scholarly journals Teleconnections between Snow Cover Change over Siberia and Crop Growth in Northeast China

2020 ◽  
Vol 12 (18) ◽  
pp. 7632
Author(s):  
Cong Guan ◽  
Lingxue Yu ◽  
Fengqin Yan ◽  
Shuwen Zhang
Keyword(s):  
Snow Cover ◽  
Northeast China ◽  
Vegetation Index ◽  
Regional Climate ◽  
Growing Season ◽  
Crop Growth ◽  
Maximum Temperature ◽  
Satellite Measurement ◽  
Agricultural Ecosystem ◽  
Snow Cover Area

Snow cover is a sensitive indicator of climate change, and the variations in snow cover can influence the global climate system and terrestrial water cycling. However, the teleconnections between snow cover changes of the northern hemisphere and the crop growth of Northeast China (NEC) are less documented. In this study, we estimated the correlations between spring snow cover area over Siberia (SSCA) and the regional climate, as well as the crop growth in NEC based on both satellite measurement and observational climate records from 1982 to 2015. The local temperature, including minimum temperature (Tmin) in May–June, maximum temperature (Tmax), and Tmin in July–August, showed significant negative correlations with SSCA. SSCA is found to be negatively correlated to rainfall during the beginning of the growing season, while positively correlated to rainfall during the peak growing season for the agricultural ecosystem of NEC. The remote responses of the normalized difference vegetation index (NDVI) to SSCA varied across different climate zones and different growing periods. The NDVI variations over cold and dry cultivated regions exhibit negative correlations with SSCA in May–June, which is opposite for the wetter areas. The negative correlation between NDVI over the agricultural ecosystem and SSCA during the peak growing season was also detected, implying the variations in SSCA might be an essential driving factor in affecting the crop growth through modifying the regional climate of NEC. In the future, more in situ observations and model simulations should be conducted to verify our results described here, which would have significant implications for maintaining regional food security and sustainable development in Northeast China under the changing climate background.

scholarly journals Assessing Snow Phenology and Its Environmental Driving Factors in Northeast China

2022 ◽  
Vol 14 (2) ◽  
pp. 262
Author(s):  
Hui Guo ◽  
Xiaoyan Wang ◽  
Zecheng Guo ◽  
Siyong Chen
Keyword(s):  
Snow Cover ◽  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Cycle ◽  
Water Source ◽  
Driving Factors ◽  
Change Rate ◽  
Mean Temperature ◽  
Forested Areas

Snow cover is an important water source and even an Essential Climate Variable (ECV) as defined by the World Meteorological Organization (WMO). Assessing snow phenology and its driving factors in Northeast China will help with comprehensively understanding the role of snow cover in regional water cycle and climate change. This study presents spatiotemporal variations in snow phenology and the relative importance of potential drivers, including climate, geography, and the normalized difference vegetation index (NDVI), based on the MODIS snow products across Northeast China from 2001 to 2018. The results indicated that the snow cover days (SCD), snow cover onset dates (SCOD) and snow cover end dates (SCED) all showed obvious latitudinal distribution characteristics. As the latitude gradually increases, SCD becomes longer, SCOD advances and SCED delays. Overall, there is a growing tendency in SCD and a delayed trend in SCED across time. The variations in snow phenology were driven by mean temperature, followed by latitude, while precipitation, aspect and slope all had little effect on the SCD, SCOD and SCED. With decreasing temperature, the SCD and SCED showed upward trends. The mean temperature has negatively correlation with SCD and SCED and positively correlation with SCOD. With increasing latitude, the change rate of the SCD, SCOD and SCED in the whole Northeast China were 10.20 d/degree, −3.82 d/degree and 5.41 d/degree, respectively, and the change rate of snow phenology in forested areas was lower than that in nonforested areas. At the same latitude, the snow phenology for different underlying surfaces varied greatly. The correlations between the snow phenology and NDVI were mainly positive, but weak correlations accounted for a large proportion.

scholarly journals Understanding Land-Atmosphere-Climate Coupling from the Canadian Prairie Dataset

2018 ◽  
Author(s):  
Alan K Betts ◽  
Raymond L Desjardins
Keyword(s):  
Snow Cover ◽  
Diurnal Cycle ◽  
Radiative Forcing ◽  
Potential Temperature ◽  
Warm Season ◽  
Growing Season ◽  
Cold Season ◽  
Maximum Temperature ◽  
Canadian Prairie ◽  
Cloud Forcing

Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land-atmosphere-cloud coupling. The key reason is that trained observers made hourly estimates of opaque cloud fraction that obscures the sun, moon or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality that they can be calibrated against Baseline Surface Radiation Network data to give the climatology of the daily short-wave, longwave and total cloud forcing (SWCF, LWCF and CF). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing reverses sign from negative in the warm season to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10°C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature and the pressure height of the lifting condensation level are all tightly coupled to opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between cloud forcing and the warm season imbalance of the diurnal cycle; which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forci, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of mixing ratio which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season and its dependence on reflective cloud.

scholarly journals Understanding Land–Atmosphere–Climate Coupling from the Canadian Prairie Dataset

Environments ◽  
2018 ◽  
Vol 5 (12) ◽  
pp. 129 ◽  
Author(s):  
Alan Betts ◽  
Raymond Desjardins
Keyword(s):  
Snow Cover ◽  
Diurnal Cycle ◽  
Radiative Forcing ◽  
Potential Temperature ◽  
Warm Season ◽  
Growing Season ◽  
Cold Season ◽  
Maximum Temperature ◽  
Canadian Prairie ◽  
Cloud Forcing

Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land–atmosphere–cloud coupling. The key reason is that trained observers made hourly estimates of the opaque cloud fraction that obscures the sun, moon, or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality such that they can be calibrated against Baseline Surface Radiation Network data to yield the climatology of the daily short-wave, long-wave, and total cloud forcing (SWCF, LWCF and CF, respectively). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing changes sign from negative in the warm season, to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10 °C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud, and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature, and the pressure height of the lifting condensation level are all tightly coupled to the opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between the cloud forcing and the warm season imbalance of the diurnal cycle, which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forcing, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer, goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of the mixing ratio, which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season, and its dependence on reflective cloud.

scholarly journals Vegetation Change and Its Response to Climate Change between 2000 and 2016 in Marshes of the Songnen Plain, Northeast China

2020 ◽  
Vol 12 (9) ◽  
pp. 3569 ◽  
Author(s):  
Yanji Wang ◽  
Xiangjin Shen ◽  
Ming Jiang ◽  
Xianguo Lu
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Maximum Temperature ◽  
Climate Data ◽  
Songnen Plain ◽  
Asymmetric Effects ◽  
Climate Change Effects

Songnen Plain is a representative semi-arid marshland in China. The Songnen Plain marshes have undergone obvious loss during the past decades. In order to protect and restore wetland vegetation, it is urgent to investigate the vegetation change and its response to climate change in the Songnen Plain marshes. Based on the normalized difference vegetation index (NDVI) and climate data, we investigated the spatiotemporal change of vegetation and its relationship with temperature and precipitation in the Songnen Plain marshes. During 2000–2016, the growing season mean NDVI of the Songnen Plain marshes significantly (p < 0.01) increased at a rate of 0.06/decade. For the climate change effects on vegetation, the growing season precipitation had a significant positive effect on the growing season NDVI of marshes. In addition, this study first found asymmetric effects of daytime maximum temperature (Tmax) and nighttime minimum temperature (Tmin) on NDVI of the Songnen Plain marshes: The growing season NDVI correlated negatively with Tmax but positively with Tmin. Considering the global asymmetric warming of Tmax and Tmin, more attention should be paid to these asymmetric effects of Tmax and Tmin on the vegetation of marshes.

scholarly journals Relative Contribution of Growing Season Length and Amplitude to Long-Term Trend and Interannual Variability of Vegetation Productivity over Northeast China

Forests ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 112 ◽  
Author(s):  
Yuke Zhou
Keyword(s):  
Northeast China ◽  
Vegetation Index ◽  
Growing Season ◽  
Maximum Growth ◽  
Coniferous Forests ◽  
Vegetation Productivity ◽  
Term Trend ◽  
Relative Contribution ◽  
Long Term Trend

