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Figure 3. Projected infrastructure hazard areas from degrading permafrost over the Northern Hemisphere by year 2050 [26]. The figure was reproduced with permission from [26], which is licensed under a Creative Commons Attribution 4.0 International License. In cold regions, climate change could significantly affect surface morphology (e.g., permafrost melting due to global warming), which may pose a threat to 70% of current infrastructure in Arctic permafrost regions by 2050 (Figure 3) [26]. Remote sensing technologies could offer a useful platform to better understand these geomorphic changes, especially those that guarantee high-resolution topography analysis [168].

Figure 3 Projected infrastructure hazard areas from degrading permafrost over the Northern Hemisphere by year 2050 [26]. The figure was reproduced with permission from [26], which is licensed under a Creative Commons Attribution 4.0 International License. In cold regions, climate change could significantly affect surface morphology (e.g., permafrost melting due to global warming), which may pose a threat to 70% of current infrastructure in Arctic permafrost regions by 2050 (Figure 3) [26]. Remote sensing technologies could offer a useful platform to better understand these geomorphic changes, especially those that guarantee high-resolution topography analysis [168].

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Snow water equivalent (SWE) describes the amount of water contained in the snowpack when completely melted. To estimate SWE from satellite microwave observations, the scattering and emission contributions from intervening atmosphere, snow surface, snowpack, and underlying soil and snow-soil interactions need to be distinguished and accounted for, which can be done by exploiting the frequency-dependent sensitivity of microwaves to land surface components [129,130] (Figure 1). Figure 1. Components of snow emissions and scattering observed by space-borne microwave radiometer and radar observations. By neglecting atmosphere scattering and upward emission, the satellite signals mainly consist of contributions from the air-snow surface (as), snow pack (v), underlying soil (g), and snow-soil interactions (gv). Figure 1. Components of snow emissions and scattering observed by space-borne microwave radiometer
Snow water equivalent (SWE) describes the amount of water contained in the snowpack when completely melted. To estimate SWE from satellite microwave observations, the scattering and emission contributions from intervening atmosphere, snow surface, snowpack, and underlying soil and snow-soil interactions need to be distinguished and accounted for, which can be done by exploiting the frequency-dependent sensitivity of microwaves to land surface components [129,130] (Figure 1). Figure 1. Components of snow emissions and scattering observed by space-borne microwave radiometer and radar observations. By neglecting atmosphere scattering and upward emission, the satellite signals mainly consist of contributions from the air-snow surface (as), snow pack (v), underlying soil (g), and snow-soil interactions (gv). Figure 1. Components of snow emissions and scattering observed by space-borne microwave radiometer
Figure 2. Estimated annual non-frozen season in 2017 derived from three operational satellite FT data products, including: (a) SSM/I (37 GHz; 25-km), (b) AMSR2 (36.5 GHz; 6-km), and (c) SMAP (1.4 GHz; 9-km). Areas outside of the FT global domain for each product are shown in grey and white.
Figure 2. Estimated annual non-frozen season in 2017 derived from three operational satellite FT data products, including: (a) SSM/I (37 GHz; 25-km), (b) AMSR2 (36.5 GHz; 6-km), and (c) SMAP (1.4 GHz; 9-km). Areas outside of the FT global domain for each product are shown in grey and white.
Figure 5. Vegetation conditions and growth trend over the high northern latitudes from 2003 through 2017. (a) Mean summer MODIS NDVI; (b) Mean summer AMSR-E/2 VOD; (c) Trends in mean summer NDVI; (d) Trends in mean summer VOD. In c and d, positive values indicate an increasing trend and negative values indicate a decreasing trend; pixels with significant trend (p-value < 0.05) are shown with crosshatch. Figure 5. Vegetation conditions and growth trend over the high northern latitudes from 2003 through regions of vegetation production increase (greening) or decrease (browning). A 10-year time series 0 monthly average AVHRR NDVI indicated an increase in photosynthetic activity in the northern hig! atitudes from 1981 to 1991 driven by earlier growing season onset [241]. A subsequent study using | onger AVHRR time series indicated an increase in tundra vegetation growth in boreal North Americz argely driven by longer growing seasons, but decreasing growth in interior forests [242]. Recent worl used time series of Landsat vegetation indices in Arctic sites, with results indicating more areas witl increasing growth than decreasing growth [243]. Analysis of a 28-year Landsat NDVI record indicate: that greening and browning of Canadian boreal forests were largely driven by disturbances from wil fire and insect damages; and, in forests not affected by disturbance, climate changes were associatec with both areas of greening and browning [240]. Regarding phenological changes, a 30-year Landsa record was used to detect an earlier/heterogeneous leaf emergence trend in temperate/boreal deciduou forests [197], while a 33-year AVHRR NDVI time series revealed regional trends toward earlier growin: season onset, later growing season end, and longer growing season duration over the high norther1 latitudes [31]. Based on recent satellite optical and microwave observations for years 2003 through 201! over the high northern latitudes, the mean summer (JJA) MODIS NDVI record (MYD13A1.006; [244 showed similar spatial patterns with the AMSR-E/2 VOD record [216], despite the different spatia resolutions and underlying physics of the observations (Figure 5a,b). Major greening and biomas: growth trends are found in northern taiga and tundra regions, where both NDVI and VOD shov significant increases (p-value < 0.05) (Figure 5c,d). However, declining NDVI and VOD trends ar also widespread, indicating decreasing productivity. The declining trend areas largely occur in borea forest but are generally less significant than positive trend areas.
