Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection

Blending algorithms model land cover change by using highly resolved spatial data from one sensor and highly resolved temporal data from another. Because the data are not usually observed concurrently, unaccounted spatial and temporal variances cause error in blending algorithms, yet, to date, there has been no definitive assessment of algorithm performance against spatial and temporal variances. Our objectives were to: (i) evaluate the accuracy of two advanced blending algorithms (STARFM and ESTARFM) and two simple benchmarking algorithms in two landscapes with contrasting spatial and temporal variances; and (ii) synthesise the spatial and temporal conditions under which the algorithms performed best. Landsat-like images were simulated on 27 dates in total using the nearest temporal cloud-free Landsat-MODIS pairs to the simulation date, one before and one after. RMSD, bias, and r2 estimates between simulated and observed Landsat images were calculated, and overall variance of Landsat and MODIS datasets were partitioned into spatial and temporal components. Assessment was performed over the whole study site, and for specific land covers. Results addressing objective (i) were that: ESTARFM did not always produce lower errors than STARFM; STARFM and ESTARFM did not always produce lower errors than simple benchmarking algorithms; and land cover spatial and temporal variances were strongly associated with algorithm performance. Results addressing objective (ii) indicated ESTARFM was superior where/when spatial variance was dominant; and STARFM was superior where/when temporal variance was dominant. We proposed a framework for selecting blending algorithms based on partitioning variance into the spatial and temporal components and suggested that comparing Landsat and MODIS spatial and temporal variances was a practical method to determine if, and when, MODIS could add value for blending

Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics : A framework for algorithm selection

Blending algorithms model land cover change by using highly resolved spatial data from one sensor and highly resolved temporal data from another. Because the data are not usually observed concurrently, unaccounted spatial and temporal variances cause error in blending algorithms, yet, to date, there has been no definitive assessment of algorithm performance against spatial and temporal variances. Our objectives were to: (i) evaluate the accuracy of two advanced blending algorithms (STARFM and ESTARFM) and two simple benchmarking algorithms in two landscapes with contrasting spatial and temporal variances; and (ii) synthesise the spatial and temporal conditions under which the algorithms performed best. Landsat-like images were simulated on 27 dates in total using the nearest temporal cloud-free Landsat-MODIS pairs to the simulation date, one before and one after. RMSD, bias, and r2 estimates between simulated and observed Landsat images were calculated, and overall variance of Landsat and MODIS datasets were partitioned into spatial and temporal components. Assessment was performed over the whole study site, and for specific land covers. Results addressing objective (i) were that: ESTARFM did not always produce lower errors than STARFM; STARFM and ESTARFM did not always produce lower errors than simple benchmarking algorithms; and land cover spatial and temporal variances were strongly associated with algorithm performance. Results addressing objective (ii) indicated ESTARFM was superior where/when spatial variance was dominant; and STARFM was superior where/when temporal variance was dominant. We proposed a framework for selecting blending algorithms based on partitioning variance into the spatial and temporal components and suggested that comparing Landsat and MODIS spatial and temporal variances was a practical method to determine if, and when, MODIS could add value for blending

Capturing dynamic ligand-to-metal charge transfer with a long-lived cationic intermediate for anionic redox

International audienceReversible anionic redox reactions represent a transformational change for creating advanced high-energy-density positive-electrode materials for lithium-ion batteries. The activation mechanism of these reactions is frequently linked to ligand-to-metal charge transfer (LMCT) processes, which have not been fully validated experimentally due to the lack of suitable model materials. Here we show that the activation of anionic redox in cation-disordered rock-salt Li1.17Ti0.58Ni0.25O2 involves a long-lived intermediate Ni3+/4+ species, which can fully evolve to Ni2+ during relaxation. Combining electrochemical analysis and spectroscopic techniques, we quantitatively identified that the reduction of this Ni3+/4+ species goes through a dynamic LMCT process (Ni3+/4+–O2− → Ni2+–On−). Our findings provide experimental validation of previous theoretical hypotheses and help to rationalize several peculiarities associated with anionic redox, such as cationic–anionic redox inversion and voltage hysteresis. This work also provides additional guidance for designing high-capacity electrodes by screening appropriate cationic species for mediating LMCT

Climate change and zoonotic infections in the Russian Arctic

Climate change in the Russian Arctic is more pronounced than in any other part of the country. Between 1955 and 2000, the annual average air temperature in the Russian North increased by 1.2°C. During the same period, the mean temperature of upper layer of permafrost increased by 3°C. Climate change in Russian Arctic increases the risks of the emergence of zoonotic infectious diseases. This review presents data on morbidity rates among people, domestic animals and wildlife in the Russian Arctic, focusing on the potential climate related emergence of such diseases as tick-borne encephalitis, tularemia, brucellosis, leptospirosis, rabies, and anthrax