Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series

The characteristics of Normalized Difference Vegetation Index (NDVI) time series can be disaggregated into a set of quantitative metrics that may be used to derive information about vegetation phenology and land cover. In this paper, we examine the patterns observed in metrics calculated for a time series of 8 years over the southwest of Western Australia—an important crop and animal production area of Australia. Four analytical approaches were used; calculation of temporal mean and standard deviation layers for selected metrics showing significant spatial variability; classification based on temporal and spatial patterns of key NDVI metrics; metrics were analyzed for eight areas typical of climatic and production systems across the agricultural zone; and relationships between total production and productivity measured by dry sheep equivalents were developed with time integrated NDVI (TINDVI). Two metrics showed clear spatial patterns; the season duration based on the smooth curve produced seven zones based on increasing length of growing season; and TINDVI provided a set of classes characterized by differences in overall magnitude of response, and differences in response in particular years. Frequency histograms of TINDVI could be grouped on the basis of a simple shape classification: tall and narrow with high, medium or low mean indicating most land is responsive agricultural cover with uniform seasonal conditions; broad and short indicating that land is of mixed cover type or seasonal conditions are not spatially uniform. TINDVI showed a relationship to agricultural productivity that is dependent on the extent to which crop or total agricultural production was directly reduced by rainfall deficiency. TINDVI proved most sensitive to crop productivity for Statistical Local Areas (SLAs) having rainfall less than 600 mm, and in years when rainfall and crop production were highly correlated. It is concluded that metrics from standardized NDVI time series could be routinely and transparently used for retrospective assessment of seasonal conditions and changes in vegetation responses and cover.     

Identification of snow cover regimes through spatial and temporal clustering of satellite microwave brightness temperatures

Areas of similar ecology are often delineated based on homogenous topography, temperature, and land cover. Once delineated, these zones become the basis for a wide variety of scientific research and management activities. For instance, in Canada, ecozones are commonly utilized ecological management units delineated using geographic, topographic, and climatic information aided by spring and summer vegetation conditions. Snow cover has an influence on local and regional hydrological conditions and climate, as well as on animal habitats. As such, we posit that inclusion of winter conditions, incorporating spatial- and temporal-variation in snow cover is an additional element for consideration when delineating areas with homogenous conditions. In our analysis we use satellite passive microwave brightness temperatures from 19 years of Special Sensor Microwave/Imager (SSM/I) measurements to produce a daily time-series on snow cover, and demonstrate how these data can be used to delineate areas of similar winter conditions. We use splines and curve fitting to generalize the dense time-series (of over 6900 days) to a set of metrics, and select three for use in cluster-based generalization of snow cover regimes: annual maximum difference between 37 and 19 GHz SSM/I measurements (with differences in magnitudes indicative of snow accumulation), variation of 37-19 GHz brightness temperatures (indicative of snow cover variability), and variation in the rate of brightness temperature change during the snow melt season (indicative of seasonal change). Our results indicate that these metrics produce spatial units that are unique, and not captured by conventional ecological management units, while also producing spatial units that cohere to those generated from summer conditions. Spatial units that are found to have spatial cohesion between summer and winter data sources are located in regions where the amount of snow tends to be low, and snow cover variability minimal. We propose that snow cover regimes may be used to augment typical vegetation-based ecological zonations or to provide insights on hydrology and animal habitat conditions. Inclusion of winter conditions is especially important when areal delineations are used to monitor impacts of climate change, and as a baseline for monitoring changes in snow cover amount, extent, and/or distribution.     

1980~2019年北疆积雪时空变化与气候和植被的关系

利用1980~2019年中国长时间序列的AVHRR逐日无云积雪面积产品和气象站实测雪深资料计算积雪日数、积雪初日、积雪终日、积雪期、雪深等积雪物候参数,研究积雪物候的时空分布变化,同时结合ECMWF-ERA5再分析资料和GIMMS NDVI3g数据集分别提取气象因子（气温、降水）和植被因子（返青期、枯黄期、生长期）,探究北疆积雪物候变化对气象因子和植被因子的响应。结果表明：北疆近40 a间的平均积雪日数为81.62 d/a,73%的区域为稳定积雪区,积雪初日在11月、终日在3月,积雪期为每年11月初至次年3月底4月初;空间上呈现不均匀分布,其中阿勒泰山地区、天山地区、大部分塔城盆地和额尔齐斯谷地区为主要积雪区,1980~2019年间北疆积雪覆盖面积比例、积雪日数和积雪期逐年降低,积雪初日基本没变,但积雪终日显著提前;ECMWF-ERA5再分析资料表明1980~2019年北疆积雪期降水量无明显变化,但积雪覆盖面积比例显著降低,说明降雪区雪深可能增加,这与北疆气象站实测雪深逐渐增加结果相吻合;平均气温与积雪期积雪覆盖面积比例、积雪日数、积雪期长度相关性较大,呈现显著负相关,积雪期降水量与积雪物候参数呈现正相关;积雪物候及其气候效应引起北疆自然植被返青期显著提前,植被生长期延长的特征。     

Evaluation of passive microwave snow water equivalent algorithms in the depth hoar-dominated snowpack of the Kuparuk River Watershed, Alaska, USA

This research investigates the utility of passive microwave remote sensing instruments to accurately determine snow water equivalent (SWE) over large spatial extents. Three existing Special Sensor Microwave Imager (SSM/I) snow water equivalent algorithms produced by Chang, Tait and Goodison were evaluated for their ability to determine snow water equivalent in a snowpack containing substantial depth hoar, large faceted snow crystals. The Kuparuk River Watershed (8140 km²) test site on the North Slope of Alaska was chosen for its snowpack containing a think depth hoar layer and long history of ground truth data. A new regional snow water equivalent algorithm was developed to determine if it could produce better results than the existing algorithms in an area known to contain significant depth hoar. The four algorithms were tested to see how well they could determine snow water equivalent: (1) on a per pixel basis, (2) across swath-averaged spatial bands of approximately 850 km², and (3) on a watershed scale. The algorithms were evaluated to see if they captured the annual spatial distribution in snow water equivalent over the watershed. Results show that the algorithms developed by Chang and from this research are generally within 3 cm of the spatially averaged snow water equivalents over the entire watershed. The algorithms produced by Chang, Tait, and in this research were able to predict the basin-wide ground measured snow water equivalent value within a percent error range from −32.4% to 24.4% in the years with a typical snowpack. None of the algorithms produce accurate results on a pixel-by-pixel scale, with errors ranging from −26% to 308%.     

Relating NDVI to ecosystem CO2 exchange patterns in response to season length and soil warming manipulations in arctic Alaska

Climate change in the Arctic will differentially affect physiological rates, leaf phenology, and species composition of tundra, resulting in changing patterns and magnitudes of ecosystem CO₂ flux. The normalized difference vegetation index (NDVI) provides a potential means to infer changes in CO₂ flux, but whether relationships developed between NDVI and flux components can be generalized across the entire growing season and in response to changes induced by climate warming is uncertain. To investigate how well such changes might be assessed using multispectral digital images, ecosystem CO₂ fluxes and NDVI were compared throughout the 2002 growing season on experimental plots with increased growing season length and soil temperature at Toolik Lake, Alaska. Season length was increased by snow removal early in the season and soil temperatures were increased using heating cables. Carbon dioxide fluxes were measured using static chamber techniques and corresponding NDVI images were taken with an agricultural digital camera. The seasonal patterns of NDVI in all treatments showed an increase to a peak in early August followed by an abrupt decline, with the snow removal plots phenologically advanced compared to the controls. Net ecosystem production (NEP) showed uptake of CO₂ early in the season leveling out to a slight loss of CO₂ at peak season for both control and extended season plots. Gross primary productivity (GPP) closely followed the pattern of NDVI and the pattern of ecosystem respiration (R_e) mirrored that of GPP. NDVI was significantly correlated to GPP and ecosystem respiration (R² = 0.50 and 0.36 respectively) across plots, dates, and treatments combined. However, most of the covariation was across dates. After accounting for seasonal variation, NDVI never accounted for more than 25% of the remaining variation in flux measures. Analysis of covariance showed that a given NDVI value corresponded to different flux rates on different dates and to different R_e among treatments after correcting for date. The slopes of the NDVI-GPP and NDVI-Re relationships were much steeper across dates than across plots. These plot-scale results suggest that NDVI alone is not sufficient to estimate carbon flux rate responses to climate change across space or years.     