In the context of global warming, the terrestrial ecosystem productivity over the Northern Hemisphere presents a substantially enhanced trend. The magnitude of summer vegetation maximum growth, known as peak growth, remains only partially understood for its role in regulating changes in vegetation productivity. This study aimed to estimate the spatiotemporal dynamics of the length of growing season (LOS) and maximum growth magnitude (MAG) over Northeast China (NEC) using a long-term satellite record of normalized difference vegetation index (NDVI) for the period 1982–2015, and quantifying their relative contribution to the long-term trend and inter-annual variability (IAV) of vegetation productivity. Firstly, the key phenological metrics, including MAG and start and end of growing season (SOS, EOS), were derived. Secondly, growing season vegetation productivity, measured as the Summary of Vegetation Index (VIsum), was obtained by cumulating NDVI values. Thirdly, the relative impacts of LOS and MAG on the trend and IAV in VIsum were explored using the relative importance (RI) method at pixel and vegetation cover type level. For the entire NEC, LOS, and MAG exhibited a slightly decreasing trend and a weak increasing trend, respectively, thus resulting in an insignificant change in VIsum. The temporal phases of VIsum presented a consistent pace with LOS, but changed asynchronously with MAG. There was an underlying cycle of about 10 years in the changes of LOS, MAG, and VIsum. At a regional scale, VIsum tended to maintain a rising trend in the northern coniferous forest and grassland in western and southern NEC. The spatial distribution of the temporal trends of LOS and MAG generally show a contrasting pattern, in which LOS duration is expected to shorten (negative trend) in the central cropland and in some southwestern grasslands (81.5% of the vegetated area), while MAG would increase (positive trend) in croplands, southern grasslands, and northern coniferous forests (16.5%). The correlation index for the entire NEC suggested that LOS was negatively associated with MAG, indicating that the extended vegetation growth duration would result in a lower growth peak and vice versa. Across the various vegetation types, LOS was a substantial factor in controlling both the trend and IAV of VIsum (RI = 75%). There was an opposite spatial pattern in the relative contribution of LOS and MAG to VIsum, where LOS dominated in the northern coniferous forests and in the eastern broadleaf forests, with MAG mainly impacting croplands and the western grasslands (RI = 27%). Although LOS was still the key factor controlling the trend and IAV of VIsum during the study period, this situation may change in the case peak growth amplitude gradually increases in the future.

scholarly journals Observational Evidences of Double Cropping Impacts on the Climate in the Northern China Plains

2012 ◽  
Vol 25 (13) ◽  
pp. 4721-4728 ◽  
Author(s):  
C.-H. Ho ◽  
S.-J. Park ◽  
S.-J. Jeong ◽  
J. Kim ◽  
J.-G. Jhun
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Regional Climate ◽  
Northern China ◽  
Specific Humidity ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Double Cropping

Abstract The impacts of harvested cropland in the double cropping region (DCR) of the northern China plains (NCP) on the regional climate are examined using surface meteorological data and the satellite-derived normalized difference vegetation index (NDVI) and land surface temperature (LST). The NDVI data are used to distinguish the DCR from the single cropping region (SCR) in the NCP. Notable increases in LST in the period May–June are found in the area identified as the DCR on the basis of the NDVI data. The difference between the mean daily maximum temperature averaged over the DCR and SCR stations peaks at 1.27°C in June. The specific humidity in the DCR is significantly smaller than in the SCR. These results suggest that the enhanced agricultural production by multiple cropping may amplify regional warming and aridity to further modify the regional climate in addition to the global climate change. Results in this study may also be used as a quantitative observed reference state of the crop/vegetation effects for future climate modeling studies.

It matters when you measure it: using snow-cover Normalised Difference Vegetation Index (NDVI) to isolate post-fire conifer regeneration

2018 ◽  
Vol 27 (12) ◽  
pp. 815 ◽  
Author(s):  
Melanie K. Vanderhoof ◽  
Todd J. Hawbaker
Keyword(s):  
Snow Cover ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Growing Season ◽  
Forest Recovery ◽  
Herbaceous Species ◽  
Herbaceous Vegetation ◽  
Local Climate ◽  
Normalised Difference Vegetation Index ◽  
Conifer Tree

Abstract. Landsat Normalised Difference Vegetation Index (NDVI) is commonly used to monitor post-fire green-up; however, most studies do not distinguish new growth of conifer from deciduous or herbaceous species, despite potential consequences for local climate, carbon and wildlife. We found that dual season (growing and snow cover) NDVI improved our ability to distinguish conifer tree presence and density. We then examined the post-fire pattern (1984–2017) in Landsat NDVI for fires that occurred a minimum of 20 years ago (1986–1997). Points were classified into four categories depending on whether NDVI, 20 years post-fire, had returned to pre-fire values in only the growing season, only under snow cover, in both seasons or neither. We found that each category of points showed distinct patterns of NDVI change that could be used to characterise the average pre-fire and post-fire vegetation condition Of the points analysed, 43% showed a between-season disagreement if NDVI had returned to pre-fire values, suggesting that using dual-season NDVI can modify our interpretations of post-fire conditions. We also found an improved correlation between 5- and 20-year NDVI change under snow cover, potentially attributable to snow masking fast-growing herbaceous vegetation. This study suggests that snow-cover Landsat imagery can enhance characterisations of forest recovery following fire.

