)(2020QNRC001)the Fundamental Research Funds for the Central Universities,China(2662021JC013,CCNU22QN018)。
文摘Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.
基金This work was financially supported by the National Natural Science Foundation of China(41771470)the China Postdoctoral Science Foundation(2020M672776).
文摘Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land management,agricultural activities,water quality,and sustainable development.The remote sensing images taken by the synthetic aperture radar(SAR)Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area;however,there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization.Therefore,in this study,we used topography indices derived from digital elevation model(DEM),SAR indices generated by Sentinel-1,and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China,and evaluated the potential of multi-source sensors to predict soil salinity.Using the soil electrical conductivity(EC)values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable,we constructed three soil salinity inversion models based on classification and regression tree(CART),random forest(RF),and extreme gradient boosting(XGBoost).Then,we evaluated the prediction ability of different models through the five-fold cross validation.The prediction accuracy of XGBoost model is better than those of CART and RF,and soil salinity predicted by the three models has similar spatial distribution characteristics.Compared with the combination of topography indices and vegetation indices,the addition of SAR indices effectively improves the prediction accuracy of the model.In general,the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor.In addition,SAR indices,vegetation indices,and topography indices are all effective variables for soil salinity prediction.Weighted Difference Vegetation Index(WDVI)is designated as the most important variable in these variables,followed by DEM.The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.
基金supported by the National Natural Science Foundation of China (41971371)the National Key Research and Development Program of China (2022YFB3903504)the Fundamental Research Funds for the Central Universities,China (CCNU22JC022)。
文摘Large-scale crop mapping using remote sensing data is of great significance for agricultural production, food security and the sustainable development of human societies. Winter rapeseed is an important oil crop in China that is mainly distributed in the Yangtze River Valley. Traditional winter rapeseed mapping practices are insufficient since they only use the spectral characteristics during the critical phenological period of winter rapeseed, which are usually limited to a small region and cannot meet the needs of large-scale applications. In this study, a novel phenology-based winter rapeseed index(PWRI) was proposed to map winter rapeseed in the Yangtze River Valley. PWRI expands the date window for distinguishing winter rapeseed and winter wheat, and it has good separability throughout the flowering period of winter rapeseed. PWRI also improves the separability of winter rapeseed and winter wheat, which traditionally have been two easily confused winter crops. A PWRI-based method was applied to the Middle Reaches of the Yangtze River Valley to map winter rapeseed on the Google Earth Engine platform. Time series composited Sentinel-2 data were used to map winter rapeseed with 10 m resolution. The mapping achieved a good result with overall accuracy and kappa coefficients exceeding 92% and 0.85, respectively. The PWRI-based method provides a new solution for high spatial resolution winter rapeseed mapping at a large scale.
文摘The small size of agricultural plots is the main difficulty for crops mapping with remote sensing data in the Sahelian region of Africa. The study aims to combine Sentinel-1 (radar) and Sentinel-2 (Optic) data to discriminate millet, maize and peanut crops. Training plots were used in order to analyse temporal variation of the three crops’ signals. T<span style="font-family:Verdana;">he NDVI (Normalized Difference Vegetation Index) was able to differentiate crops only at the end of the rainy season (October). </span><span style="font-family:Verdana;">The optical data as well as the radar ones could not easily discriminate the three crops during the growing season, because in that period vegetation cover is low, and soil contribution to the signals (due to roughness and moisture) was more important than that of real vegetation. However, the ratio of VH/VV (VH: incident signal in vertical polarization and reflected signal in horizontal polarization;VV: incident signal in vertical polarization and reflected signal in horizontal polarization) gave a difference between millet and the two other crops at the beginning cultural season (July 11). Difference appears from the second third of September when the harvest of cereals crops (millet and maize) began. From middle of October, the peanut signal dropped sharply thus facilitating the differentiation of peanut from the two other crops. This analysis led to the identification of data that have could be used to discriminate these crops (useful data). Classification of the combined useful data gave an overall high accuracy of 82%, with 96%, 61% and 65% for peanut, maize and millet, respectively. The non-agricultural areas (water, natural vegetation, habit, bare soil) were well classified with an accuracy greater than 90%.</span>
基金funded by the Xinjiang Uygur Autonomous Region Natural Science Foundation Program(2022D01A214)the Tianchi Talent Introduction Programme(Young Doctor)+1 种基金the Project of 2024 Philosophy and Social Science Internal Cultivation(24FPY001)the Universities Basic Research Operating Expenses Scientific Research Projects of Xinjiang(XJEDU2023P019).
文摘Soil is the largest carbon pool on the Earth's surface.With the application of remote sensing technology,Soil Organic Carbon(SOC)estimation has become a hot topic in digital soil mapping.However,the heterogeneity of geomorphology can affect the performance of remote sensing in determining soil organic carbon.In the Bosten Lake Watershed in northwestern China,we collected 116 soil samples from farm land,uncultivated land,and woodland.To establish an SOC prediction model,we produced 16 optical remote sensing variables and 9 environmental covariates.Three types of land use were studied:farm land,uncultivated land,and woodland.Five machine learning models were used for these land use types:gradient Tree(ET),Support Vector Machine(SVM),Random Forest(RF),Adaptive gradient Boosting(AdaBoost),and extreme Gradient Boosting(XGBoost).The main driving variables for changes in organic carbon content across the entire sample area were Enhanced Vegetation Index(EVI),Enhanced Vegetation Index 2(EVI2),Soil-Adjusted Vegetation Index(SAVI);for farm land,it was Clay Index(CI2);for farm land and woodland,it was Color Index(CI).The results showed that in terms of prediction accuracy,RF and XGBoost outperformed SVM.In terms of simulation precision,the ET model's woodland model(R^(2)=0.86,RMSE=7.72),the ET model's farm land model(R^(2)=0.82,RMSE=6.66),and the uncultivated land model of the RF model(R^(2)=0.81,RMSE=1.09)performed best.Compared to global modeling,establishing SOC estimation models based on different land use types yielded more ideal results in this study.These findings provide new insights into high-precision estimation of organic carbon content.
基金Funded by the European UnionThe Al4AGRl project entitled"Romanian Excellence Center on Artificial Intelligence on Earth Observation Data for Agriculture"received funding from the European Union's Horizon Europe research and innovation program under grant agreement no.101079136Al4AGRl project received funding from the European Union's Horizon Europe research and innovation programme[101079136].
文摘Artifi cial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security.However,progress in this field is often limited by the lack of specialized,error-free labeled datasets.This paper introduces DAClA5,a multispectral image dataset for agricultural crop identification,complemented with Sentinel-1 radar data.The dataset consists of 172 Sentinel-2 multispectral images(800×450 pixels)and 159 Sentinel-1 radar images,collected over Braov,Romania,from 2020 to 2024,with precise,in-situ verified labels.Additionally,6,454 Sentinel-2 and 5,995 Sentinel-1 rectangular patches(32 x 32 pixels)were extracted,exceeding 6 million pixels in total.The cropland parcels considered in our dataset are used for research and are owned and cultivated by the National Institute of Research and Development for Potato and Sugar Beet,ensuring error-free labeling.The labels in our dataset provide detailed information about crop types,offering insights into crop distribution,growth stages,and phenological events.Furthermore,we present a comprehensive dataset analysis and two key use cases:crop identifi cation based on a"past vs.present"approach and early crop identification during the agricultural season.
文摘基于Sentinel-2 NDVI(normalized difference vegetation index)时间序列数据特征绘制了江汉平原2020—2024年的农作物种植强度空间分布并分析其变化特征。首先对NDVI数据进行了云掩膜和时序平滑等预处理获取完整的作物NDVI时序曲线;然后,提取曲线的波峰和波谷、作物的SOS(start of season)和EOS(end of season)等关键物候信息,在此基础上提出了一种波峰计数的种植强度提取方法,并通过野外调查数据进行了精度验证。研究结果表明,该方法能够有效绘制江汉平原单季作物、双季轮作和三季轮作的空间分布,5 a整体精度均超过85%。该方法为精准农业的发展提供了可靠的技术手段。
基金Under the auspices of National Natural Science Foundation of China(No.31971639)National Natural Science Foundation of Fujian Province,China(No.2023J01225)。
文摘The red-edge bands and their derived vegetation indices play a crucial role in monitoring vegetation health.The Gaofen-6(GF-6)and Sentinel-2A satellites are equipped with two and three red-edge bands,respectively,thus making them invaluable for monit-oring forest health.To compare the performance of these two satellites’red-edge bands in monitoring forest health,this study selected forests in Liuyang City,Hunan Province and Tonggu County,Jiangxi Province and Hanzhong City,Shaanxi Province in China as study areas and used three commonly used red-edge indices and the Random Forest(RF)algorithm for the comparison.The three selected red-edge indices were the Normalized Difference Red-Edge Index 1(NDRE1),the Missouri emergency resource information system Ter-restrial Chlorophyll Index(MTCI),and the Inverted Red-Edge Chlorophyll Index(IRECI).Through training of sample regions,this study determined the spectral differences among three forest health levels and established classification criteria for these levels.The res-ults showed that GF-6 imagery provided higher accuracy in distinguishing forest health levels than Sentinel-2A,with an average accur-acy of 90.22%versus 76.55%.This difference is attributed to variations in the wavelengths used to construct the red-edge indices between GF-6 and Sentinel-2A.In the RF algorithm,this study employed three distinct band combinations for classification:all bands including red-edge bands,excluding red-edge bands,and only red-edge bands.The results indicated that GF-6 outperformed Sentinel-2A when using the first and second band combinations,yet slightly underperforming with the third.This outcome was closely associ-ated with the importance of each band’s contribution to classification accuracy reveled by the Gini importance score,their sensitivity in detecting forest health conditions,and the total number of bands employed in the classification process.Overall,the NDRE1 derived from GF-6 achieved the highest average accuracy(90.22%).This study provides a scientific basis for selecting appropriate remote sens-ing data and techniques for forest health monitoring,which is of significant importance for the future ecological protection of forests.
基金This work was supported by the Key Project of Natural Science Research of Education Department of Anhui Province(Grant No.KJ2019A0120)the National Key Research and Development Program of China(2019YFE0115200)+2 种基金and the Open Research Fund of National Engineering Research Center for Agro-Ecological Big Data Analysis&Application(Grant No.AE2018011)The authors appreciate the European Space Agency(ESA)which provides Sentinel-2 images for this researchthe editor and anonymous reviewers for their valuable comments that helped us to improve this manuscript.
文摘Soybean and maize are important raw materials for the production of food and livestock feed.Accurate mapping of these two crops is of great significance to crop management,yield estimation,and crop-damage control.In this study,two towns in Guoyang County,Anhui Province,China,were selected as the study area,and Sentinel-2 images were adopted to map the distributions of both crops in the 2019 growing season.The data obtained on August 18(early pod-setting stage of soybean)was determined to be the most applicable to soybean and maize mapping by means of the Jeffries-Matusita(JM)distance.Subsequently,three machine-learning algorithms,i.e.,random forest(RF),support vector machine(SVM)and back-propagation neural network(BPNN)were employed and their respective performance in crop identification was evaluated with the aid of 254 ground truth plots.It appeared that RF with a Kappa of 0.83 was superior to the other two methods.Furthermore,twenty candidate features containing the reflectance of ten spectral bands(spatial resolution at 10 m or 20 m)and ten remote-sensing indices were input into the RF algorithm to conduct an important assessment.Seven features were screened out and served as the optimum subset,the mapping results of which were assessed based on the ground truth derived from the unmanned aerial vehicle(UAV)images covering six ground samples.The optimum feature-subset achieved high-accuracy crop mapping,with a reduction of data volume by 65%compared with the total twenty features,which also overrode the performance of ten spectral bands.Therefore,feature-optimization had great potential in the identification of the two crops.Generally,the findings of this study can provide a valuable reference for mapping soybean and maize in areas with a fragmented landscape of farmland and complex planting structure.