基于MODIS数据的我国天山典型区积雪特征研究

准确监测天山地区积雪面积和积雪日数对合理利用水资源及分析区域气候变化有重要意义。MODIS每日积雪产品可以为大面积快速积雪制图与监测提供依据,但因云量较高成为其应用的瓶颈。利用结合MODIS产品的时间与空间信息有效地减少了云对MODIS积雪产品的影响,并利用改进的MODIS积雪数据和DEM分析2002~2009年天山地区积雪面积和积雪日数的变化特征。结果表明:积雪频率总体上随着海拔升高而增大;不同坡向积雪面积差异明显,西北坡积雪覆盖率最高,北坡、西坡和东北坡次之,南坡和东南坡的积雪覆盖率最低;2006~2008年研究区积雪面积出现低值,年内最大积雪面积呈逐年减少的趋势;随着海拔下降,积雪日数逐渐变小,天山南部地区积雪日数仅为40 d以下;积雪日数大的区域年际积雪日数变化相对稳定,积雪日数少于40 d的区域积雪日数的变异系数最大,年际积雪日数变化不稳定。     

Recent trends of ice phenology for eight large lakes using MODIS products in Northeast China

Optical remote sensing images with high temporal resolution can be used to monitor lake ice phenology, a periodic freezing and thawing cycle of ice resulting from seasonal and inter-annual climate variations. In the research reported here, we used MODIS satellite data to establish the time series of lake ice extent and extracted lake ice phenology dates and durations for eight large typical lakes in Northeast China for the hydrological years from 2003 to 2016. The MODIS-derived results were validated against ice records at hydrological stations. The mean absolute error for a freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS) and break-up end (BUE) was 3.1, 4.8, 6.6 and 6.6 days, respectively. Our findings indicated that the investigated lakes were tending to freeze later and melt earlier and were frozen for a shortened period over time. FUS was experiencing a delay of 0.65 days per year and BUE was advancing by 0.19 days per year, implying a decrease of frozen duration (FD) of 0.84 days per year taking all eight lakes into consideration. The lake ice duration increased with latitude, and the lakes with a relatively smaller area had a higher yearly rate of change and were more variable compared with the larger ones. The relationship between lake ice phenology and other influencing factors was evaluated using correlation coefficients and partial least squares regression. The results showed that the freeze-up process was more dependent on the lake morphometry, while the break-up process was more dependent on climate changes, particularly on air temperature, which had the highest correlation coefficient (r = ?0.69, p < 0.01).     

Monitoring snowmelt across the Arctic forest–tundra ecotone using Synthetic Aperture Radar

Detailed snowpack observations, meteorology, topography and landcover classification were integrated with multi‐temporal SAR data to assess its capability for landscape scale snowmelt mapping at the forest–tundra ecotone. At three sites along an approximately 8° latitudinal gradient in the Fennoscandian mountain range, 16 multi‐temporal spaceborne ERS‐2 synthetic aperture radar (SAR) were used for mapping snowmelt.

Comparison of field measurements and backscatter values demonstrates the difficulty of interpreting observed backscatter response because of complex changes in snow properties on diurnal and seasonal temporal scales. Diurnal and seasonal melt–freeze effects in the snowpack, relative to the timing of ERS‐2 SAR image acquisition, effectively reduce the temporal resolution of such data for snow mapping, even at high latitudes.

The integration of diverse data sources did reveal significant associations between vegetation, topography and snowmelt. Several problems with the application of thresholding for the automatic identification of snowmelt were encountered. These largely related to changes in backscattering from vegetation in the late stages of snowmelt. Due to the impact of environmental heterogeneity in vegetation at the forest–tundra ecotone, we suggest that the potential to map snow cover using single polarization C‐band SAR at the forest–tundra ecotone may be limited to tundra areas.     

