Figure 1.
Land cover stability in Ukraine, southern Russia, and northern Kazakhstan as revealed by IGBP global land cover classification scheme MODIS 0.05° land cover products (resampled to AMSR-E spatial resolution: 0.25°) from 2003–2010: (
a) crop–natural vegetation mosaic (IGBP class 14); (
b) cropland (IGBP class 12); and (
c) grassland (IGBP class 10). Land cover percentage from 2003–2010 displayed as red = maximum percentage, green = mean percentage, and blue = range of percentages. For legend, refer to
Table 2.
Figure 1.
Land cover stability in Ukraine, southern Russia, and northern Kazakhstan as revealed by IGBP global land cover classification scheme MODIS 0.05° land cover products (resampled to AMSR-E spatial resolution: 0.25°) from 2003–2010: (
a) crop–natural vegetation mosaic (IGBP class 14); (
b) cropland (IGBP class 12); and (
c) grassland (IGBP class 10). Land cover percentage from 2003–2010 displayed as red = maximum percentage, green = mean percentage, and blue = range of percentages. For legend, refer to
Table 2.
Figure 2.
Study region cropland stability map superimposed with the 49 specific AMSR-E pixels selected for this study. The AMSR-E pixels are numbered by latitude starting from the most southern site. Name for each site is their closest large settlement (cf.
Table 3). Red squares are in Ukraine, cyan squares in Russia, and blue squares in Kazakhstan.
Figure 2.
Study region cropland stability map superimposed with the 49 specific AMSR-E pixels selected for this study. The AMSR-E pixels are numbered by latitude starting from the most southern site. Name for each site is their closest large settlement (cf.
Table 3). Red squares are in Ukraine, cyan squares in Russia, and blue squares in Kazakhstan.
Figure 3.
Scatter plots and linear regression fits of station GDD with satellite GDD—AMSR-E GDD (black circles) and MODIS GDD (blue diamonds)—at Simferopol, Ukraine (site 4) for 2003. The linear regression fit for the two datasets were high with r2 of 0.93 for the AMSR-E GDD and r2 of 0.89 for the MODIS GDD.
Figure 3.
Scatter plots and linear regression fits of station GDD with satellite GDD—AMSR-E GDD (black circles) and MODIS GDD (blue diamonds)—at Simferopol, Ukraine (site 4) for 2003. The linear regression fit for the two datasets were high with r2 of 0.93 for the AMSR-E GDD and r2 of 0.89 for the MODIS GDD.
Figure 4.
(left) Time series plot of AMSR-E (black) and station (red) daily GDD as a function of station AGDD for 2003 at Kirovohrad, Ukraine (site 15); and (right) linear regression fit of AMSR-E GDD with station GDD for the same dataset yielding strong correspondence with r2 = 0.95. Note also the bias (intercept) and the underestimation (slope < 1) of the AMSR-E GDD relative to the station GDD.
Figure 4.
(left) Time series plot of AMSR-E (black) and station (red) daily GDD as a function of station AGDD for 2003 at Kirovohrad, Ukraine (site 15); and (right) linear regression fit of AMSR-E GDD with station GDD for the same dataset yielding strong correspondence with r2 = 0.95. Note also the bias (intercept) and the underestimation (slope < 1) of the AMSR-E GDD relative to the station GDD.
Figure 5.
Average (2003–2010) MODIS (plus signs) and AMSR-E (circles) GDD and their fitted (solid (MODIS LST) and dashed (AMSR-E ta) lines) average GDD as a function of DOY (left) and AGDD (right) for two cropland sites at the latitudinal extremes of the study region: Cheboksary, Russia (site 48, 55.7°N; (a,b)); and Cherkessk, Russia (site 1, 44.4°N; (c,d)). N.B.: MODIS GDDs are multiplied by 8, while AMSR-E GDDs are eight-day sums.
Figure 5.
Average (2003–2010) MODIS (plus signs) and AMSR-E (circles) GDD and their fitted (solid (MODIS LST) and dashed (AMSR-E ta) lines) average GDD as a function of DOY (left) and AGDD (right) for two cropland sites at the latitudinal extremes of the study region: Cheboksary, Russia (site 48, 55.7°N; (a,b)); and Cherkessk, Russia (site 1, 44.4°N; (c,d)). N.B.: MODIS GDDs are multiplied by 8, while AMSR-E GDDs are eight-day sums.
