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
Paddy rice agriculture is practiced in both rain-fed and irrigated ecosystems in the Philippines.However,small farms are prevalent in the region,and current satellite-based mapping techniques do not distinguish betwee...Paddy rice agriculture is practiced in both rain-fed and irrigated ecosystems in the Philippines.However,small farms are prevalent in the region,and current satellite-based mapping techniques do not distinguish between the two ecosystems at farm scales.This study developed an approach to rapidly map irrigated and rain-fed paddy rice in Iloilo,Philippines at 10 m resolutions using Google Earth Engine.This approach used an ensemble of classifiers based on time-series vegetation indices to produce dry and wet seasonal maps for the entire province.Results showed a predominance of rain-fed rice areas in both seasons,with irrigated rice making up only onefourth of the total rice area.The overall accuracy was achieved at 68%for the dry season and 75%for the wet season based on ground-acquired points and very high-resolution imagery.The two types of paddies were classified at accuracies up to 87%.Furthermore,the land cover maps showed a strong agreement with the municipal statistics.The resultant maps complement current official statistics and demonstrate the prowess of phenology-based mapping to create paddy inventories in a timely manner to inform food security and agricultural policies.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
Accurate forest cover maps are the basis for estimating forest biomass and are crucial for climate regulation and biodiversity conservation,especially in sub-humid and semi-arid regions such as Oklahoma,USA.To date,th...Accurate forest cover maps are the basis for estimating forest biomass and are crucial for climate regulation and biodiversity conservation,especially in sub-humid and semi-arid regions such as Oklahoma,USA.To date,there is very limited data and knowledge of the spatial pattern and temporal dynamics of forest cover in Oklahoma,and current forest cover maps have large uncertainties.In this study,multi-sensor datasets,including the Phased Arrayed L-band Synthetic Aperture Radar(PALSAR-2),Landsat,and spaceborne Light Detection and Ranging(LiDAR),were combined to generate annual forest cover maps for the years 2015 to 2021.Specifically,both PALSAR-derived HV,HH-HV,and HH/HV and Landsat-derived Normalized Difference Vegetation Index(NDVI)were used together to generate annual maps of forest cover and three forest types(evergreen,deciduous,and mixed forest)at 30-m spatial resolution for each year.The canopy height and canopy coverage samples from the Global Ecosystem Dynamics Investigation(GEDI)and the Ice,Cloud,and land Elevation Satellite-2(ICESat-2)were used to assess forest cover maps.We also compared the spatial distribution and forested area of several forest products.Our results show that using the forest definition(canopy height>5 m,canopy coverage>10%over an area of 0.5 ha)of the Food and Agriculture Organization of the United Nations(FAO),the accuracy of resultant PALSAR/Landsat forest cover map for 2019 were 77.4%(GEDI)and 95.6%(ICESat-2).The estimated forested area(51,916 km2)was moderately higher(7.2%)than the forested area from the USDA Forest Inventory and Analysis(FIA)statistics dataset(48,202 km2)in 2017.Between 2016 and 2020,Oklahoma’s forested area increased slightly by 1.9%.The PALSAR/Landsat forest maps are more accurate in western Oklahoma compared to other satellite-based forest products.The resultant annual maps of forest cover and three different forest types over Oklahoma can be used to support statewide forest management and conservation.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Ministry of Foreign Affairs and Trade,New ZealandUniversity of Auckland.
文摘Paddy rice agriculture is practiced in both rain-fed and irrigated ecosystems in the Philippines.However,small farms are prevalent in the region,and current satellite-based mapping techniques do not distinguish between the two ecosystems at farm scales.This study developed an approach to rapidly map irrigated and rain-fed paddy rice in Iloilo,Philippines at 10 m resolutions using Google Earth Engine.This approach used an ensemble of classifiers based on time-series vegetation indices to produce dry and wet seasonal maps for the entire province.Results showed a predominance of rain-fed rice areas in both seasons,with irrigated rice making up only onefourth of the total rice area.The overall accuracy was achieved at 68%for the dry season and 75%for the wet season based on ground-acquired points and very high-resolution imagery.The two types of paddies were classified at accuracies up to 87%.Furthermore,the land cover maps showed a strong agreement with the municipal statistics.The resultant maps complement current official statistics and demonstrate the prowess of phenology-based mapping to create paddy inventories in a timely manner to inform food security and agricultural policies.
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.
基金supported in part by research grants from the US National Science Foundation(OIA-1946093,OIA-1920946).
文摘Accurate forest cover maps are the basis for estimating forest biomass and are crucial for climate regulation and biodiversity conservation,especially in sub-humid and semi-arid regions such as Oklahoma,USA.To date,there is very limited data and knowledge of the spatial pattern and temporal dynamics of forest cover in Oklahoma,and current forest cover maps have large uncertainties.In this study,multi-sensor datasets,including the Phased Arrayed L-band Synthetic Aperture Radar(PALSAR-2),Landsat,and spaceborne Light Detection and Ranging(LiDAR),were combined to generate annual forest cover maps for the years 2015 to 2021.Specifically,both PALSAR-derived HV,HH-HV,and HH/HV and Landsat-derived Normalized Difference Vegetation Index(NDVI)were used together to generate annual maps of forest cover and three forest types(evergreen,deciduous,and mixed forest)at 30-m spatial resolution for each year.The canopy height and canopy coverage samples from the Global Ecosystem Dynamics Investigation(GEDI)and the Ice,Cloud,and land Elevation Satellite-2(ICESat-2)were used to assess forest cover maps.We also compared the spatial distribution and forested area of several forest products.Our results show that using the forest definition(canopy height>5 m,canopy coverage>10%over an area of 0.5 ha)of the Food and Agriculture Organization of the United Nations(FAO),the accuracy of resultant PALSAR/Landsat forest cover map for 2019 were 77.4%(GEDI)and 95.6%(ICESat-2).The estimated forested area(51,916 km2)was moderately higher(7.2%)than the forested area from the USDA Forest Inventory and Analysis(FIA)statistics dataset(48,202 km2)in 2017.Between 2016 and 2020,Oklahoma’s forested area increased slightly by 1.9%.The PALSAR/Landsat forest maps are more accurate in western Oklahoma compared to other satellite-based forest products.The resultant annual maps of forest cover and three different forest types over Oklahoma can be used to support statewide forest management and conservation.
