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Chen P.,CAS Institute of Geographical Sciences and Natural Resources Research | Chen P.,Jiangsu Center For Collab Innovation Geographical Information Resource Development And Application
Remote Sensing | Year: 2015

Remote predictions of the nitrogen nutrition index (NNI) are useful for precise nitrogen (N) management in the field. Several studies have recommended two methods for estimating the NNI, which are classified as mechanistic and semi-empirical methods in this study. However, no studies have been conducted to thoroughly analyze and compare these two methods. Using winter wheat as an example, this study compared the performances of these two methods for estimating the NNI to determine which method is more suitable for practical use. Field measurements were conducted to determine the above ground biomass, N concentration and canopy spectra during different wheat growth stages in 2012. Nearly 120 samples of data were collected and divided into different calibration and validation datasets (containing data from single or multi-growth stages). Based on the above datasets, the performances of the two NNI estimation methods were compared, and the influences of phenology on the methods were analyzed. All models that used the mechanistic method with different calibration datasets performed well when validated by validation datasets containing single growth or multi-growth stage data. The validation results had R2 values between 0.82 and 0.94, root mean square error (RMSE) values between 0.05 and 0.17, and RMSE% values between 5.10% and 14.41%. Phenology had no effect on this type of NNI estimation method. However, the semi-empirical method was influenced by phenology. The performances of the models established using this method were determined by the type of data used for calibration. Thus, the mechanistic method is recommended as a better methodfor estimating the NNI. By combining proper N management strategies, it can be used for precise N management. © 2015 by the authors; licensee MDPI, Basel, Switzerland. Source


Bai Y.,CAS Institute of Geographical Sciences and Natural Resources Research | Feng M.,University of Maryland University College | Jiang H.,Guangzhou Institute of Geography | Wang J.,CAS Institute of Geographical Sciences and Natural Resources Research | Wang J.,Jiangsu Center For Collab Innovation Geographical Information Resource Development And Application
Remote Sensing | Year: 2015

This paper presents a rigorous validation of five widely used global land cover products, i.e., GLCC (Global Land Cover Characterization), UMd (University of Maryland land cover product), GLC2000 (Global Land Cover 2000 project data), MODIS LC (Moderate Resolution Imaging Spectro-radiometer Land Cover product) and GlobCover (GLOBCOVER land cover product), and a national land cover map GLCD-2005 (Geodata Land Cover Dataset for year 2005) against an independent reference data set over China. The land cover reference data sets in three epochs (1990, 2000, and 2005) were collected on a web-based prototype system using a sampling-based labeling approach. Results show that, in China, the highest overall accuracy is observed in GLCD-2005 (72.3%), followed by MODIS LC (68.9%), GLC2000 (65.2%), GlobCover (57.7%) and GLCC (57.2%), while UMd has the lowest accuracy (48.6%); all of the products performed best in representing "Trees" and "Others", well with "Grassland" and "Cropland",but problematic with "Water" and "Urban" across China in general. Moreover, in respect of GLCD-2005, there are significant accuracy differences across seven geographical locations of China, ranging from 46.3% in the Southwest, 77.5% in the South, 79.2% in the Northwest, 80.8% in the North, 81.8% in the Northeast, 82.6% in the Central, to 89.0% in the East. This study indicates that a regionally focused land cover map would in fact be more accurate than extracting the same region from a globally produced map. © 2015 by the authors; licensee MDPI, Basel, Switzerland. Source


Guo S.,Wuhan University | Meng L.,Wuhan University | Zhu A.-X.,Nanjing Normal University | Zhu A.-X.,State Key Laboratory Cultivation Base of Geographical Environment Evolution Jiangsu Province | And 7 more authors.
Remote Sensing | Year: 2015

