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The objective of this study is to evaluate the accuracy of the daily nadir total column ozone products derived from the nadir mapper instrument on the Ozone Mapping and Profiler Suite (OMPS) flying onboard the Suomi National Polar-orbiting Partnership satellite (S-NPP) launched as a part of the Joint Polar Satellite System (JPSS) program between NOAA and NASA. Since NOAA is already operationally processing OMPS nadir total ozone products, evaluations were made in this study on the total column ozone research products generated by NASA’s science team, utilizing the latest version of their Backscatter Ultraviolet (BUV) retrieval algorithms, to provide insight into the performance of the operation system. Comparisons were made with globally distributed ground-based Brewer and Dobson spectrophotometer total column ozone measurements. Linear regressions show fair agreement between OMPS and ground-based total column ozone measurements with a root-mean-square error (RMSE) of approximately 3% (10 DU). The comparison results indicate that the OMPS total column ozone data are 0.59% higher than the Brewer measurements with a standard deviation of 2.82% while 1.09% higher than the Dobson measurements with a standard deviation of 3.27%. Additionally, the variability of relative differences between OMPS and ground total column ozone were analyzed as a function of latitude, time, viewing geometry, and total column ozone value. Results show a 2% bias over most latitudes and viewing conditions when total column ozone value varies between 220 DU and 450 DU.

Crop models are robust tools for simulating the impact of climate change on rice development and production, but are usually designed for specific stations and varieties. This study focuses on a more adaptable model called Simulation Model for Rice-Weather Relations (SIMRIW). The model was calibrated and validated in major rice production regions over China, and the parameters that most affect the model’s output were determined in sensitivity analyses. These sensitive parameters were estimated in different ecological zones. The simulated results of single and double rice cropping systems in different ecological zones were then compared. The accuracy of SIMRIW was found to depend on a few crucial parameters. Using optimized parameters, SIMRIW properly simulated the rice phenology and yield in single and double cropping systems in different ecological zones. Some of the parameters were largely dependent on ecological zone and rice type, and may reflect the different climate conditions and rice varieties among ecological zones.

This work aims to analyze the spatial and temporal variability of aerosol optical depth (AOD) from 2000 to 2012 in the Changjiang River Delta (CRD), China. US Terra satellite moderate resolution imaging spectroradiometer (MODIS) AOD and Angstrom exponent (α) data constitute a baseline, with the empirical orthogonal functions (EOFs) method used as a major data analysis method. The results show that the maximum value of AOD observed in June is 1.00±0.12, and the lowest value detected in December is 0.40±0.05. AOD in spring and summer is higher than in autumn and winter. On the other hand, the α-value is lowest in spring (0.86±0.10), which are affected by coarse particles. High α-value appears in summer (1.32±0.05), which indicate that aerosols are dominated by fine particles. The spatial distribution of AOD has a close relationship with terrain and population density. Generally, high AODs are distributed in the low-lying plains, and low AODs in the mountainous areas. The spatial and temporal patterns of seasonal AODs show that the first three EOF modes cumulatively account for 77% of the total variance. The first mode that explains 67% of the total variance shows the primary spatial distribution of aerosols, i.e., high AODs are distributed in the northern areas and low AODs in the southern areas. The second mode (7%) shows that the monsoon climate probably plays an important role in modifying the distribution of aerosols, especially in summer and winter. In the third mode (3%), this distribution of aerosols usually occurs in spring and winter when the prevailing northwestern or western winds could bring aerosol particles from the inland areas into the central regions of the CRD.

A Bayesian optimal estimation (OE) retrieval technique was used to retreive aerosol optical depth (AOD), aerosol single scattering albedo (SSA), and an asymmetry factor (g) at seven ultraviolet wavelengths, along with total column ozone (TOC), from the measurements of the UltraViolet Multifilter Rotating Shadowband Radiometer (UV-MFRSR) deployed at the Southern Great Plains (SGP) site during March through November in 2009. The OE technique specifies appropriate error covariance matrices and optimizes a forward model (Tropospheric ultraviolet radiative transfer model, TUV), and thus provides a supplemental method for use across the network of the Department of Agriculture UV-B Monitoring and Research Program (USDA UVMRP) for the retrieval of aerosol properties and TOC with reasonable accuracy in the UV spectral range under various atmospheric conditions. In order to assess the accuracy of the OE technique, we compared the AOD retreivals from this method with those from Beer’s Law and the AErosol RObotic Network (AERONET) AOD product. We also examine the OE retrieved TOC in comparison with the TOC from the U.S. Department of Agriculture UV-B Monitoring and Research Program (USDA UVMRP) and the Ozone Monitoring Instrument (OMI) satellite data. The scatterplots of the estimated AOD from the OE method agree well with those derived from Beer’s law and the collocated AERONETAOD product, showing high values of correlation coefficients, generally 0.98 and 0.99, and large slopes, ranging from 0.95 to 1.0, as well as small offsets, less than 0.02 especially at 368 nm. The comparison of TOC retrievals also indicates the promising accuracy of the OE method in that the standard deviations of the difference between the OE derived TOC and other TOC products are about 5 to 6 Dobson Units (DU). Validation of the OE retrievals on these selected dates suggested that the OE technique has its merits and can serve as a supplemental tool in further analyzing UVMRP data.