Figure 6.
Thermal climates as a function of latitude revealed by: (a) average daily GDD; and (b) Thermal Time to Peak (TTPGDD). Latitudes were the geographic centers of AMSR-E pixels. All 49 study sites are displayed in both figures. In (b), hollow circles = Russia, orange diamonds = Kazakhstan, and cyan crosses on blue background = Ukraine. Both panels show a general decrease in (a) GDD or (b) TTP as latitude increases from 44° to 56°N. The uppermost two hollow circles are the northernmost study sites (site 49, Kazan’, Russia and site 48, Cheboksary, Russia), while the lowermost two hollow circles are the southernmost study sites (site 1, Cherkessk, Russia and 2, Stavropol, Russia).
Figure 6.
Thermal climates as a function of latitude revealed by: (a) average daily GDD; and (b) Thermal Time to Peak (TTPGDD). Latitudes were the geographic centers of AMSR-E pixels. All 49 study sites are displayed in both figures. In (b), hollow circles = Russia, orange diamonds = Kazakhstan, and cyan crosses on blue background = Ukraine. Both panels show a general decrease in (a) GDD or (b) TTP as latitude increases from 44° to 56°N. The uppermost two hollow circles are the northernmost study sites (site 49, Kazan’, Russia and site 48, Cheboksary, Russia), while the lowermost two hollow circles are the southernmost study sites (site 1, Cherkessk, Russia and 2, Stavropol, Russia).
Figure 7.
Line plots of annual observed GDD (blue), annual predicted GDD based on multi-year average model (orange) and observed GDD residuals (green) at Orenburg (RU) for: a cooler year (2003) (left); a close-to-average year (2009) (center); and a hotter year (2010) (right). N.B: GDDs are 8-day sums.
Figure 7.
Line plots of annual observed GDD (blue), annual predicted GDD based on multi-year average model (orange) and observed GDD residuals (green) at Orenburg (RU) for: a cooler year (2003) (left); a close-to-average year (2009) (center); and a hotter year (2010) (right). N.B: GDDs are 8-day sums.
Figure 8.
Average MODIS NDVI and EVI as a function of AMSR-E AGDD for Petropavlovsk 3, Kazakhstan (site 42) for 2003–2010. Note that the NDVI displays a larger dynamic range than the EVI.
Figure 8.
Average MODIS NDVI and EVI as a function of AMSR-E AGDD for Petropavlovsk 3, Kazakhstan (site 42) for 2003–2010. Note that the NDVI displays a larger dynamic range than the EVI.
Figure 9.
(a–d)In 2003, lower latitude sites (Cherkessk, Russia: 44.4°N (a) and Simferopol, Ukraine: 45.6°N (c) display bimodal growing seasons, while the higher latitude sites show unimodal, shorter growing seasons (Kazan’, Russia: 56.1°N (d)). The middle latitude sites display a longer unimodal growing season (Odesa, Ukraine: 47.3°N (b)).
Figure 9.
(a–d)In 2003, lower latitude sites (Cherkessk, Russia: 44.4°N (a) and Simferopol, Ukraine: 45.6°N (c) display bimodal growing seasons, while the higher latitude sites show unimodal, shorter growing seasons (Kazan’, Russia: 56.1°N (d)). The middle latitude sites display a longer unimodal growing season (Odesa, Ukraine: 47.3°N (b)).
Figure 10.
NDVI and EVI interannual variability in one of the southernmost study sites (Simferopol, Ukraine at 45.6°N) from 2003–2010. Whether due to changes in cultivation practice or crop failures, the VI curves change from bimodal (2003–2004) to unimodal (2005–2009) and back to bimodal (2010).
Figure 10.
NDVI and EVI interannual variability in one of the southernmost study sites (Simferopol, Ukraine at 45.6°N) from 2003–2010. Whether due to changes in cultivation practice or crop failures, the VI curves change from bimodal (2003–2004) to unimodal (2005–2009) and back to bimodal (2010).
