A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Extraction of Desertification Information in Hulun Buir Based on MODIS Image Data

[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.

Review of large scale crop remote sensing monitoring based on MODIS data

China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.

Effects of Freezing Disaster on Green-up Date of Vegetation Using MODIS/EVI Time Series Data

In the field of global changes, the relationship between plant phenology and climate, which reflects the response of terrestrial ecosystem to global climate change, has become a key subject that is highly concerned. Using the moderate-resolution imaging spectroradiometer (MODIS)/enhanced vegetation index(EVI) collected every eight days during January- July from 2005 to 2008 and the corresponding remote sensing data as experimental materials, we constructed cloud-free images via the Harmonic analysis of time series (HANTS). The cloud-free images were then treated by dynamic threshold method for obtaining the vegetation phenology in green up period and its distribution pattern. And the distribution pattern between freezing disaster year and normal year were comparatively analyzed for revealing the effect of freezing disaster on vegetation phenology in experimental plot. The result showed that the treated EVI data performed well in monitoring the effect of freezing disaster on vegetation phenology, accurately reflecting the regions suffered from freezing disaster. This result suggests that processing of remote sensing data using HANTS method could well monitor the ecological characteristics of vegetation.

Monitoring of Summer High Temperature Damage by Using MODIS Data to Estimate Air Temperature

[Objective] This study was to monitor the hot damage of high temperature on rice in summer by using MODIS data to estimate air temperature. [Method] A new statistical algorithm was introduced for daytime air temperature （Ta） retrievals over east China by using the Moderate Resolution Imaging Spectroradiometer （MODIS） data, and the high temperature monitoring for rice in south China in 2007 summer was used to demonstrate. [Result] High temperature plays a key role in rice production during rice heading stage in summer in southern China. Using MODIS data to monitor the hot damage of high temperature is a feasible way to relieve agricultural disasters. [Conclusion] The result of this study provided a method to monitor hot damage of high temperature tn rice in summer of China.

Vegetation NPP Distribution Based on MODIS Data and CASA Model——A Case Study of Northern Hebei Province

Net Primary Productivity （NPP） is one of the important biophysical variables of vegetation activity, and it plays an important role in studying global carbon cycle, carbon source and sink of ecosystem, and spatial and temporal distribution of CO2. Remote sensing can provide broad view quickly, timely and multi-temporally, which makes it an attractive and powerful tool for studying ecosystem primary productivity, at scales ranging from local to global. This paper aims to use Moderate Resolution Imaging Spectroradiometer （MODIS） data to estimate and analyze spatial and temporal distribution of NPP of the northern Hebei Province in 2001 based on Carnegie-Ames-Stanford Approach （CASA） model. The spatial distribution of Absorbed Photosynthetically Active Radiation （APAR） of vegetation and light use efficiency in three geographical subregions, that is, Bashang Plateau Region, Basin Region in the northwestern Hebei Province and Yanshan Mountainous Region in the Northern Hebei Province were analyzed, and total NPP spatial distribution of the study area in 2001 was discussed. Based on 16-day MODIS Fraction of Photosynthetically Active Radiation absorbed by vegetation （FPAR） product, 16-day composite NPP dynamics were calculated using CASA model; the seasonal dynamics of vegetation NPP in three subreglons were also analyzed. Result reveals that the total NPP of the study area in 2001 was 25.1877 × 10^6gC/（m^2.a）, and NPP in 2001 ranged from 2 to 608gC/（m^2-a）, with an average of 337.516gC/（m^2.a）. NPP of the study area in 2001 accumulated mainly from May to September （DOY 129-272）, high NIP values appeared from June to August （DOY 177-204）, and the maximum NPP appeared from late July to mid-August （DOY 209-224）.

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS: METHODS AND IDEAL TESTS

Clouds have important effects on the infi＇ared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, but also minimize the initial error of cloud parameters by adjusting part of the infrared radiances data. On the basis of the Grapes-3D-var （Global and Regional Assimilation and Prediction Enhanced System）, cloud liquid water, cloud ice water and cloud cover are added as the governing variables in the assimilation. Under the conditions of clear sky, partly cloudy cover and totally cloudy cover, the brightness temperature of 16 MODIS channels are assimilated respectively in ideal tests. Results show that when the simulated background brightness temperatures are lower than the observation, the analyzed field will increase the simulated brightness temperature by increasing its temperature and reducing its moisture, cloud liquid water, cloud ice water, and cloud cover. The simulated brightness temperature can be reduced if adjustment is made in the contrary direction. The adjustment of the temperature and specific humidity under the clear sky conditions conforms well to the design of MODIS channels, but it is weakened for levels under cloud layers. The ideal tests demonstrate that by simultaneously adding both cloud parameters and atmospheric parameters as governing variables during the assimilation of infrared radiances, both the cloud parameters and atmospheric parameters can be adjusted using the observed infrared radiances and conventional meteorological elements to make full use of the infrared observations.

MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS Ⅱ: IMPACTS ON RAINSTORM FORECASTING

Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations in the data assimilation of numerical weather predictions,especially those in infrared channels. It is common to only assimilate radiances under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky.On the basis of the Global and Regional Assimilation and Prediction Enhanced System 3-dimensional variance(GRAPES-3DVar),cloud liquid water content, ice-water content and cloud cover are employed as governing variables in the assimilation system.This scheme can improve the simulation of infrared transmittance by a fast radiative transfer model for TOVS (RTTOV)and adjust the atmospheric and cloud parameters based on infrared radiance observations.In this paper,we investigate a heavy rainfall over Guangdong province on May 26,2007,which is right after the onset of a South China Sea monsoon.In this case,channels of the Moderate Resolution Imaging Spectroradiometer(MODIS)for observing water vapor(Channel 27)and cloud top altitude(Channel 36)are selected for the assimilation.The process of heavy rainfall is simulated by the Weather Research and Forecasting(WRF)model.Our results show that the assimilated MODIS data can improve the distribution of water vapor and temperature in the first guess field and indirectly adjust the upper-level wind field.The tendency of adjustment agrees well with the satellite observations.The assimilation scheme has positive impacts on the short-range forecasting of rainstorm.

Spatial distribution of aerosol pollution based on MODIS data over Beijing,China

With the help of regression analysis, the relationships were detected between aerosol＇s contribution to apparent reflectance （ACR） derived from Moderate Resolution Imaging Spectroradiometer （MODIS） on board Terra and hourly PM10 mass concentration measured at 30 ground-based locations in Beijing for the August of 2003 and 2004. It was shown that there was a good correlation between the ACR and PM10 （linear correlation coefficient, R=0.56）. On the basis of this relationship, spatial distribution and possible sources of PM10 derived from MODIS were analyzed and two frequently heavily-polluted regions were found, namely downtown of the city and the district near Xishan Mountain. These two regions coincidently are also urban heat island centers. The foundings of this paper will be greatly useful for environmental monitoring and urban planning for Beijing, especially for the 2008 Olympic game to be held in Beijing.

The Regional Surface Heating Field over the Heterogeneous Landscape of the Tibetan Plateau Using MODIS and In Situ Data

In this study, a parameterization scheme based on Moderate Resolution Imaging Spectroradiometer （MODIS） data and in-situ data was tested for deriving the regional surface heating field over a heterogeneous landscape. As a case study, the methodology was applied to the whole Tibetan Plateau （TP） area. Four images of MODIS data （i.e., 30 January 2007, 15 April 2007, 1 August 2007, and 25 October 2007） were used in this study for comparison among winter, spring, summer, and autumn. The results were validated using the observations measured at the stations of the Tibetan Observation and Research Platform （TORP）. The results show the following： （1） The derived surface heating field for the TP area was in good accord with the land-surface status, showing a wide range of values due to the strong contrast of surface features in the area. （2） The derived surface heating field for the TP was very close to the field measurements （observations）. The APD （absolute percent difference） between the derived results and the field observations was 〈10%. （3） The mean surface heating field over the TP increased from January to April to August, and decreased in October. Therefore, the reasonable regional distribution of the surface heating field over a heterogeneous landscape can be obtained using this methodology. The limitations and further improvement of this method are also discussed.

Monitoring Winter Wheat Freeze Injury Using Multi-Temporal MODIS Data

Freeze injury is an usual disaster for winter wheat in Shanxi Province, China, and monitoring freeze injury is of important economic significance. The aim of this article is to monitor and analyze the winter wheat freeze injury using remote sensing data, to monitor the occurrence and spatial distribution of winter wheat freeze in time, as well as the severity of the damage. The winter wheat freeze injury was monitored using multi-temporal moderate-resolution imaging spectroradiometer （MODIS） data, combined with ground meteorological data and field survey data, the change of normalized difference vegetation index （NDVI） before and after freeze injury was analyzed, as well as the effect of winter wheat growth recovery rate on yield. The results showed that the NDVI of winter wheat decreased dramatically after the suffering from freeze injury, which was the prominent feature for the winter wheat freeze injury monitoring. The degrees of winter wheat freeze injury were different in the three regions, of which, Yuncheng was the worst severity and the largest freeze injury area, the severity of freeze injury correlates with the breeding stage of the winter wheat. The yield of winter wheat showed positive correlation with its growth recovery rate （r=0.659^＊＊ which can be utilized to monitor the severity of winter wheat freeze injury as well as its impact on yield. It can effectively monitor the occurrence and severity of winter wheat freeze injury using horizontal and vertical profile distribution and growth wheat freeze injury in Shanxi Province. recovery rate, and provide a basis for monitoring the winter