Detailed and accurate information on the spatial variation of soil over low-relief areas is a critical component of environmental studies and agricultural management. Early studies show that the pattern of soil dynamics provides comprehensive information about soil and can be used as a new environmental covariate to indicate spatial variation in soil in low relief areas. In practice, however, data gaps caused by cloud cover can lead to incomplete patterns over a large area. Missing data reduce the accuracy of soil information and make it hard to compare two patterns from different locations. In this study, we introduced a new method to fill data gaps based on historical data. A strong correlation between MODIS band 7 and cumulated reference evapotranspiration (CET0) has been confirmed by theoretical derivation and by the real data. Based on this correlation, data gaps in MODIS band 7 can be predicted by daily evaporation data. Furthermore, correlations among bands are used to predict soil reflectance in MODIS bands 1-6 from MODIS band 7. A location in northeastern Illinois with a large area of low relief farmland was selected to examine this idea. The results show a good exponential relationship between MODIS band 7 and CET00.5 in most locations of the study area (with average R2 = 0.55, p < 0.001, and average NRMSE 10.40%). A five-fold cross validation shows that the approach proposed in this study captures the regular pattern of soil surface reflectance change in bands 6 and 7 during the soil drying process, with a Normalized Root Mean Square Error (NRMSE) of prediction of 13.04% and 10.40%, respectively. Average NRMSE of bands 1-5 is less than 20%. This suggests that the proposed approach is effective for filling the data gaps from cloud cover and that the method reduces the data collection requirement for understanding the dynamic feedback pattern of soil, making it easier to apply to larger areas for soil mapping. © 2015 by the authors. Source


Bao Y.,Nanjing University | Tian Q.,Nanjing University | Chen M.,Jiangsu Center For Collab Innovation Geographical Information Resource Development And Application | Chen M.,Chinese University of Hong Kong | Chen M.,Nanjing Normal University
Remote Sensing | Year: 2015

Due to the spatiotemporal variations of complex optical characteristics, accurately estimating chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing techniques remains challenging. In this study, a weighted algorithm was developed to estimate the Chl-a concentrations based on spectral classification and weighted matching using normalized mutual information (NMI). Based on the NMI algorithm, three water types (Class 1 to Class 3) were identified using the in situ normalized spectral reflectance data collected from Taihu Lake. Class-specific semi-analytic algorithms for the Chl-a concentrations were established based on the GOCI data. Next, weighted factors, which were used to determine the matching probabilities of different water types, were calculated between the GOCI data and each water type using the NMI algorithm. Finally, Chl-a concentrations were estimated using the weighted factors and the class-specific inversion algorithms for the GOCI data. Compared to the non-classification and hard-classification algorithms, the accuracies of the weighted algorithms were higher. The mean absolute error and root mean square error of the NMI weighted algorithm decreased to 22.63% and 9.41 mg/m3, respectively. The results also indicated that the proposed algorithm could reduce discontinuous or jumping effects associated with the hard-classification algorithm. © 2015 by the authors. Source


Ning L.,Key Laboratory of Virtual Geographic Environment Ministry of Education | Ning L.,Nanjing Normal University | Ning L.,University of Massachusetts Amherst | Ning L.,Jiangsu Center For Collab Innovation Geographical Information Resource Development And Application | And 2 more authors.
Journal of Climate | Year: 2015

Projections of historical and future changes in climate extremes are examined by applying the bias-correction spatial disaggregation (BCSD) statistical downscaling method to five general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). For this analysis, 11 extreme temperature and precipitation indices that are relevant acrossmultiple disciplines (e.g., agriculture and conservation) are chosen. Over the historical period, the simulated means, variances, and cumulative distribution functions (CDFs) of each of the 11 indices are first compared with observations, and the performance of the downscaling method is quantitatively evaluated. For the future period, the ensemble average of the five GCM simulations points to more warm extremes, fewer cold extremes, and more precipitation extremes with greater intensities under all three scenarios. The changes are larger under higher emissions scenarios. The inter-GCM uncertainties and changes in probability distributions are also assessed. Changes in the probability distributions indicate an increase in both the number and interannual variability of future climate extreme events. The potential deficiencies of the method in projecting future extremes are also discussed. © 2015 American Meteorological Society. Source

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