Figure 11.
Changes in seasonal patterns by sites during 2003–2010: no change (blue circles), one change (white stars with pink borders), or two changes (red squares). Northern study sites displayed no change in seasonal pattern, while southern study sites experienced multiple changes.
Figure 11.
Changes in seasonal patterns by sites during 2003–2010: no change (blue circles), one change (white stars with pink borders), or two changes (red squares). Northern study sites displayed no change in seasonal pattern, while southern study sites experienced multiple changes.
Figure 12.
(a–f) Comparison of AMSR-E AGDDta at 95% of the initial peak VIs with the MODIS AGDDlst at 95% of the initial peak VIs in 2003. The panels span a latitudinal gradient: lowest latitude (Cherkessk, Russia (a,d)), middle latitude (Sumy, Ukraine (b,e)), and highest latitude (Kazan’, Russia (c,f)) for 95% of the initial peak NDVI (a–c) and EVI (d–f). There is strong linear relationship between AGDDs from AMSR-E and MODIS at 95% of initial peak VIs for these sites in 2003 with r2 ranging from 0.88 to >0.99. This strong positive relationship was consistent in space and time with r2 ranging from 0.60 to >0.99 across all study sites and years.
Figure 12.
(a–f) Comparison of AMSR-E AGDDta at 95% of the initial peak VIs with the MODIS AGDDlst at 95% of the initial peak VIs in 2003. The panels span a latitudinal gradient: lowest latitude (Cherkessk, Russia (a,d)), middle latitude (Sumy, Ukraine (b,e)), and highest latitude (Kazan’, Russia (c,f)) for 95% of the initial peak NDVI (a–c) and EVI (d–f). There is strong linear relationship between AGDDs from AMSR-E and MODIS at 95% of initial peak VIs for these sites in 2003 with r2 ranging from 0.88 to >0.99. This strong positive relationship was consistent in space and time with r2 ranging from 0.60 to >0.99 across all study sites and years.
Figure 13.
Average GDD residuals and NDVI with error bars displaying maxima and minima for sites at similar latitude. (a) Sites 35–37 and 39–41 in Kazakhstan (NDVI—blue triangles and GDD residuals—black diamonds) and sites 34 and 42 in Kazakhstan (NDVI—green crosses and GDD residuals—gray circles); and (b,c) sites 1 and 2 in Russia (NDVI—blue diamonds and GDD residuals—black circles) for selected bimodal cropland pattern years and unimodal years, respectively.
Figure 13.
Average GDD residuals and NDVI with error bars displaying maxima and minima for sites at similar latitude. (a) Sites 35–37 and 39–41 in Kazakhstan (NDVI—blue triangles and GDD residuals—black diamonds) and sites 34 and 42 in Kazakhstan (NDVI—green crosses and GDD residuals—gray circles); and (b,c) sites 1 and 2 in Russia (NDVI—blue diamonds and GDD residuals—black circles) for selected bimodal cropland pattern years and unimodal years, respectively.
Figure 14.
Comparison of VIs and GDD at similar places during heat wave years (Kuybuskev 2 (a), Russia in 2010 and Mykolayive (b), Ukraine in 2007) and average years (Kuybuskev 2 (c), Russia in 2005 and Mykolayive (d), Ukraine in 2009). N.B: GDDs are eight-day sums.
Figure 14.
Comparison of VIs and GDD at similar places during heat wave years (Kuybuskev 2 (a), Russia in 2010 and Mykolayive (b), Ukraine in 2007) and average years (Kuybuskev 2 (c), Russia in 2005 and Mykolayive (d), Ukraine in 2009). N.B: GDDs are eight-day sums.
Figure 15.
Sites affected by: the 2010 heat wave (blue circles), the 2007 heat wave (red squares), both the 2010 and 2007 heat waves (white stars with pink borders), and sites affected by neither the 2010 nor the 2007 heat wave (cyan triangles).
Figure 15.
Sites affected by: the 2010 heat wave (blue circles), the 2007 heat wave (red squares), both the 2010 and 2007 heat waves (white stars with pink borders), and sites affected by neither the 2010 nor the 2007 heat wave (cyan triangles).
Table 1.
Percentage of days with meteorological station data missing during the growing season.
Table 1.
Percentage of days with meteorological station data missing during the growing season.
Year/Site | Simferopol, UA | Odesa, UA | Mykolayiv, UA | Kirovohrad, UA | Kharkiv 2, UA | Saratov 4, RU |
---|
Tmax | Tmin | Tmax | Tmin | Tmax | Tmin | Tmax | Tmin | Tmax | Tmin | Tmax | Tmin |
---|
2003 | 0 | 0 | 4 | 6 | 0 | 0 | 0 | 2 | 3 | 4 | 0 | 0 |
2004 | 57 | 51 | 70 | 70 | 58 | 52 | 58 | 52 | 58 | 53 | 0 | 0 |
2005 | 59 | 41 | 61 | 41 | 60 | 43 | 59 | 42 | 59 | 41 | 0 | 0 |
2006 | 44 | 21 | 49 | 28 | 45 | 22 | 44 | 22 | 44 | 22 | 0 | 0 |
2007 | 68 | 56 | 68 | 59 | 68 | 59 | 70 | 59 | 68 | 55 | 0 | 0 |
2008 | 70 | 55 | 71 | 56 | 71 | 56 | 71 | 56 | 70 | 55 | 0 | 0 |
2009 | 90 | 82 | 91 | 84 | 91 | 84 | 90 | 86 | 90 | 82 | 0 | 0 |
2010 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 0 | 0 |
Table 2.
Interpretative legend for
Figure 1 that display IGBP MODIS 0.05° land cover variation from 2003–2010 in the study region. The table shows how the color in the LC map (
Figure 1) arises from the false color composite of red, green, and blue color planes that display, respectively, the maximum percentage of LC class, the average percentage of LC class, and the range of percentages of LC class over the study period. Source: [
50].
Table 2.
Interpretative legend for Figure 1 that display IGBP MODIS 0.05° land cover variation from 2003–2010 in the study region. The table shows how the color in the LC map (Figure 1) arises from the false color composite of red, green, and blue color planes that display, respectively, the maximum percentage of LC class, the average percentage of LC class, and the range of percentages of LC class over the study period. Source: [50].
Color in LC Map | Red = Max% LC | Green = Mean% LC | Blue = Range% LC | Interpretation |
---|
Black | None | None | None | Land cover (LC) class absent |
Blues | Low | Low | High | Unstable but ephemeral periphery; rare and erratic |
Magentas | High | Low | High | Unstable but persistent periphery; sometimes high, but usually low |
Whites | High | High | High | Unstable core; sometimes low, but usually high |
Yellows | High | High | Low | Stable core of LC; always high so low range |
Table 3.
Description of 49 study sites named by their closest town and country, numbered from lower latitude (1) to higher latitude (49), their geographic coordinates, and average cropland (CRP) cover percent and range (2003–2010). Note sites with 100% average CRP cover throughout the study period (bold), and larger CRP cover percent range (underlined).
Table 3.
Description of 49 study sites named by their closest town and country, numbered from lower latitude (1) to higher latitude (49), their geographic coordinates, and average cropland (CRP) cover percent and range (2003–2010). Note sites with 100% average CRP cover throughout the study period (bold), and larger CRP cover percent range (underlined).