Simulating .net primary production of grasslands in northeastern Asia using MODIS data from 2000 to 2005

The net primary production （NPP） of grasslands in northeastern Asia was estimated using improved CASA model with MODIS data distributed from 2000 and ground data as driving variables from 2000 to 2005. Average annual NPP was 146.05 g C m^-2 yr^-1 and average annual total NPP was 0.32 Pg C yr^-1 in all grasslands during the period. It was shown that average annual grassland NPP in the whole northeastern Asia changed dramatically from 2000 to 2005, with the highest value of 174.80 g C m^-2 yr^-1 in 2005 and the lowest value of 125.65 g C m^-2 yr^-1 in 2001. On regional scale, average annual grassland NPP of 179.71 g C m^-2 yr^-1 in southeastern Russia was the highest among the three main grassland regions in the six years. Grasslands in northern China exhibited the highest average annual total NPP of 0.16 Pg C yr^-1 and contributed 51.42% of the average annual total grassland NPP in northeastern Asia. Grassland NPP in northeastern Asia also showed a clear seasonal pattern with the highest NPP occurred in July every year. Average monthly grassland NPP in southeastern Russia was the highest from May to August while average monthly grassland NPP in northern China showed the highest NPP before May and after August. The change rate distribution of grassland NPP between the former three years and the latter three years showed grassland NPP changed slightly between the two stages in most regions, and that NPP change rate in 80.98% of northeastern Asia grasslands was between -0.2 and 0.2. Grassland NPP had close correlation with precipitation and temperature, that indicates climate change will influence the grassland NPP and thus have a great impact on domestic livestock in this region in future.

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer （MODIS） weekly normalized difference vegetation index （NDVI） product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels （〉90%） masked by the United States Department of Agriculture （USDA） Cropland Data Layer （CDL）. The NDVI-based time-series difference between flood years and normal year （median of years 2000-2014） was used to detect flood occur- rences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error （4.8%） of leave-one-out cross validation （LOOCV） along with smaller statistical error indicators （root mean square error （RMSE）, mean absolute error （MAE）, and mean bias error （MBE））, further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.

Evaluation of spatial-temporal dynamics in surface water temperature of Qinghai Lake from 2001 to 2010 by using MODIS data

Lake surface water temperature （SWT） is an important indicator of lake state relative to its water chemistry and aquatic ecosystem,in addition to being an important regional climate indicator.However,few literatures involving spatial-temporal changes of lake SWT in the Qinghai-Tibet Plateau,including Qinghai Lake,are available.Our objective is to study the spatial-temporal changes in SWT of Qinghai Lake from 2001 to 2010,using Moderate-resolution Imaging Spectroradiometer （MODIS） data.Based on each pixel,we calculated the temporal SWT variations and long-term trends,compared the spatial patterns of annual average SWT in different years,and mapped and analyzed the seasonal cycles of the spatial patterns of SWT.The results revealed that the differences between the average daily SWT and air temperature during the temperature decreasing phase were relatively larger than those during the temperature increasing phase.The increasing rate of the annual average SWT during the study period was about 0.01℃/a,followed by an increasing rate of about 0.05℃/a in annual average air temperature.The annual average SWT from 2001 to 2010 showed similar spatial patterns,while the SWT spatial changes from January to December demonstrated an interesting seasonal reversion pattern.The high-temperature area transformed stepwise from the south to the north regions and then back to the south region from January to December,whereas the low-temperature area demonstrated a reversed annual cyclical trace.The spatial-temporal patterns of SWTs were shaped by the topography of the lake basin and the distribution of drainages.

Estimation of Land Surface Temperature over the Tibetan Plateau Using AVHRR and MODIS Data

Estimation of large-scale land surface temperature from satellite images is of great importance for the study of climate change. This is especially true for the most challenging areas, such as the Tibetan Plateau （TP）. In this paper, two split window algorithms （SWAs）, one for the NOAA’s Advanced Very High Resolu-tion Radiometer （AVHRR）, and the other for the Moderate Resolution Imaging Spectroradiometer （MODIS）, were applied to retrieve land surface temperature （LST） over the TP simultaneously. AVHRR and MODIS data from 17 January, 14 April, 23 July, and 16 October 2003 were selected as the cases for winter, spring, summer, and autumn, respectively. Firstly, two key parameters （emissivity and water vapor content） were calculated at the pixel scale. Then, the derived LST was compared with in situ measurements from the Coordinated Enhanced Observing Period （CEOP） Asia-Australia Monsoon Project （CAMP） on the TP （CAMP/Tibet） area. They were in good accordance with each other, with an average percentage error （PE） of 10.5% for AVHRR data and 8.3% for MODIS data, meaning the adopted SWAs were applicable in the TP area. The derived LST also showed a wide range and a clear seasonal difference. The results from AVHRR were also in agreement with MODIS, with the latter usually displaying a higher level of accuracy.

