## 5 Conclusions and Recommendations  
In this paper, we have compared and contrasted the peak timing of cropland vegetation maturity using two complementary remote sensing datasets. Time series of LSP of VOD at three microwave  
Figure 14: VOD and NDVI plots for sample sites: **(a)** Mykolayiv, UA; and **(b)** Voronezh, RU, affected by the 2007 and 2010 Ukrainian and Russian heatwaves, respectively. In both plots, purple circle represents VOD at 10.65 GHz and blue diamond represents NDVI plots average (2003–2010) excluding the respective heatwave years with relative maximum and minimum error bars. The red circle and orange diamond plots represent heatwave year VODs and NDVIs, respectively, for both sites. Note the PH in both the average and heatwave affected years. Note also the shapes and magnitudes of the time series in the heatwave years relative to the other year’s average. Figure 15: Scatter plots of: **(a)** NDVI at half-TTP as a function of PH\({}_{\text{NDVI}}\) derived from a CxQ model for the 2010 heatwave affecting Russia and Kazakhstan; and **(b)** the 2010 data and the average data for the rest of the years (2003–2009). Note in **(a)** the position of the 2010 phenometrics presented by red diamonds relative to the other years in their respective sites. Note also the 2010 marker for the upper-right corner, which is for the MFO site in Mari El, Russia. frequencies--6.925, 10.65, and 18.7 GHz--from the passive Advanced Microwave Scanning Radiometer on EOS (AMSR-E) was able to capture the seasonality of croplands (both bimodal and unimodal seasonal patterns) similar to but different from that of the VNIR MODIS NDVI. A higher microwave frequency generates a higher VOD, since smaller wavelengths are more quickly attenuated by aboveground vegetation. The timing and magnitude of the growing season peak (TTP and PH) are more consistent quantitative cropland phenometrics, in large part because they are minimally affected by snow or frost, unlike the commonly-used but ill-defined phenometrics of Start of Season and End of Season. The vegetation optical depth (VOD) retrieved from passive microwave data appears to be more robust to atmospheric effects and biomass saturation than the phenometrics based on the NDVI. We have also demonstrated the application of the downward convex quadratic (CxQ) function to model cropland dynamics using MODIS NDVI yielding strong fits (0.78 < R\({}^{2}\) < 0.99). However, the shape of the VOD time series curve shape was more complicated than a simple quadratic fit. The passive microwave AMSR-E PHVOD determined using the growing season maximum value of the growing season approach favorably corresponded with that of the VNIR MODIS peak greenness (PH\({}_{\text{NDVI}}\)) derived using the CxQ model over the study domain with R\({}^{2}\) between 0.77 and 0.84. Although the 10.65 GHz PHVOD exhibited the best linear regression fit (R\({}^{2}\) = 0.84) and with lowest standard error (SEE = 0.128) with the PH\({}_{\text{NDVI}}\), the ANCOVA model analysis revealed that the slopes resulting from the linear regression fits were not significantly different by microwave frequency. In contrast, the intercepts from the ANCOVA analysis were significantly different: not surprising given the different dynamic ranges of the VODs. Despite the strong correspondence between peak LSPs of the two datasets, there was a general lag of AMSR-E PHVOD by two weeks compared to that of MODIS PH\({}_{\text{NDVI}}\). Based on PH\({}_{\text{NDVI}}\) analysis of same dataset using the two procedures, we found a one-week bias between the two methods, translating into a lag of three weeks between PH\({}_{\text{NDVI}}\) and PH\({}_{\text{VOD}}\). We conclude that maximum value based PHVOD can yield phenometrics complementary to the model based PH\({}_{\text{NDVI}}\), especially in cloud and aerosol obscured parts of the world. The unsuitability of the CxQ model for characterizing the VOD curves and deriving simple phenometrics limits the generalizability of the CxQ model.