Site No. | Name | Latitude | Longitude | Cropland (%) | Range (%) | Site No. | Name | Latitude | Longitude | Cropland (%) | Range (%) |
---|
1 | Cherkessk, RU | 44.4 | 43.5 | 99 | 0.6 | 26 | Kursk, RU | 52.1 | 37.5 | 95 | 4.1 |
2 | Stavropol, RU | 45.0 | 42.4 | 100 | 0.3 | 27 | Orenburg, RU | 52.4 | 55.2 | 94 | 4.7 |
3 | Krasnodar, RU | 45.6 | 39.6 | 98 | 0.6 | 28 | Kokshetau 1, KZ | 52.7 | 69.2 | 91 | 8.5 |
4 | Simferopol’, UA | 45.6 | 34.1 | 100 | 1.2 | 29 | Barnaul 2, RU | 52.7 | 83.0 | 99 | 0.7 |
5 | Tulcea, UA | 45.8 | 29.2 | 96 | 0.3 | 30 | Kuybyskev 2, RU | 52.7 | 50.2 | 91 | 9.6 |
6 | Rostov-on-Don 2, RU | 46.7 | 39.8 | 100 | 0.2 | 31 | Orel, RU | 52.7 | 35.7 | 64 | 2.7 |
7 | Odesa, UA | 47.3 | 30.7 | 100 | 0.0 | 32 | Kokshetau 2, KZ | 53.0 | 67.4 | 79 | 10.6 |
8 | Rostov-on-Do 1, RU | 47.5 | 40.9 | 88 | 6.2 | 33 | Lipetsk, RU | 53.0 | 39.1 | 84 | 4.4 |
9 | Donets’k, UA | 47.5 | 37.7 | 100 | 0.7 | 34 | Kokshetau 3, KZ | 53.7 | 68.2 | 84 | 10.0 |
10 | Mykolayiv, UA | 47.5 | 32.3 | 100 | 0.1 | 35 | Kostanay 1, KZ | 53.7 | 63.3 | 78 | 8.0 |
11 | Zaporiyhzhya 1, UA | 47.8 | 35.7 | 100 | 0.1 | 36 | Kostanay 2, KZ | 53.7 | 62.2 | 86 | 18.5 |
12 | Zaporiyhzhya 2, UA | 48.1 | 34.1 | 100 | 0.0 | 37 | Kurgan, KZ | 53.7 | 65.6 | 74 | 17.3 |
13 | Luhans’k, RU | 48.7 | 40.4 | 99 | 2.1 | 38 | Barnaul_1, RU | 53.7 | 79.4 | 82 | 11.3 |
14 | Volgograd, RU | 48.7 | 44.8 | 46 | 13.4 | 39 | Kokshetau 4, KZ | 54.0 | 69.0 | 93 | 4.7 |
15 | Kirovohrad, UA | 48.7 | 31.8 | 99 | 1.0 | 40 | Kostanay 3, KZ | 54.0 | 64.0 | 79 | 15.7 |
16 | Kharkiv 2, UA | 49.0 | 36.2 | 91 | 6.6 | 41 | Petropavlovsk 2, KZ | 54.4 | 70.8 | 97 | 3.6 |
17 | Khmel’nyts’kyz, UA | 49.0 | 26.8 | 88 | 11.0 | 42 | Petropavlovsk 3, KZ | 54.4 | 67.4 | 92 | 5.4 |
18 | Vinnytsya, UA | 49.0 | 28.9 | 96 | 0.5 | 43 | Kuybyskev 1, RU | 54.4 | 50.8 | 95 | 8.8 |
19 | Poltava, UA | 49.6 | 35.1 | 97 | 1.6 | 44 | Ryazan, RU | 54.4 | 39.3 | 65 | 13.7 |
20 | Kharkiv 1, UA | 49.9 | 37.0 | 72 | 3.9 | 45 | Petropavlovsk 1, KZ | 54.7 | 69.5 | 86 | 4.4 |
21 | Saratov 1, RU | 50.8 | 46.9 | 69 | 27.9 | 46 | Omsk 1, RU | 54.7 | 72.9 | 61 | 18.7 |
22 | Sumy, UA | 50.8 | 34.1 | 85 | 7.1 | 47 | Omsk 2, RU | 55.0 | 74.5 | 59 | 14.5 |
23 | Semipalatinsk, RU | 51.4 | 81.7 | 98 | 3.3 | 48 | Cheboksary, RU | 55.7 | 47.1 | 93 | 6.1 |
24 | Voronezh, RU | 51.4 | 39.8 | 98 | 1.7 | 49 | Kazan’, RU | 56.1 | 49.5 | 81 | 2.3 |
25 | Saratov 4, RU | 51.8 | 45.3 | 88 | 3.6 | |
Table 4.
Average (2003–2010) MODIS land cover with the 10 km buffer zone for meteorological stations and corresponding AMSR-E pixel study sites. Bold indicates the dominant land cover (all sites are in crop dominated areas).
Table 4.
Average (2003–2010) MODIS land cover with the 10 km buffer zone for meteorological stations and corresponding AMSR-E pixel study sites. Bold indicates the dominant land cover (all sites are in crop dominated areas).
Site | LULC (Stations) | LULC (AMSR-E Pixels) |
---|
CRP | CNVM | UBU | CRP | CNVM | UBU |
---|
Simferopol, UA | 97.9 | 0.0 | 2.1 | 99.7 | 0.0 | 0.0 |
Odesa, UA | 100.0 | 0.0 | 0.0 | 100.0 | 0.0 | 0.0 |
Mykolaiv, UA | 98.5 | 0.0 | 1.5 | 99.6 | 0.0 | 0.0 |
Kirovohrad, UA | 99.5 | 0.3 | 0.0 | 98.7 | 0.2 | 0.0 |
Kharkiv_2, UA | 91.5 | 0.7 | 7.4 | 91.0 | 0.3 | 3.4 |
Saratov_4, RU | 97.3 | 2.7 | 0.0 | 88.4 | 11.4 | 0.0 |
Table 5.
Root mean square error (RMSE), linear regression model (intercept, slope and r2), and analysis of covariance (ANCOVA) between station and satellite GDDs from AMSR-E and MODIS sensors. AMS = AMSR-E; MOD = MODIS.
Table 5.
Root mean square error (RMSE), linear regression model (intercept, slope and r2), and analysis of covariance (ANCOVA) between station and satellite GDDs from AMSR-E and MODIS sensors. AMS = AMSR-E; MOD = MODIS.
Site | RMSE * | Intercept | Slope | r2 | ANCOVA (p Value) |
---|
AMS | MOD | AMS | MOD | AMS | MOD | AMS | MOD | Slope | Intercept |
---|
Simferopol, UA | 13.77 | 29.89 | 16.84 | 17.71 | 0.95 | 1.05 | 0.95 | 0.88 | 0.447 | <0.05 |
Odesa, UA | 23.95 | 24.19 | 31.88 | 8.29 | 0.88 | 1.06 | 0.87 | 0.88 | 0.177 | 0.080 |
Mykolaiv, UA | 20.56 | 23.81 | 25.82 | 0.74 | 0.92 | 1.13 | 0.92 | 0.92 | <0.05 | 0.224 |
Kirovohrad, UA | 18.34 | 22.25 | 23.58 | −1.70 | 0.92 | 1.11 | 0.93 | 0.89 | 0.096 | 0.328 |
Kharkiv_2, UA | 17.41 | 18.23 | 28.80 | −5.94 | 0.83 | 1.09 | 0.91 | 0.91 | <0.05 | 0.231 |
Saratov_4, RU | 20.22 | 16.31 | 32.52 | −1.76 | 0.84 | 1.06 | 0.96 | 0.92 | <0.05 | <0.05 |
Table 6.
Peak GDDs and VIs and corresponding AGDDs during heat wave years and average years at two representative sites.
Table 6.
Peak GDDs and VIs and corresponding AGDDs during heat wave years and average years at two representative sites.
Site | Weather | Metric | 1st Peak | 2nd Peak |
---|
Value | AGDD | Value | AGDD |
---|
Kuybyskev_2, RU | Average | GDD | 180.00 | 722 | 190.00 | 2060 |
NDVI | 0.59 | 1230 | | |
EVI | 0.34 | 1230 | | |
Kuybyskev_2, RU | Heat wave | GDD | 250.00 | 2480 | | |
NDVI | 0.51 | 960 | | |
EVI | 0.26 | 960 | | |
Mykolayiv, UA | Average | GDD | 196.00 | 1910 | | |
NDVI | 0.62 | 960 | 0.63 | 2460 |
EVI | 0.38 | 960 | 0.41 | 2460 |
Mykolayiv, UA | Heat wave | GDD | 240.00 | 1100 | 230.00 | 2580 |
NDVI | 0.49 | 660 | | |
EVI | 0.26 | 660 | | |