Abstract

We demonstrate the synergistic use of surface air temperature retrieved from AMSR-E (Advanced Microwave Scanning Radiometer on Earth observing satellite) and two vegetation indices (VIs) from the shorter wavelengths of MODIS (MODerate resolution Imaging Spectroradiometer) to characterize cropland phenology in the major grain production areas of Northern Eurasia from 2003–2010. We selected 49 AMSR-E pixels across Ukraine, Russia, and Kazakhstan, based on MODIS land cover percentage data. AMSR-E air temperature growing degree-days (GDD) captures the weekly, monthly, and seasonal oscillations, and well correlated with station GDD. A convex quadratic (CxQ) model that linked thermal time measured as growing degree-days to accumulated growing degree-days (AGDD) was fitted to each pixel’s time series yielding high coefficients of determination (0.88 ≤ r2 ≤ 0.98). Deviations of observed GDD from the CxQ model predicted GDD by site corresponded to peak VI for negative residuals (period of higher latent heat flux) and low VI at beginning and end of growing season for positive residuals (periods of higher sensible heat flux). Modeled thermal time to peak, i.e., AGDD at peak GDD, showed a strong inverse linear trend with respect to latitude with r2 of 0.92 for Russia and Kazakhstan and 0.81 for Ukraine. MODIS VIs tracked similar seasonal responses in time and space and were highly correlated across the growing season with r2 > 0.95. Sites at lower latitude (≤49°N) that grow winter and spring grains showed either a bimodal growing season or a shorter unimodal winter growing season with substantial inter-annual variability, whereas sites at higher latitude (≥56°N) where spring grains are cultivated exhibited shorter, unimodal growing seasons. Sites between these extremes exhibited longer unimodal growing seasons. At some sites there were shifts between unimodal and bimodal patterns over the study period. Regional heat waves that devastated grain production in 2007 in Ukraine and in 2010 in Russia and Kazakhstan appear clearly anomalous. Microwave based surface air temperature data holds great promise to extend to parts of the planet where the land surface is frequently obscured by clouds, smoke, or aerosols, and where routine meteorological observations are sparse or absent.

Highlights

  • About 10% of the earth’s terrestrial surface (1.5 billion ha) is covered by crops, both irrigated and rainfed [1]

  • Growing degree-day (GDD) from both sensors were compared with available nearby meteorological station calculated from MODIS land surface temperature (LST) 8-day composites were multiplied by eight to rescale them into the daily air temperature GDD to check for consistency using linear regression model

  • We present the accumulating daily GDDs over eight days (AMSR-E) thermal time to peak (TTP) (AGDD) in relation to latitude

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Summary

Introduction

About 10% of the earth’s terrestrial surface (1.5 billion ha) is covered by crops, both irrigated and rainfed [1]. The theoretical basis of using remotely sensed information to monitor crop growth and development was based on United States Department of Agriculture (USDA) research in the 1960s through the. The enhanced vegetation index (EVI; [18]) was designed to overcome this loss of sensitivity over dense vegetation, adjust for soil background effects, and reduce atmospheric effects in the red band by including information from the blue portion of the spectrum [18] These vegetation indices—NDVI and EVI—are complementary for global vegetation studies and together improve detection of changes in surface vegetation and extraction of canopy biophysical variables [18,19]. We explore whether the synergistic use of vegetation indices from visible and near infrared (VNIR) reflectance and surface air temperature data retrieved through microwave radiometry can improve the characterization of cropland phenology at the mid-latitudes.

Remote Sensing Data
In Situ Data
Study Region
Land and northern
29 March to 31and
Land Surface Seasonalities of Growing Degree-Days
Comparisons of GDD Derived from Satellite and Station Data
Root mean
Scatter
Latitudinal
TimeDuring
Land Surface
LandofSurface of Vegetation the responses peakPhenologies
Interannual Variation in Land
Sensor-Specific Differences in Thermal Time to Peak VI
Shifting
Heat Wave Impacts on LSPs
Conclusions
Full Text
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