Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote s...Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely used.However,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures.This study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study region.The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)algorithm.The best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms.The methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping patterns.The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county level.These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.展开更多
Understanding the spatial and temporal variations of cropping systems is very important for agricultural policymaking and food security assessment, and can provide a basis for national policies regarding cropping syst...Understanding the spatial and temporal variations of cropping systems is very important for agricultural policymaking and food security assessment, and can provide a basis for national policies regarding cropping systems adjustment and agricultural adaptation to climate change. With rapid development of society and the economy, China＇s cropping structure has profoundly changed since the reform and opening up in 1978, but there has been no systematic investigation of the pattern, process and characteristics of these changes. In view of this, a crop area database for China was acquired and compiled at the county level for the period 1980-2011, and linear regression and spatial analysis were employed to investigate the cropping structure type and cropping proportion changes at the national level. This research had three main findings： （1） China＇s cropping structure has undergone significant changes since 2002; the richness of cropping structure types has increased significantly and a diversified-type structure has gradually replaced the single types. The single-crop types--dominated by rice, wheat or maize--declined, affected by the combination of these three major food crops in mixed plantings and conversion of some of their planting area to other crops. （2） In the top 10 types, 82.7% of the county-level cropping structure was rice, wheat, maize and their combinations in 1980; however, this proportion decreased to 50.7% in 2011, indicating an adjustment period of China＇s cropping structure. Spatial analysis showed that 63.8% of China＇s counties adjusted their cropping structure, with the general change toward reducing the main food types and increasing fruits and vegetables during 1980-2011. （3） At the national level, the grain-planting pattern dominated by rice shifted to coexistence of rice, wheat and maize during this period. There were significant decreasing trends for 47% of rice, 61% of wheat and 29.6% of maize cropping counties. The pattern of maize cropping had the most significant change, with the maize proportion decreasing in the zone from north- eastern to southwestern China during this period. Cities and their surroundings were hotspots for cropping structural adjustment. Urbanization has significantly changed cropping structure, with most of these regions showing rapid increases in the proportion of fruit and vegetables.Our research suggests that the policy of cropping structural adjustment needs to consider geographical characteristics and spatial planning of cropping systems. In this way, the future direction of cropping structural adjustment wilt be appropriate and scientifically based, such as where there is a need to maintain or increase rice and wheat cropping, increase soybean and decrease maize, and increase the supply of fruit and vegetables.展开更多
LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and ...LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and in precision agriculture.The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field-often informed by data collected during ground and aerial surveys.However,manual digitizing and labeling is time-consuming,expensive and subject to human error.Automated remote sensing methods is a cost-effective alternative,with machine learning gaining popularity for classifying crop types.This study evaluated the use of LiDAR data,Sentinel-2 imagery,aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area.Different combinations of the three datasets were evaluated along with ten machine learning.The classification results were interpreted by comparing overall accuracies,kappa,standard deviation and f-score.It was found that LiDAR data successfully differentiated between different crop types,with XGBoost providing the highest overall accuracy of 87.8%.Furthermore,the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data,with LiDAR obtaining a mean overall accuracy of 84.3%and Sentinel-2 a mean overall accuracy of 83.6%.However,the combination of all three datasets proved to be the most effective at differentiating between the crop types,with RF providing the highest overall accuracy of 94.4%.These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.展开更多
Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes.Much spatial information and spectral signatures of hyperspectral images(HSIs)present greater pot...Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes.Much spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine crops.The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural domain.It is significant for the prediction and growth monitoring of crop yields.Amongst the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature extraction.This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm onHSI.The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI.To accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality.In addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature extraction.For hyperparameter tuning of the dilated CNN model,the HMAO algorithm is utilized.Eventually,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm.Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.展开更多
[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hu...[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hulunbeier area, according to field measured spectrum data, the optimum time of main crops, barley, wheat, rapeseed, based on crops spectrum characteristics, by dint of decision-making tree method, and considering spectral matching method, classification of crops was studied such as SAM. [Result] By dint of Landsat TM image gained in the first half of August, based on geographic and atmospheric proof-reading, decision-making tree was constructed. Plantation information about wheat, barley, and rapeseed and plantation grassland was extracted successfully. The general classification accuracy reached 86.90%. Kappa coefficient was 0.831 1. [Conclusion] Taking typical spectrum image as data source, and applying Decision Tree Method to get crops type＇s information had fine application future.展开更多
