Aim We intend to characterize and understand the spatial and temporal patterns of vegetation phenology shifts in North America during the period 1982-2006. Location North America.Methods A piecewise logistic model is used to extract phenological metrics from a time-series data set of the normalized difference vegetation index (NDVI). An extensive comparison between satellite-derived phenological metrics and groundbased phenology observations for 14,179 records of 73 plant species at 802 sites across North America is made to evaluate the information about phenology shifts obtained in this study. ResultsThe spatial pattern of vegetation phenology shows a strong dependence on latitude but a substantial variation along the longitudinal gradient. A delayed dormancy onset date (0.551 days year -1 , P = 0.013) and an extended growing season length (0.683 days year -1 , P = 0.011) are found over the mid and high latitudes in North America during 1982-2006, while no significant trends in greenup onset are observed. The delayed dormancy onset date and extended growing season length are mainly found in the shrubland biome. An extensive validation indicates a strong robustness of the satellite-derived phenology information.Main conclusions It is the delayed dormancy onset date, rather than an advanced greenup onset date, that has contributed to the prolonged length of the growing season over the mid and high latitudes in North America during recent decades. Shrublands contribute the most to the delayed dormancy onset date and the extended growing season length. This shift of vegetation phenology implies that vegetation activity in North America has been altered by climatic change, which may further affect ecosystem structure and function in the continent.

Maximum light use efficiency (ε max ) is a key parameter for the estimation of net primary productivity (NPP) derived from remote sensing data. There are still many divergences about its value for each vegetation type. The ε max for some typical vegetation types in China is simulated using a modified least squares function based on NOAA/AVHRR remote sensing data and field-observed NPP data. The vegetation classification accuracy is introduced to the process. The sensitivity analysis of ε max to vegetation classification accuracy is also conducted.The results show that the simulated values of ε max are greater than the value used in CASA model, and less than the values simulated with BIOME-BGC model. This is consistent with some other studies. The relative error of ε max resulting from classification accuracy is −5.5%-8.0%. This indicates that the simulated values of ε max are reliable and stable.

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Crop phenology is an important parameter for crop growth monitoring, yield prediction, and growth simulation. The dynamic threshold method is widely used to retrieve vegetation phenology from remotely sensed vegetation index time series. However, crop growth is not only driven by natural conditions, but also modified through field management activities. Complicated planting patterns, such as multiple cropping, makes the vegetation index dynamics less symmetrical. These impacts are not considered in current approaches for crop phenology retrieval based on the dynamic threshold method. Thus, this paper aimed to (1) investigate the optimal thresholds for retrieving the start of the season (SOS) and the end of the season (EOS) of different crops, and (2) compare the performances of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in retrieving crop phenology with a modified version of the dynamic threshold method. The reference data included SOS and EOS ground observations of three major crop types in 2015 and 2016, which includes rice, wheat, and maize. Results show that (1) the modification of the original method ensures a 100% retrieval rate, which was not guaranteed using the original method. The modified dynamic threshold method is more suitable to retrieve crop SOS/EOS because it considers the asymmetry of crop vegetation index time series. (2) It is inappropriate to retrieve SOS and EOS with the same threshold for all crops, and the commonly used 20% or 50% thresholds are not the optimal thresholds for all crops. (3) For single and late rice, the accuracies of the SOS estimations based on EVI are generally higher compared to those based on NDVI. However, for spring maize and summer maize, results based on NDVI give higher accuracies. In terms of EOS, for early rice and summer maize, estimates based on EVI result in higher accuracies, but, for late rice and winter wheat, results based on NDVI are closer to the ground records.

Spatial information on irrigation is needed for a variety of applications, such as studies on water exchange between the land surface and atmosphere, climate change, and irrigation water requirements, water resources management, hydrological modeling, and agricultural planning. However, it is hard to map irrigated areas automatically by traditional image classification methods because of the high spectral similarity between the same crops with and without irrigation. In this study, we developed three irrigation potential indices by using the time series normalized difference vegetation index (NDVI) and precipitation data. Using these indices and a spatial allocation model, we downscaled the census data on irrigation from administrative units to individual pixels and produced a new irrigation map of China around the year 2000. We collected 614 reference samples (262 irrigated samples and 352 nonirrigated) in mainland China to validate our new irrigation map and also two global irrigation maps: one is produced by the Food and Agriculture Organization of the United Nations and the University of Frankfurt (FAO/UF map), whereas the other is produced by the International Water Management Institute (IWMI map). The overall accuracies of IWMI map (0.0089282 ) and the new map (1 km) are 60.91% and 68.40%, respectively. We also resampled the IWMI map and the new map to match the spatial resolution of FAO/ UF map (0.0833333 ), and calculated the producer accuracies of FAO/UF map, resampled IWMI map, and resampled new irrigation map. The accuracies are 83.2%, 83.2%, and 87.0%, respectively. We further compared the three maps using cluster and outlier analysis and spot analysis. Comparison results suggest that our new map agrees very well with the patterns of irrigated area distribution from the FAO/UF map, but differs greatly from the IWMI map. Results from this study suggest that our method is a promising tool for mapping irrigated areas. It has several advantages. First, its inputs are quite simple, and no training samples are needed. Second, our model is general and repeatable. Third, it can be used to map historical irrigated areas. The limitations of our method are also discussed.

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Abstract:The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this study made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982-2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a OPEN ACCESSRemote Sens. 2013, 5 4032 higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. These results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades.