To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of polici...To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of policies.However,the data landscape is changing.With emerging big data and cloud-based services,there are new opportunities for data collection,influencing both official data collection processes and the operation of the programmes they monitor.This paper uses cases and examples to explore the potential of crowdsourcing and public earth observation(EO)data products for monitoring and tracking the SDGs.This paper suggests that cloud-based services that integrate crowdsourcing and public EO data products provide cost-effective solutions for monitoring and tracking the SDGs,particularly for low-income countries.The paper also discusses the challenges of using cloud services and big data for SDG monitoring.Validation and quality control of public EO data is very important;otherwise,the user will be unable to assess the quality of the data or use it with confidence.展开更多
Eradicating extreme poverty is one of the UN’s primary sustainable development goals(SDG).Arable land is related to eradicating poverty(SDG1)and hunger(SDG2).However,the linkage between arable land use and poverty re...Eradicating extreme poverty is one of the UN’s primary sustainable development goals(SDG).Arable land is related to eradicating poverty(SDG1)and hunger(SDG2).However,the linkage between arable land use and poverty reduction is ambiguous and has seldom been investigated globally.Six indicators of agricultural inputs,crop intensification and extensification were used to explore the relationship between arable land use and poverty.Non-parametric machine learning methods were used to analyze the linkage between agriculture and poverty at the global scale,including the classification and regression tree(CART)and random forest models.We found that the yield gap,fertilizer consumption and potential cropland ratio in protected areas correlated with poverty.Developing countries usually had a ratio of actual to potential yield less than 0.33 and fertilizer consumption less than 7.31 kg/ha.Overall,crop extensification,intensification and agricultural inputs were related to poverty at the global level.展开更多
Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices.China’s global crop-monitoring system(CropWatch)us...Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices.China’s global crop-monitoring system(CropWatch)uses remote sensing data combined with selected field data to determine key crop production indicators:crop acreage,yield and production,crop condition,cropping intensity,crop-planting proportion,total food availability,and the status and severity of droughts.Results are combined to analyze the balance between supply and demand for various food crops and if needed provide early warning about possible food shortages.CropWatch data processing is highly automated and the resulting products provide new kinds of inputs for food security assessments.This paper presents a comprehensive overview of CropWatch as a remote sensingbased system,describing its structure,components,and monitoring approaches.The paper also presents examples of monitoring results and discusses the strengths and limitations of the CropWatch approach,as well as a comparison with other global crop-monitoring systems.展开更多
While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties,the temporal resolution of the data is rather low,which can be easily made worse by cloud contamination.In contr...While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties,the temporal resolution of the data is rather low,which can be easily made worse by cloud contamination.In contrast,although Moderate Resolution Imaging Spectroradiometer(MODIS)can only achieve a spatial resolution of 250 m in its normalised difference vegetation index(NDVI)product,it has a high temporal resolution,covering the Earth up to multiple times per day.To combine the high spatial resolution and high temporal resolution of different data sources,a new method(Spatial and Temporal Adaptive Vegetation index Fusion Model[STAVFM])for blending NDVI of different spatial and temporal resolutions to produce high spatialtemporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM).STAVFM defines a time window according to the temporal variation of crops,takes crop phenophase into consideration and improves the temporal weighting algorithm.The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution.An application of the generated NDVI dataset in crop biomass estimation was provided.An average absolute error of 17.2%was achieved.The estimated winter wheat biomass correlated well with observed biomass(R^(2) of 0.876).We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail.There is potential to apply the approach in many other studies,including crop production estimation,crop growth monitoring and agricultural ecosystem carbon cycle research,which will contribute to the implementation of Digital Earth by describing land surface processes in detail.展开更多
Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote...Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products.Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping,which leads to high spatial discre-pancies among remote sensing cropland products.Since no dataset could cope with all limitations,multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers.Here,in the current study,four cropland products,produced initially from multiple sensors(e.g.Landsat-8 OLI,Sentinel-2 MSI,and PROBA-V)to cover the period(2015-2017),were integrated based on their cropland mapping accuracy to build a more accurate cropland layer.The four cropland layers’accuracy was assessed at Agro-ecological zones units via an inten-sive reference dataset(17,592 samples).The most accurate crop-land layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa.As a result,the new layer was produced in higher cropland mapping accuracy(overall accuracy=91.64%and cropland’s F-score=0.75).The layer mapped the African cropland area as 282 Mha(9.38%of the Continent area).Compared to earlier crop-land synergy layers,the constructed cropland mask showed a considerable improvement in its spatial resolution(30 m instead of 250 m),mapping quality,and closeness to official statistics(R^(2)=0.853 and RMSE=2.85 Mha).The final layer can be down-loaded as described under the“Data Availability Statement”section.展开更多
Differences in progress across sustainable development goals(SDGs)are widespread globally;meanwhile,the rising call for prioritizing specific SDGs may exacerbate such gaps.Nevertheless,how these progress differences w...Differences in progress across sustainable development goals(SDGs)are widespread globally;meanwhile,the rising call for prioritizing specific SDGs may exacerbate such gaps.Nevertheless,how these progress differences would influence global sustainable development has been long neglected.Here,we present the first quantitative assessment of SDGs’progress differences globally by adopting the SDGs progress evenness index.Our results highlight that the uneven progress across SDGs has been a hindrance to sustainable development because(1)it is strongly associated with many public health risks(e.g.,air pollution),social inequalities(e.g.,gender inequality,modern slavery,wealth gap),and a reduction in life expectancy;(2)it is also associated with deforestation and habitat loss in terrestrial and marine ecosystems,increasing the challenges related to biodiversity conservation;(3)most countries with low average SDGs performance show lower progress evenness,which further hinders their fulfillment of SDGs;and(4)many countries with high average SDGs performance also showcase stagnation or even retrogression in progress evenness,which is partly ascribed to the antagonism between climate actions and other goals.These findings highlight that while setting SDGs priorities may be more realistic under the constraints of multiple global stressors,caution must be exercised to avoid new problems from intensifying uneven progress across goals.Moreover,our study reveals that the urgent needs regarding SDGs of different regions seem complementary,emphasizing that regional collaborations(e.g.,demand-oriented carbon trading between SDGs poorly performed and well-performed countries)may promote sustainable development achievements at the global scale.展开更多
Estimation of crop yield at a regional level is essential for making agricultural planning and addressing food security issues in Ethiopia.Remote sensing observations,particularly the leaf area index(LAI),have a stron...Estimation of crop yield at a regional level is essential for making agricultural planning and addressing food security issues in Ethiopia.Remote sensing observations,particularly the leaf area index(LAI),have a strong relationship with crop yield.This study has proposed an approach to estimate wheat yield at field level and regional scale in Ethiopia by assimilating the retrieved MODIS time-series LAI data into the WOrld FOod STudies(WOFOST)model.To improve the estimation of crop yield in the region,the Ensemble Kalman Filter(EnKF)was used to incorporate the LAI into the WOFOST model.The estimation accuracy of wheat crop yield was validated using field-measured yields collected during the 2018 growing season.Our findings indicated that wheat yield was more precisely estimated by WOFOST(at water-limited mode)with EnKF algorithm(R^(2)=0.80 and RMSE=413 kg ha^(−1))compared to that of without assimilating remotely sensed LAI(R^(2)=0.58,RMSE=592 kg ha^(−1)).These results demonstrated that assimilating MODIS-LAI into WOFOST has high potential and practicality to give a reference for wheat yield estimation.The findings from this study can provide information to policy,decision-makers,and other similar sectors to implement an appropriate and timely yield estimation measure.展开更多
基金funded by the National Key Research and Development Program of China(Grant No.2016YFA0600304)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA19030201).
文摘To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of policies.However,the data landscape is changing.With emerging big data and cloud-based services,there are new opportunities for data collection,influencing both official data collection processes and the operation of the programmes they monitor.This paper uses cases and examples to explore the potential of crowdsourcing and public earth observation(EO)data products for monitoring and tracking the SDGs.This paper suggests that cloud-based services that integrate crowdsourcing and public EO data products provide cost-effective solutions for monitoring and tracking the SDGs,particularly for low-income countries.The paper also discusses the challenges of using cloud services and big data for SDG monitoring.Validation and quality control of public EO data is very important;otherwise,the user will be unable to assess the quality of the data or use it with confidence.