Development of a quantitative understanding of soil organic carbon （SOC） dynamics is vital for management of soil to sequester carbon （C） and maintain fertility, thereby contributing to food security and climate c...Development of a quantitative understanding of soil organic carbon （SOC） dynamics is vital for management of soil to sequester carbon （C） and maintain fertility, thereby contributing to food security and climate change mitigation. There are well-established process-based models that can be used to simulate SOC stock evolution; however, there are few plant residue C input values and those that exist represent a limited range of environments. This limitation in a fundamental model component （i.e., C input） constrains the reliability of current SOC stock simulations. This study aimed to estimate crop-specific and environment-specific plant-derived soil C input values for agricultural sites in France based on data from 700 sites selected from a recently established French soil monitoring network （the RMQS database）. Measured SOC stock values from this large scale soil database were used to constrain an inverse RothC modelling approach to derive estimated C input values consistent with the stocks. This approach allowed us to estimate significant crop-specific C input values （P 〈 0.05） for 14 out of 17 crop types in the range from 1.84 =h 0.69 t C ha-1 year-1 （silage corn） to 5.15 =k 0.12 t C ha-1 year-1 （grassland/pasture）. Furthermore, the incorporation of climate variables improved the predictions. C input of 4 crop types could be predicted as a function of temperature and 8 as a function of precipitation. This study offered an approach to meet the urgent need for crop-specific and environment-specific C input values in order to improve the reliability of SOC stock prediction.展开更多
Crop type data are an important piece of information for many applications in agriculture.Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limite...Crop type data are an important piece of information for many applications in agriculture.Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions.In this research,we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device(CCD)data.We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a longterm time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution.To increase accuracy,four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images.These phenological metrics were used to further identify each of the crop types with less,but easier to access,ancillary field survey data.We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment.The results show that our classification accuracy was 92%when compared with the highly accurate but limited ZY-3 images and matched up to 80%to the statistical crop areas.展开更多
文摘Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely used.However,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures.This study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study region.The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)algorithm.The best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms.The methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping patterns.The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county level.These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.
基金National Natural Science Foundation of China,No.41571172Ministry of Finance of China through the Non-Profit National Research Institute,No.2017-CAAS-30
文摘Understanding the spatial and temporal variations of cropping systems is very important for agricultural policymaking and food security assessment, and can provide a basis for national policies regarding cropping systems adjustment and agricultural adaptation to climate change. With rapid development of society and the economy, China＇s cropping structure has profoundly changed since the reform and opening up in 1978, but there has been no systematic investigation of the pattern, process and characteristics of these changes. In view of this, a crop area database for China was acquired and compiled at the county level for the period 1980-2011, and linear regression and spatial analysis were employed to investigate the cropping structure type and cropping proportion changes at the national level. This research had three main findings： （1） China＇s cropping structure has undergone significant changes since 2002; the richness of cropping structure types has increased significantly and a diversified-type structure has gradually replaced the single types. The single-crop types--dominated by rice, wheat or maize--declined, affected by the combination of these three major food crops in mixed plantings and conversion of some of their planting area to other crops. （2） In the top 10 types, 82.7% of the county-level cropping structure was rice, wheat, maize and their combinations in 1980; however, this proportion decreased to 50.7% in 2011, indicating an adjustment period of China＇s cropping structure. Spatial analysis showed that 63.8% of China＇s counties adjusted their cropping structure, with the general change toward reducing the main food types and increasing fruits and vegetables during 1980-2011. （3） At the national level, the grain-planting pattern dominated by rice shifted to coexistence of rice, wheat and maize during this period. There were significant decreasing trends for 47% of rice, 61% of wheat and 29.6% of maize cropping counties. The pattern of maize cropping had the most significant change, with the maize proportion decreasing in the zone from north- eastern to southwestern China during this period. Cities and their surroundings were hotspots for cropping structural adjustment. Urbanization has significantly changed cropping structure, with most of these regions showing rapid increases in the proportion of fruit and vegetables.Our research suggests that the policy of cropping structural adjustment needs to consider geographical characteristics and spatial planning of cropping systems. In this way, the future direction of cropping structural adjustment wilt be appropriate and scientifically based, such as where there is a need to maintain or increase rice and wheat cropping, increase soybean and decrease maize, and increase the supply of fruit and vegetables.
基金This work forms part of a larger project titled“Salt Accumulation and Waterlogging Monitoring System(SAWMS)Development”which was initiated and funded by the Water Research Commission(WRC)of South Africa(contract number K5/2558//4)More information about this project is available in WRC Report No TT 782/18,titled SALT ACCUMULATION AND WATERLOGGING MONITORING SYSTEM(SAWMS)DEVELOPMENT(ISBN 978-0-6392-0084-2)+1 种基金available at www.wrc.org.za.This work was also supported by the National Research Foundation(grant number 112300)The authors would also like to thank www.linguafix.net for their language editing services.