Figure 5. Vegetation conditions and growth trend over the high northern latitudes from 2003 through 2017. (a) Mean summer MODIS NDVI; (b) Mean summer AMSR-E/2 VOD; (c) Trends in mean summer NDVI; (d) Trends in mean summer VOD. In c and d, positive values indicate an increasing trend and negative values indicate a decreasing trend; pixels with significant trend (p-value < 0.05) are shown with crosshatch. Figure 5. Vegetation conditions and growth trend over the high northern latitudes from 2003 through regions of vegetation production increase (greening) or decrease (browning). A 10-year time series 0 monthly average AVHRR NDVI indicated an increase in photosynthetic activity in the northern hig! atitudes from 1981 to 1991 driven by earlier growing season onset [241]. A subsequent study using | onger AVHRR time series indicated an increase in tundra vegetation growth in boreal North Americz argely driven by longer growing seasons, but decreasing growth in interior forests [242]. Recent worl used time series of Landsat vegetation indices in Arctic sites, with results indicating more areas witl increasing growth than decreasing growth [243]. Analysis of a 28-year Landsat NDVI record indicate: that greening and browning of Canadian boreal forests were largely driven by disturbances from wil fire and insect damages; and, in forests not affected by disturbance, climate changes were associatec with both areas of greening and browning [240]. Regarding phenological changes, a 30-year Landsa record was used to detect an earlier/heterogeneous leaf emergence trend in temperate/boreal deciduou forests [197], while a 33-year AVHRR NDVI time series revealed regional trends toward earlier growin: season onset, later growing season end, and longer growing season duration over the high norther1 latitudes [31]. Based on recent satellite optical and microwave observations for years 2003 through 201! over the high northern latitudes, the mean summer (JJA) MODIS NDVI record (MYD13A1.006; [244 showed similar spatial patterns with the AMSR-E/2 VOD record [216], despite the different spatia resolutions and underlying physics of the observations (Figure 5a,b). Major greening and biomas: growth trends are found in northern taiga and tundra regions, where both NDVI and VOD shov significant increases (p-value < 0.05) (Figure 5c,d). However, declining NDVI and VOD trends ar also widespread, indicating decreasing productivity. The declining trend areas largely occur in borea forest but are generally less significant than positive trend areas.
Figure 6. Antarctic surface ice velocity map. The floating velocity field of the Drygalski Ice Tongue is enlarged in the close-up below. The figure was reproduced with permission from [60]. The velocity of ice flow in the Antarctic has been closely monitored using optical and radar remote sensing due to its importance in determining ice discharge and sea level rise [60,256]. The velocity map derived from the MODIS-based Mosaic of Antarctica data showed the general flow patterns of glaciers and ice sheets moving from interior Antarctica toward the ocean for the periods from 2003-2004 and 2008-2009 (Figure 6). Continuous monitoring of the widespread ice flow over the entire continent is highly needed for improving our understanding of ice sheet dynamics and evolution in a warming climate [60,256].
Figure 6. Antarctic surface ice velocity map. The floating velocity field of the Drygalski Ice Tongue is enlarged in the close-up below. The figure was reproduced with permission from [60]. The velocity of ice flow in the Antarctic has been closely monitored using optical and radar remote sensing due to its importance in determining ice discharge and sea level rise [60,256]. The velocity map derived from the MODIS-based Mosaic of Antarctica data showed the general flow patterns of glaciers and ice sheets moving from interior Antarctica toward the ocean for the periods from 2003-2004 and 2008-2009 (Figure 6). Continuous monitoring of the widespread ice flow over the entire continent is highly needed for improving our understanding of ice sheet dynamics and evolution in a warming climate [60,256].
Figure 7. Climatology and changes of the TP snow cover fraction (%) calculated using MCD10A1-TP (a, b) and TPSCE (c, d) from 2001-2014. Black dots in (b) and (d) indicate the changes are statistically significant at the 95% level. The figure was reproduced with permission from [264]. ieee," aecram Tie Based on the MODIS snow cover product (MOD10A2) and observations from 37 meteorological stations, a significant trend of earlier onset of snow ablation during the 2001-2015 period was detected over the TP [262]. By analyzing MODIS snow cover data from 2001 to 2011, other research [263] found about 34.14% (5.56%) of the TP area having a declining (significant declining) trend in snow duration, while 24.75% (3.9%) of the region showed increasing snow duration. To further enhance the accuracy of TP snow products, Chen et al. [264] integrated snow cover data from multiple sources to generate a gap-filled daily 5-km Tibetan Plateau snow cover extent record (TPSCE) from 1981-2016. As revealed by the TPSCE, the snow cover fraction increased in the northern interior TP river basins and upper reaches of the Yangtze, Mekong, and Brahmaputra River basins (Figure 7).
Figure 7. Climatology and changes of the TP snow cover fraction (%) calculated using MCD10A1-TP (a, b) and TPSCE (c, d) from 2001-2014. Black dots in (b) and (d) indicate the changes are statistically significant at the 95% level. The figure was reproduced with permission from [264]. ieee," aecram Tie Based on the MODIS snow cover product (MOD10A2) and observations from 37 meteorological stations, a significant trend of earlier onset of snow ablation during the 2001-2015 period was detected over the TP [262]. By analyzing MODIS snow cover data from 2001 to 2011, other research [263] found about 34.14% (5.56%) of the TP area having a declining (significant declining) trend in snow duration, while 24.75% (3.9%) of the region showed increasing snow duration. To further enhance the accuracy of TP snow products, Chen et al. [264] integrated snow cover data from multiple sources to generate a gap-filled daily 5-km Tibetan Plateau snow cover extent record (TPSCE) from 1981-2016. As revealed by the TPSCE, the snow cover fraction increased in the northern interior TP river basins and upper reaches of the Yangtze, Mekong, and Brahmaputra River basins (Figure 7).
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