The Amazon basin has experienced an extreme drought that started in the austral summer of 2022-23 and extends into 2024. This drought started earlier than other previous droughts. Although some rain fell during the au...The Amazon basin has experienced an extreme drought that started in the austral summer of 2022-23 and extends into 2024. This drought started earlier than other previous droughts. Although some rain fell during the austral summer, totals remained below average. Higher temperatures during austral winter and spring 2023, which affected most of Central South America, then aggravated drought conditions. This coincided with an intense El Niño and abnormally warm tropical North Atlantic Ocean temperatures since mid-2023. Decreased rainfall across the Amazon basin, negative anomalies in evapotranspiration (derived from latent heat) and soil moisture indicators, as well as increased temperatures during the dry-to-wet transition season, September-October-November (SON) 2023, combined to delay the onset of the wet season in the hydrological year 2023-24 by nearly two months and caused it to be uncharacteristically weak. SON 2023 registered a precipitation deficit of the order of 50 to 100 mm/month, and temperatures +3˚C higher than usual in Amazonia, leading to reduced evapotranspiration and soil moisture indicators. These processes, in turn, determined an exceptionally late onset and a lengthening of the dry season, affecting the 2023-2024 hydrological year. These changes were aggravated by a heat wave from June to December 2023. Drought-heat compound events and their consequences are the most critical natural threats to society. River levels reached record lows, or dried up completely, affecting Amazonian ecosystems. Increased risk of wildfires is another concern exacerbated by these conditions.展开更多
Currently three up-to-date Terrestrial Reference Frames(TRF) are available, the ITRF2014 from IGN, the DTRF2014 from DGFI-TUM, and JTRF2014 from JPL. All use the identical input data of space-geodetic station position...Currently three up-to-date Terrestrial Reference Frames(TRF) are available, the ITRF2014 from IGN, the DTRF2014 from DGFI-TUM, and JTRF2014 from JPL. All use the identical input data of space-geodetic station positions and Earth orientation parameters, but the concept of combining these data is fundamentally different. The IGN approach is based on the combination of technique solutions, while the DGFI is combining the normal equation systems. Both yield in reference epoch coordinates and velocities for a global set of stations. JPL uses a Kalman filter approach, realizing a TRF through weekly time series of geocentric coordinates. As the determination of the CRF is not independent of the TRF and vice versa, the choice of the TRF might impact on the CRF, Within this work we assess this effect.We find that the estimated Earth orientation parameter(EOP) from DTRF2014 agree best with those from ITRF2014, the EOP resulting from JTRF2014 show besides clear yearly signals also some artifacts linked to certain stations. The estimated source position time series however, agree with each other better than ±1 μas. When fixing EOP and station positions we can see the maximal effect of the TRF on the CRF. Here large systematics in position as well as proper motion arise. In case of ITRF2008 they can be linked to the missing data after 2008. By allowing the EOP and stations to participate in the adjustment,the agreement increases, however, systematics remain.展开更多
The effectiveness of machine learning algorithms and the limited reference data introduce uncertainness for sugarcane classification.To address these problems,our study classified sugarcane plantations at the field sc...The effectiveness of machine learning algorithms and the limited reference data introduce uncertainness for sugarcane classification.To address these problems,our study classified sugarcane plantations at the field scale using multi-temporal and multi-sensor data together with a large number of ground truth datasets(>13,000 points)and compared the efficacy of ensemble and kernel classifier methods over 3 years(2021,2022,and 2023)across Northeast Thailand.In the first step,land cover was generated from a random forest classifier,demonstrating excellent results for all years with an OA higher than 95%.In the second step,the discretization of sugarcane from non-sugarcane classes in the agricultural category was conducted using four efficient machine learning algorithms(decision tree(DT),random forest(RF),support vector machine(SVM),and one-class SVM).The RF classifier gave the optimal results with over 90%accuracy.Our results aligned with provincial statistics from the Office of the Cane and Sugar Board,thereby highlighting the efficacy and reliability of the RF method in mapping sugarcane in small fields and cloudy regions.A temporal evolution analysis of sugarcane cultivation spanning the preceding 3 years revealed a significant increase in the productive area.Our findings provide crucial information for sustainable management practices.展开更多