文摘LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and in precision agriculture.The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field-often informed by data collected during ground and aerial surveys.However,manual digitizing and labeling is time-consuming,expensive and subject to human error.Automated remote sensing methods is a cost-effective alternative,with machine learning gaining popularity for classifying crop types.This study evaluated the use of LiDAR data,Sentinel-2 imagery,aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area.Different combinations of the three datasets were evaluated along with ten machine learning.The classification results were interpreted by comparing overall accuracies,kappa,standard deviation and f-score.It was found that LiDAR data successfully differentiated between different crop types,with XGBoost providing the highest overall accuracy of 87.8%.Furthermore,the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data,with LiDAR obtaining a mean overall accuracy of 84.3%and Sentinel-2 a mean overall accuracy of 83.6%.However,the combination of all three datasets proved to be the most effective at differentiating between the crop types,with RF providing the highest overall accuracy of 94.4%.These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.
基金This work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R384)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes.Much spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine crops.The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural domain.It is significant for the prediction and growth monitoring of crop yields.Amongst the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature extraction.This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm onHSI.The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI.To accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality.In addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature extraction.For hyperparameter tuning of the dilated CNN model,the HMAO algorithm is utilized.Eventually,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm.Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.
基金Supported by the Open Subject of Key Lab of Resources Remote-sensing and Digital Agriculture in Agricultural Department(RDA1008)~~
文摘[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hulunbeier area, according to field measured spectrum data, the optimum time of main crops, barley, wheat, rapeseed, based on crops spectrum characteristics, by dint of decision-making tree method, and considering spectral matching method, classification of crops was studied such as SAM. [Result] By dint of Landsat TM image gained in the first half of August, based on geographic and atmospheric proof-reading, decision-making tree was constructed. Plantation information about wheat, barley, and rapeseed and plantation grassland was extracted successfully. The general classification accuracy reached 86.90%. Kappa coefficient was 0.831 1. [Conclusion] Taking typical spectrum image as data source, and applying Decision Tree Method to get crops type＇s information had fine application future.
基金Supported by the Soil Scientific Interest Group (GIS Sol) of Francefinanced by the "Groupement d'Intrêt Scientifique Sol". Jeroen Meersmans' postdoctoral position was funded by the French Environment and Energy Management Agency (ADEME)funded by the EU projects "Greenhouse gas management in European land use systems (GHG-Europe)" (FP7-ENV-2009-1-244122) and "CARBO-Extreme" (FP7-ENV-2008-1-226701)
文摘Development of a quantitative understanding of soil organic carbon （SOC） dynamics is vital for management of soil to sequester carbon （C） and maintain fertility, thereby contributing to food security and climate change mitigation. There are well-established process-based models that can be used to simulate SOC stock evolution; however, there are few plant residue C input values and those that exist represent a limited range of environments. This limitation in a fundamental model component （i.e., C input） constrains the reliability of current SOC stock simulations. This study aimed to estimate crop-specific and environment-specific plant-derived soil C input values for agricultural sites in France based on data from 700 sites selected from a recently established French soil monitoring network （the RMQS database）. Measured SOC stock values from this large scale soil database were used to constrain an inverse RothC modelling approach to derive estimated C input values consistent with the stocks. This approach allowed us to estimate significant crop-specific C input values （P 〈 0.05） for 14 out of 17 crop types in the range from 1.84 =h 0.69 t C ha-1 year-1 （silage corn） to 5.15 =k 0.12 t C ha-1 year-1 （grassland/pasture）. Furthermore, the incorporation of climate variables improved the predictions. C input of 4 crop types could be predicted as a function of temperature and 8 as a function of precipitation. This study offered an approach to meet the urgent need for crop-specific and environment-specific C input values in order to improve the reliability of SOC stock prediction.
基金the Key Program of National Natural Science Foundation of China[grant numbers 51339004 and 51209163].
文摘Crop type data are an important piece of information for many applications in agriculture.Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions.In this research,we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device(CCD)data.We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a longterm time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution.To increase accuracy,four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images.These phenological metrics were used to further identify each of the crop types with less,but easier to access,ancillary field survey data.We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment.The results show that our classification accuracy was 92%when compared with the highly accurate but limited ZY-3 images and matched up to 80%to the statistical crop areas.