Multiple Cropping Intensity in China Derived from Agro-meteorological Observations and MODIS Data

Double-and triple-cropping in a year have played a very important role in meeting the rising need for food in China.However,the intensified agricultural practices have significantly altered biogeochemical cycles and soil quality.Understanding and mapping cropping intensity in China′s agricultural systems are therefore necessary to better estimate carbon,nitrogen and water fluxes within agro-ecosystems on the national scale.In this study,we investigated the spatial pattern of crop calendar and multiple cropping rotations in China using phenological records from 394 agro-meteorological stations(AMSs)across China.The results from the analysis of in situ field observations were used to develop a new algorithm that identifies the spatial distribution of multiple cropping in China from moderate resolution imaging spectroradiometer(MODIS)time series data with a 500 m spatial resolution and an 8-day temporal resolution.According to the MODIS-derived multiple cropping distribution in 2002,the proportion of cropland cultivated with multiple crops reached 34%in China.Double-cropping accounted for approximately 94.6%and triple-cropping for 5.4%.The results demonstrat that MODIS EVI(Enhanced Vegetation Index)time series data have the capability and potential to delineate the dynamics of double-and triple-cropping practices.The resultant multiple cropping map could be used to evaluate the impacts of agricultural intensification on biogeochemical cycles.

Change Detection of Rice Cultivation in Bangladesh Based on the Phenological Analysis of MODIS Data

Mapping rice cultivation is indispensable for monitoring food supply conditions in Bangladesh because of the economical importance of the crop for supporting ever increasing population in the country. In this paper, we extract the rice paddy field using the MODIS satellite data for five districts of Pabna, Manikganj, Sherpur, Sylhet, and Gazipur, each of which is characterized with its own aspects in terms of rice cultivation. Land classification is implemented using the vegetation index information derived from the red (band 1) and near-infrared (band 2) bands of MODIS 8-day composite time series data for the two time periods of 2001-2003 and 2011-2013. Results of unsupervised classification indicate that the paddy area coverage increased about 4% and 1% in Gazipur and Sylhet, respectively. In Pabna, Manikganj, and Sherpur, on the other hand, paddy area decreased by 10%, 2% and 5%, respectively, whereas notable increase of 12%, 2% and 7% was found in homestead area coverage, which is becoming more and more important for better management of small-scale agroforestry. At the same time, in Sherpur and Sylhet, forest area increased by 1% and 2% over the same time period. As a validation of these results, the changes detected in Gazipur are compared with those previously derived from the analysis of Landsat data with higher spatial resolution of 30 m as compared with that of MODIS (250 m). Also, the seasonal rice cropping pattern is studied in these five districts for discriminating cultivated rice types. These changes suggest that as a whole, efforts are being made to increase the food production, though the influence of population pressure and economic growth is apparent in these regions.

A DEM Inversion Method for Inter-Tidal Zone Based on MODIS Dataset:A Case Study in the Dongsha Sandbank of Jiangsu Radial Tidal Sand-Ridges,China

Surthce elevation is tile basic data for geo-science. It is difficult to retrieve tidal-flats＇ elevation from a single Re- mote Sensing （RS） image because of the complicated sediment dynanical environment and huge spatial difference in tidal-flats＇ moisture content. A Digital Elevation Model （DEM） construction method for inconstant inter-tidal zone based on high tempo-resolution MODIS data set in a short period is proposed in a ease study on the Dongsha Sandbank of the Jiangsu Radial Tidal Sand-ridges. In the present study, a batch-preprocessing method based on image partition to handle massive MODIS IB images is developed and applied to 8163 scenes of MODIS images. The dataset of short-period and muhi-temporal MODIS images for inter-tidal flats＇ DEM inversion is selected and the usability of MODIS dataset is analyzed. Shorelines of the Dongsha Sandbank are extracted by use of batch supervised classification. In accord with tidal- 0 level forecasted by the Chenjiawu Tidal Gauge Station at the overpass moment of each RS image, DEMs of inter-tidal flats in January and sutmner（Jul, Aug and Sept）, 2003 are built under ArcG1S9.2. Studies show that： （1） The dataset of short-duration and muhi-phase MODIS images can be used to retrieve the historical DEM of tidal-flats at changeful tidal flats. （2） Aualysis on usability of MODIS images from Aqua and Terra indicates that there are more usable and higbquality MODIS images in spring, autumn and winter, but less in summer. Therefore, the period for building inter-tidal fiats＇ DEM is suggested to be one month in spring, autumn and winter and three months in summer.