文摘The Amazon basin has experienced an extreme drought that started in the austral summer of 2022-23 and extends into 2024. This drought started earlier than other previous droughts. Although some rain fell during the austral summer, totals remained below average. Higher temperatures during austral winter and spring 2023, which affected most of Central South America, then aggravated drought conditions. This coincided with an intense El Niño and abnormally warm tropical North Atlantic Ocean temperatures since mid-2023. Decreased rainfall across the Amazon basin, negative anomalies in evapotranspiration (derived from latent heat) and soil moisture indicators, as well as increased temperatures during the dry-to-wet transition season, September-October-November (SON) 2023, combined to delay the onset of the wet season in the hydrological year 2023-24 by nearly two months and caused it to be uncharacteristically weak. SON 2023 registered a precipitation deficit of the order of 50 to 100 mm/month, and temperatures +3˚C higher than usual in Amazonia, leading to reduced evapotranspiration and soil moisture indicators. These processes, in turn, determined an exceptionally late onset and a lengthening of the dry season, affecting the 2023-2024 hydrological year. These changes were aggravated by a heat wave from June to December 2023. Drought-heat compound events and their consequences are the most critical natural threats to society. River levels reached record lows, or dried up completely, affecting Amazonian ecosystems. Increased risk of wildfires is another concern exacerbated by these conditions.
基金supported by the Deutsche Forschungsgemeinschaft(DFG), Project Nr.:HE 5937/2-1 and NO318/ 13-1supported by the European Research Council(ERC) under the ERC-2017-STG SENTIFLEX project(Grant Agreement 755617)
文摘Currently three up-to-date Terrestrial Reference Frames(TRF) are available, the ITRF2014 from IGN, the DTRF2014 from DGFI-TUM, and JTRF2014 from JPL. All use the identical input data of space-geodetic station positions and Earth orientation parameters, but the concept of combining these data is fundamentally different. The IGN approach is based on the combination of technique solutions, while the DGFI is combining the normal equation systems. Both yield in reference epoch coordinates and velocities for a global set of stations. JPL uses a Kalman filter approach, realizing a TRF through weekly time series of geocentric coordinates. As the determination of the CRF is not independent of the TRF and vice versa, the choice of the TRF might impact on the CRF, Within this work we assess this effect.We find that the estimated Earth orientation parameter(EOP) from DTRF2014 agree best with those from ITRF2014, the EOP resulting from JTRF2014 show besides clear yearly signals also some artifacts linked to certain stations. The estimated source position time series however, agree with each other better than ±1 μas. When fixing EOP and station positions we can see the maximal effect of the TRF on the CRF. Here large systematics in position as well as proper motion arise. In case of ITRF2008 they can be linked to the missing data after 2008. By allowing the EOP and stations to participate in the adjustment,the agreement increases, however, systematics remain.
基金financially supported by Mahasarakham University.
文摘The effectiveness of machine learning algorithms and the limited reference data introduce uncertainness for sugarcane classification.To address these problems,our study classified sugarcane plantations at the field scale using multi-temporal and multi-sensor data together with a large number of ground truth datasets(>13,000 points)and compared the efficacy of ensemble and kernel classifier methods over 3 years(2021,2022,and 2023)across Northeast Thailand.In the first step,land cover was generated from a random forest classifier,demonstrating excellent results for all years with an OA higher than 95%.In the second step,the discretization of sugarcane from non-sugarcane classes in the agricultural category was conducted using four efficient machine learning algorithms(decision tree(DT),random forest(RF),support vector machine(SVM),and one-class SVM).The RF classifier gave the optimal results with over 90%accuracy.Our results aligned with provincial statistics from the Office of the Cane and Sugar Board,thereby highlighting the efficacy and reliability of the RF method in mapping sugarcane in small fields and cloudy regions.A temporal evolution analysis of sugarcane cultivation spanning the preceding 3 years revealed a significant increase in the productive area.Our findings provide crucial information for sustainable management practices.