The Normalized Difference Vegetation Index (NDVI) is an important vegetation greenness indicator. Compared to the AVHRR GIMMS NDVI data, the availability of two datasets with 1 km spatial resolution, i.e., Terra MODIS (MOD13A3) monthly composite and SPOT Vegetation (VGT) 10-day composite NDVI, extends the application dimensions at spatial and temporal scales. An overlapping period of 12 years between the datasets now makes it possible to investigate the consistency of the two datasets. Linear regression trend analysis was performed to compare the two datasets in this study. The results show greater consistency in regression slopes in the semi-arid regions of northern China. Alternatively, the results show only slight changes in the Terra MODIS NDVI regression slope in most areas of southern China whereas the SPOT VGT NDVI shows positive changes over a large area. The corresponding regression slope values between Terra MODIS and SPOT VGT NDVI datasets from the linear fit had a fair agreement in the spatial dimension. However, larger positive and negative differences were observed at the junction of the three regions (East China, Central China, and North China). These differences can be partially explained by the positive standard deviation differences distributed over a large area at the junction of these three regions. This study demonstrated that Terra MODIS and SPOT VGT NDVI have a relatively robust basis for characterizing vegetation changes in annual NDVI in most of the semi-arid and arid regions in northern China.

We use the aerosol optical depth (AOD) measured by the moderate resolution imaging spectrometer (MODIS) onboard the Terra satellite, air pollution index (API) daily data measured by the Shanghai Environmental Monitoring Center (SEMC), and the ensemble empirical mode decomposition (EEMD) method to analyze the air quality variability in Shanghai in the recent decade. The results indicate that a trend with amplitude of 1.0 is a dominant component for the AOD variability in the recent decade. During the World Expo 2010, the average AOD level reduced 30% in comparison to the long-term trend. Two dominant annual components decreased 80% and 100%. This implies that the air quality in Shanghai was remarkably improved, and environmental initiatives and comprehensive actions for reducing air pollution are effective. AOD and API variability analysis results indicate that semi-annual and annual signals are dominant components implying that the monsoon weather is a dominant factor in modulating the AOD and API variability. The variability of AOD and API in selected districts located in both downtown and suburban areas shows similar trends; i.e., in 2000 the AOD began a monotonic increase, reached the maxima around 2006, then monotonically decreased to 2011 and from around 2006 the API started to decrease till 2011. This indicates that the air quality in the entire Shanghai area, whether urban or suburban areas, has remarkably been improved. The AOD improved degrees (IDS) in all the selected districts are (8.6±1.9)%, and API IDS are (9.2±7.1)%, ranging from a minimum value of 1.5% for Putuo District to a maximum value of 22% for Xuhui District.

A simple and accurate method to estimate evapotranspiration (ET) is essential for dynamic monitoring of the Earth system at a large scale. In this paper, we developed an artificial neural network (ANN) model forced by remote sensing and AmeriFlux data to estimate ET. First, the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data (normalized difference vegetation index, land surface temperature and surface net radiation) for the period 2002–2006. ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period. The validation sites covered five different vegetation types and were not involved in the ANN training. The coefficient of determination (R 2) value for comparison between estimated and measured ET was 0.77, the root-mean-square error was 0.62 mm/d, and the mean residual was − 0.28. The simple model developed in this paper captured the seasonal and interannual variation features of ET on the whole. However, the accuracy of estimated ET depended on the vegetation types, among which estimated ET showed the best result for deciduous broadleaf forest compared to the other four vegetation types.

The response of the agro-ecological system to the environment includes the response of individual crop's physiological process and the adaption of the crop community to the environment and its change. Observation and simulation at the single scale level cannot fully explain the above process. It is necessary to develop cross-scale agro-ecological models and study the interaction of agro-ecological processes across different scales. Two typical agro-ecological models (DSSAT and AEZ) are employed in this study and a framework for effective cross-scale data-model and model-model fusion is proposed and illustrated based on the application of DSSAT and AEZ models. The site-specific crop model is up-scaled by using this data-model fusion method, observed data from 36 different agricultural observation stations nationwide and historical weather stations observations (1962-1990) are employed and average crop productivity are estimated. Comparison of the estimation results shows the major consistency across the two models and also indicates specific directions for further improvement. This would help the future efforts in cross-scale crop model fusion.