基金supported financially by the National Key Research and Development Program(Grant No.2016YFA0600304)the National Natural Science Foundation of China(Grant No.41861144019)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA19030201).
文摘Eradicating extreme poverty is one of the UN’s primary sustainable development goals(SDG).Arable land is related to eradicating poverty(SDG1)and hunger(SDG2).However,the linkage between arable land use and poverty reduction is ambiguous and has seldom been investigated globally.Six indicators of agricultural inputs,crop intensification and extensification were used to explore the relationship between arable land use and poverty.Non-parametric machine learning methods were used to analyze the linkage between agriculture and poverty at the global scale,including the classification and regression tree(CART)and random forest models.We found that the yield gap,fertilizer consumption and potential cropland ratio in protected areas correlated with poverty.Developing countries usually had a ratio of actual to potential yield less than 0.33 and fertilizer consumption less than 7.31 kg/ha.Overall,crop extensification,intensification and agricultural inputs were related to poverty at the global level.
基金The development of CropWatch and its operation was supported by grants from Major Programs of the Chinese Academy of Sciences during the 9th Five-Year Plan period(KZ951-A1-302-02[19982000])the Key Program of the Chinese Academy of Sciences(KZ95T-03-02[19982000])+4 种基金the Knowledge Innovation Programs of the Chinese Academy of Sciences(KZCX2-313[20002002],KZCX3-SW-338-2[20032007],KSCX1-YW-09-01[20082010])the National Key Technologies Research and Development Program of China during the 10th Five-Year Plan Period(2001BA513B02[20012003])the National High-Tech Research and Development Program of China(2003AA131050[20032005],2012AA12A307[20122014],2013AA12A302[20132015])the National Extension Program for Main Achievements(KJSX0504[20052007])the Conversion Program for Technical Achievements in Agriculture(GQ050006[20052007])by the Ministry of Science and Technology of China.
文摘Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices.China’s global crop-monitoring system(CropWatch)uses remote sensing data combined with selected field data to determine key crop production indicators:crop acreage,yield and production,crop condition,cropping intensity,crop-planting proportion,total food availability,and the status and severity of droughts.Results are combined to analyze the balance between supply and demand for various food crops and if needed provide early warning about possible food shortages.CropWatch data processing is highly automated and the resulting products provide new kinds of inputs for food security assessments.This paper presents a comprehensive overview of CropWatch as a remote sensingbased system,describing its structure,components,and monitoring approaches.The paper also presents examples of monitoring results and discusses the strengths and limitations of the CropWatch approach,as well as a comparison with other global crop-monitoring systems.
基金The research was supported by National Natural Science Foundation of China,Nos.40801144 and 41171331Knowledge Innovation Program of CAS,No.KSCX1-YW-09-01the National Key Technology R&D Program,No.2008BADA8B02.
文摘While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties,the temporal resolution of the data is rather low,which can be easily made worse by cloud contamination.In contrast,although Moderate Resolution Imaging Spectroradiometer(MODIS)can only achieve a spatial resolution of 250 m in its normalised difference vegetation index(NDVI)product,it has a high temporal resolution,covering the Earth up to multiple times per day.To combine the high spatial resolution and high temporal resolution of different data sources,a new method(Spatial and Temporal Adaptive Vegetation index Fusion Model[STAVFM])for blending NDVI of different spatial and temporal resolutions to produce high spatialtemporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model(STARFM).STAVFM defines a time window according to the temporal variation of crops,takes crop phenophase into consideration and improves the temporal weighting algorithm.The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution.An application of the generated NDVI dataset in crop biomass estimation was provided.An average absolute error of 17.2%was achieved.The estimated winter wheat biomass correlated well with observed biomass(R^(2) of 0.876).We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail.There is potential to apply the approach in many other studies,including crop production estimation,crop growth monitoring and agricultural ecosystem carbon cycle research,which will contribute to the implementation of Digital Earth by describing land surface processes in detail.