2000~2010年吉林省积雪时空变化特征及其与气候的关系

利用Terra卫星提供的2000年10月1日到2010年4月30日每日雪覆盖产品MOD10A1,提取研究区积雪覆盖指数SCI、积雪日数SCD、积雪初日SCOD及积雪终日SCMD遥感信息,结合同期吉林省界内23个地面气象观测站的同期气温和降水资料,分析该区积雪的变化特征与气温和降水的关系。结果表明:① 吉林省大部分地区积雪日数为30~90 d,东部山区积雪持续时间长、积雪初日日期早以及积雪终日日期晚,中西部地区变化情况相反;② 积雪覆盖指数SCI呈波浪式变化,与积雪季气温呈负相关;③ 积雪日数与气温呈反相关、与降水量呈正相关,与积雪季气温、夏季降水量的相关系数分别为-0.7407、0.6875;积雪初日情况相反,与积雪季气温、夏季平均气温为0.743、0.5479;积雪终日与气温呈反相关、与降水量呈正相关,与积雪季气温、夏季降水量为-0.5214、0.4647。积雪指数均对气温的变化更敏感,气温升高导致积雪初日推迟、积雪终日提前,从而使积雪日数减小;积雪季降水量的增加有利于积雪日数增大,而积雪终日的推迟有利于夏季降水量的增加。     

Hourly simulations of the microwave brightness temperature of seasonal snow in Quebec, Canada, using a coupled snow evolution-emission model

To interpret the snowpack evolution, and in particular to estimate snow water equivalent (SWE), passive microwave remote sensing has proved to be a useful tool given its sensitivity to snow properties. However, the main uncertainties using existing SWE algorithms arise from snow metamorphism which evolves during the winter season, and changes the snow emissivity. To consider the evolution in snow emissivity a coupled snow evolution-emission model can be used to simulate the brightness temperature (T_B) of the snowpack.During a dedicated campaign in the winter season, from November to April, of 2007-2008 two surface-based radiometers operating at 19 GHz and 37 GHz continuously measured the passive microwave radiation emitted through a seasonal snowpack in southern Quebec (Canada). This paper aims at modeling and interpreting this time series of T_B over the whole season, with an hourly step, using a coupled multi-layer snow evolution-emission model. The thermodynamic snow evolution model, referred as to Crocus, was driven by local meteorological measurements. Results from this model provided, in turn, the input variables to run the Microwave Emission Model of Layered Snowpacks (MEMLS) in order to predict T_B at 19 GHz and 37 GHz for both vertical (V) and horizontal (H) polarizations. The accuracy of T_B predicted by the Crocus-MEMLS coupled model was evaluated using continuous measurements from the surface-based radiometers.The weather conditions observed during the winter season were diverse, including several warm periods with melting snow and rain-on-snow events, producing very complex variations in the time series of T_B. To aid our analysis, we identified days with melting snow versus days with dry snow. The Crocus-MEMLS coupled model was able to accurately predict melt events with a success rate of 86%. The residual error was due to an overestimation of the duration of several melt events simulated by Crocus. This problem was explained by 1) a limitation of percolation, and 2) a very long-acting melt of lower layers due to geothermal flux.When the snowpack was completely dry, the global trend of T_B during the season was characterized by a decrease of T_B due to growth in the snow grain size. During most of the season, Crocus-MEMLS correctly predicted the evolution of T_B resulting from temperature gradient metamorphism; the root mean square errors ranged between 2.8 K for the 19 GHz vertical polarization (19V) and 6.9 K for the 37 GHz horizontal polarization (37H). However, during dry periods near the end of the season, the values of T_B were strongly overestimated. This overestimation was mainly due to a limitation of the growth of large snow grains in the wet snowpack simulated by Crocus. This effect was confirmed by estimating snow grain sizes from the observed T_B and the coupled model. The estimated snow grain sizes were larger and more realistic than those initially predicted by the Crocus model.