scholarly journals Forest Phenology Dynamics and Its Responses to Meteorological Variations in Northeast China

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xinfang Yu ◽  
Qiankun Wang ◽  
Huimin Yan ◽  
Yong Wang ◽  
Kege Wen ◽  
...  
Keyword(s):  
Northeast China ◽  
Vegetation Index ◽  
Mixed Forest ◽  
Negative Relationship ◽  
Growing Season ◽  
Linear Regression Method ◽  
Temperate Steppe ◽  
Spring Temperature ◽  
Steppe Region ◽  
Forest Region

Based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data (2000–2009), we extracted forest phenological variables in Northeast China using a threshold-based method, which included the start of the growing season (SOS), end of the growing season (EOS), and length of the growing season (LOS). The spatial variation of phenological trends was analyzed using the linear regression method. In Northeast China, SOS was delayed at the rate of <1.5 days per year. The delay trend of EOS was well distributed in the entire region with almost the same rates. LOS increased slightly. The analysis of the relationship between forest phenology and meteorological variations shows that SOS was mainly affected by spring temperature, whereas SOS had a negative relationship with precipitation in the warm-temperate deciduous broadleaf forest region. The EOS in temperate steppe region was affected by temperature and precipitation in August, whereas the others were significantly affected by temperature. Because of the increased temperature in spring, the LOS of the temperate steppe region and temperate mixed forest region increased, and the LOS was positively correlated with the mean temperature of summer in the cool-temperate needleleaf forest region.

Monitoring snowpack evolution with meteorological reanalysis data in the Atlas Mountains

2021 ◽  
Author(s):  
Wassim Mohamed Baba ◽  
Abdelghani Boudhar ◽  
Simon Gascoin ◽  
Lahoucine Hanich ◽  
Ahmed Marchane ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Growing Season ◽  
Reanalysis Data ◽  
Water Runoff ◽  
Snow Cover Area ◽  
Seasonal Snow ◽  
Atlas Mountains ◽  
Snow Water ◽  
Seasonal Snow Cover

&lt;p&gt;The seasonal snow cover in the Altas mountains of Morocco is an important resource, mostly because it provides melt-water runoff for irrigation during the crop growing season. However, the knowledge on physical properties of the snowpack (e.g., snow water equivalent (SWE) and snowmelt) is still very limited due to the scarcity or the lack of ground measurements in the elevated area. In this study we suggest that the recent progresses of meteorological reanalysis data (e.g., MERRA-2 and ERA-5) open new perspectives to overcome this issue. We fed a distributed snowpack evolution model (SnowModel) with downscaled ERA-5 and MERRA-2 reanalyses and evaluate their performance to simulate snow cover. The modeling covers the period 1981 to 2019 (37 water years). SnowModel simulations were assessed using observations of river discharge, snow height and snow cover area derived from MODIS.&lt;/p&gt;&lt;p&gt;For most of hydrological years, the results show a good performance for both MERRA-2 and ERA-5 with a slight superiority of ERA-5, to reproduce the snowpack state.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Key words&lt;/strong&gt;: snow, snow water equivalent, reanalysis , MERRA-2, ERA-5&lt;/p&gt;

scholarly journals Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Feng Gao ◽  
Xiaoyang Zhang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Real Time ◽  
Land Surface ◽  
Vegetation Index ◽  
Growing Season ◽  
Crop Growth ◽  
Growth Stages ◽  
Crop Phenology ◽  
Crop Growth Stages

Crop phenology is critical for agricultural management, crop yield estimation, and agroecosystem assessment. Traditionally, crop growth stages are observed from the ground, which is time-consuming and lacks spatial variability. Remote sensing Vegetation Index (VI) time series has been used to map land surface phenology (LSP) and relate to crop growth stages mostly after the growing season. In recent years, high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season. This paper summarizes two classes of near-real-time mapping methods, i.e., curve-based and trend-based approaches. The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages. The curve-based approaches are capable of a short-term prediction. The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds. The trend-based approaches only use current observations. Both curve-based and trend-based approaches are promising in mapping crop growth stages timely. Nevertheless, mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages. The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season. Recent satellite datasets such as the harmonized Landsat and Sentinel-2 (HLS) are promising for mapping crop phenology within the season over large areas. Operational applications in the near future are feasible.

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