Assessing the Impact of Climate Variability on Cropland Productivity in the Canadian Prairies Using Time Series MODIS FAPAR

Dong, Ting; Liu, Jiangui; Shang, Jiali; Qian, Budong; Huffman, Ted; Zhang, Yinsuo; Champagne, Catherine; Daneshfar, Bahram

doi:10.3390/rs8040281

Cited by 21 publications

(10 citation statements)

References 59 publications

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“…Using this approach, a number of impact factors are used as covariates for spatially interpolating meteorological elements, greatly improving interpolation precision, and multiple surfaces can be interpolated simultaneously [29]. The program is particularly suited to interpolating time series meteorological data and has been widely applied in recent phenological research [30]. The measures of interpolation accuracy generated in the log file include the root of the generalized cross-validation (RTGCV), the root of the mean square error (RTMSE), the root of the mean square residual (RTMSR), and signal value.…”

Section: Climate Datamentioning

confidence: 99%

Climate and Spring Phenology Effects on Autumn Phenology in the Greater Khingan Mountains, Northeastern China

Zhao

et al. 2018

Remote Sensing

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Vegetation phenology plays a key role in terrestrial ecosystem nutrient and carbon cycles and is sensitive to global climate change. Compared with spring phenology, which has been well studied, autumn phenology is still poorly understood. In this study, we estimated the date of the end of the growing season (EOS) across the Greater Khingan Mountains, China, from 1982 to 2015 based on the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index third-generation (NDVI3g) dataset. The temporal correlations between EOS and climatic factors (e.g., preseason temperature, preseason precipitation), as well as the correlation between autumn and spring phenology, were investigated using partial correlation analysis. Results showed that more than 94% of the pixels in the Greater Khingan Mountains exhibited a delayed EOS trend, with an average rate of 0.23 days/y. Increased preseason temperature resulted in earlier EOS in most of our study area, except for the semi-arid grassland region in the south, where preseason warming generally delayed EOS. Similarly, EOS in most of the mountain deciduous coniferous forest, forest grassland, and mountain grassland forest regions was earlier associated with increased preseason precipitation, but for the semi-arid grassland region, increased precipitation during the preseason mainly led to delayed EOS. However, the effect of preseason precipitation on EOS in most of the Greater Khingan Mountains was stronger than that of preseason temperature. In addition to the climatic effects on EOS, we also found an influence of spring phenology on EOS. An earlier SOS led to a delayed EOS in most of the study area, while in the southern of mountain deciduous coniferous forest region and northern of semi-arid grassland region, an earlier SOS was often followed by an earlier EOS. These findings suggest that both climatic factors and spring phenology should be incorporated into autumn phenology models in order to improve prediction accuracy under present and future climate change scenarios.

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Section: Climate Datamentioning

confidence: 99%

Climate and Spring Phenology Effects on Autumn Phenology in the Greater Khingan Mountains, Northeastern China

Zhao

et al. 2018

Remote Sensing

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“…The Parametric Double Hyperbolic Tangent (PDHT) mathematic model was employed to fit the daily EVI2 in order to derive phenological indicators. It has proven to be well suited for reconstruction of daily f APAR in the Canadian Prairies [45]. The fitted daily EVI2 can be expressed as:…”

Section: Extraction Of Sos From Evi2mentioning

confidence: 99%

Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada

et al. 2019

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Information on crop seeding date is required in many applications; such as crop management and yield forecasting. This study presents a novel method to estimate crop seeding date at the field level from time-series 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data and growing degree days (GDD; base 5 °C; °C-days). The start of growing season (SOS) was first derived from time-series EVI2 (two-band Enhanced Vegetation Index) calculated from a MODIS 8-day composite surface reflectance product (MOD09Q1; Collection 6). Based on GDD; calculated from the Daymet gridded estimates of daily weather parameters; a simple model was developed to establish a linkage between the observed seeding date and the SOS. Calibration and validation of the model was conducted on three major crops; spring wheat; canola and oats; in the Province of Manitoba; Canada. The estimated SOS had a strong linear correlation with the observed seeding date; with a deviation of a few days depending on the year. The seeding date of the three crops can be calculated from the SOS by adjusting the number of days needed to accumulate GDD (AGDD) for emergence. The overall root-mean-square-difference (RMSD) of the estimated seeding date was less than 10 days. Validation showed that the accuracy of the estimated seeding date was crop-type independent. The developed method is useful for estimating the historical crop seeding date from remote sensing data in Canada; to support studies of the interactions among seeding date; crop management and crop yield under climate change. It is anticipated that this method can be adapted to other crops in other locations using the same or different satellite data.

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“…Thus, ANUSPLIN can process two or more splines smoothly, which allows the introduction of multiple impact factors as covariates for spatial interpolation of meteorological elements. More importantly, it can perform spatial interpolation of multiple surfaces at the same time, which is especially suitable for meteorological time series data [44][45][46]. ANUSPLIN has been widely used internationally.…”

Section: Snow Depth Datamentioning

confidence: 99%

The Effect of Snow Depth on Spring Wildfires on the Hulunbuir from 2001–2018 Based on MODIS

Hong

Zhang

et al. 2019

Remote Sensing

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Wildfires are one of the important disturbance factors in natural ecosystems and occur frequently around the world. Detailed research on the impact of wildfires is crucial not only for the development of livestock husbandry but also for the sustainable use of natural resources. In this study, based on the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product MC464A1 and site snow depth measurements, the kernel density estimation method (KDE), unary linear regression analysis, Sen + Mann-Kendall trend analysis, correlation analysis, and R/S analysis were used to evaluate the relationship between snow and spring wildfires (SWFs) in Hulunbuir. Our results indicated that SWFs decreased during the period of 2001–2018, were mainly distributed in the eastern portion of the study area, and that the highest SWF density was 7 events/km2. In contrast, the maximum snow depth increased during the period of 2001–2018 and the snow depth was deeper in the middle but shallower in the east and west. The SWFs and snow depth have significant negative correlations over space and time. The snow depth mainly affects the occurrence of SWFs indirectly by affecting the land surface temperature (LST) and Land Surface Water Index (LSWI) in spring. The snow depth was positively correlated with the LSWI in most of Hulunbuir and strongly negatively correlated with the LST, and this correlation was stronger in the eastern and western regions of Hulunbuir. The results of the Hurst exponent indicated that in the future, the snow depth trend will be opposite that of the current state, meaning that the trend of decreasing snow depth will increase dramatically in most of the study area, and SWFs may become more prominent. According to the validation results, the Hurst exponent is a reliable method for predicting the snow depth tendency. This research can be based on the snow conditions of the previous year to identify areas where fires are most likely to occur, enabling an improved and more targeted preparation for spring fire prevention. Additionally, the present study expands the theory and methods of wildfire occurrence research and promotes research on disasters and disaster chains.

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Assessing the Impact of Climate Variability on Cropland Productivity in the Canadian Prairies Using Time Series MODIS FAPAR

Cited by 21 publications

References 59 publications

Climate and Spring Phenology Effects on Autumn Phenology in the Greater Khingan Mountains, Northeastern China

Climate and Spring Phenology Effects on Autumn Phenology in the Greater Khingan Mountains, Northeastern China

Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada

The Effect of Snow Depth on Spring Wildfires on the Hulunbuir from 2001–2018 Based on MODIS

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