基金was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19030201]National Natural Science Foundation of China[41861144019 and 41561144013].
文摘Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products.Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping,which leads to high spatial discre-pancies among remote sensing cropland products.Since no dataset could cope with all limitations,multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers.Here,in the current study,four cropland products,produced initially from multiple sensors(e.g.Landsat-8 OLI,Sentinel-2 MSI,and PROBA-V)to cover the period(2015-2017),were integrated based on their cropland mapping accuracy to build a more accurate cropland layer.The four cropland layers’accuracy was assessed at Agro-ecological zones units via an inten-sive reference dataset(17,592 samples).The most accurate crop-land layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa.As a result,the new layer was produced in higher cropland mapping accuracy(overall accuracy=91.64%and cropland’s F-score=0.75).The layer mapped the African cropland area as 282 Mha(9.38%of the Continent area).Compared to earlier crop-land synergy layers,the constructed cropland mask showed a considerable improvement in its spatial resolution(30 m instead of 250 m),mapping quality,and closeness to official statistics(R^(2)=0.853 and RMSE=2.85 Mha).The final layer can be down-loaded as described under the“Data Availability Statement”section.
基金This work was supported by the National Natural Science Foundation of China(42001267,42041005,and 42041007)the International Partnership Program of Chinese Academy of Sciences(121311KYSB20170004-04)the Chinese Academy of Sciences Strategic Priority Research Program(A)(grant no.XDA20050103)。
文摘Differences in progress across sustainable development goals(SDGs)are widespread globally;meanwhile,the rising call for prioritizing specific SDGs may exacerbate such gaps.Nevertheless,how these progress differences would influence global sustainable development has been long neglected.Here,we present the first quantitative assessment of SDGs’progress differences globally by adopting the SDGs progress evenness index.Our results highlight that the uneven progress across SDGs has been a hindrance to sustainable development because(1)it is strongly associated with many public health risks(e.g.,air pollution),social inequalities(e.g.,gender inequality,modern slavery,wealth gap),and a reduction in life expectancy;(2)it is also associated with deforestation and habitat loss in terrestrial and marine ecosystems,increasing the challenges related to biodiversity conservation;(3)most countries with low average SDGs performance show lower progress evenness,which further hinders their fulfillment of SDGs;and(4)many countries with high average SDGs performance also showcase stagnation or even retrogression in progress evenness,which is partly ascribed to the antagonism between climate actions and other goals.These findings highlight that while setting SDGs priorities may be more realistic under the constraints of multiple global stressors,caution must be exercised to avoid new problems from intensifying uneven progress across goals.Moreover,our study reveals that the urgent needs regarding SDGs of different regions seem complementary,emphasizing that regional collaborations(e.g.,demand-oriented carbon trading between SDGs poorly performed and well-performed countries)may promote sustainable development achievements at the global scale.
基金supported by the National Key Research and Development Project of China[2019YFE0126900]Strategic Priority Research Program of Chinese Academy of Sciences[XDA19030200].
文摘Estimation of crop yield at a regional level is essential for making agricultural planning and addressing food security issues in Ethiopia.Remote sensing observations,particularly the leaf area index(LAI),have a strong relationship with crop yield.This study has proposed an approach to estimate wheat yield at field level and regional scale in Ethiopia by assimilating the retrieved MODIS time-series LAI data into the WOrld FOod STudies(WOFOST)model.To improve the estimation of crop yield in the region,the Ensemble Kalman Filter(EnKF)was used to incorporate the LAI into the WOFOST model.The estimation accuracy of wheat crop yield was validated using field-measured yields collected during the 2018 growing season.Our findings indicated that wheat yield was more precisely estimated by WOFOST(at water-limited mode)with EnKF algorithm(R^(2)=0.80 and RMSE=413 kg ha^(−1))compared to that of without assimilating remotely sensed LAI(R^(2)=0.58,RMSE=592 kg ha^(−1)).These results demonstrated that assimilating MODIS-LAI into WOFOST has high potential and practicality to give a reference for wheat yield estimation.The findings from this study can provide information to policy,decision-makers,and other similar sectors to implement an appropriate and timely yield estimation measure.
