Sub-pixel analysis to enhance the accuracy of evapotranspiration determined using MODIS images

A study was carried out to estimate the actual evapotranspiration(ET)over a 1074 km2 of the humid area of Perak State(Malaysia),where water and evaporation cycle deeply influences the climate,natural resources and human living aspects.Images from both Terra and Aqua platforms of the Moderate Resolution Imaging Spectroradiometer(MODIS)sensor were used for ET estimation by employing the Surface Energy Balance Algorithm for Land(SEBAL)model.As a part of the accuracy assessment process,in-situ measurements on soil temperature and reference ET(ET0)were recorded at the time of satellite overpass.In order to enhance the accuracy of the generated ET maps,MODIS images were subjected to sub-pixel analysis by assigning weights for different land surface cover(urban,agriculture and multi-surface areas)reflections.The weighting process was achieved by integrating ET from pure pixels with the respective site-specific ET0 of each land cover.The enhanced SEBAL model estimated ET exhibited a good correlation with the in-situ measured Penman-Montieth ET0,with R2 values for the Aqua and the Terra platforms of 0.67 and 0.73,respectively.However,the correlation of the non-enhanced ET maps resulted in R2 values of 0.61 and 0.68 for the Aqua and the Terra platforms,respectively.Hence,the results of this study revealed the feasibility of employing the sub-pixel analysis method for an accurate estimation of ET over large areas.

The Study of Extracting River Nets Based on Intelligence Ant Colony Algorithm on MODIS Remote Sensing Images

How to extract river nets effectively is of great significance for water resources investigation,flood forecasting and environmental monitoring,etc.In the paper,combining with ant colony algorithm,a new approach of extracting river nets on moderate-resolution imaging spectroradiometer(MODIS)remote sensing images was proposed through analyzing two general extraction methods of river nets.The experiment results show that river nets can be optimized by ant colony algorithm efficiently,and difference ratio between the experimental vectorgraph and the data of National Fundamental Geographic Information System is down to 8.7%.The proposed algorithm can work for extracting river nets on MODIS remote sensing images effectively.

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.

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.

Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness(PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer(MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index(DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.

Crop Classification Using MODIS NDVI Data Denoised by Wavelet: A Case Study in Hebei Plain, China

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.

Evapotranspiration Estimation Based on MODIS Products and Surface Energy Balance Algorithms for Land(SEBAL) Model in Sanjiang Plain,Northeast China

In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.

A Simple Method to Extract Tropical Monsoon Forests Using NDVI Based on MODIS Data:A Case Study in South Asia and Peninsula Southeast Asia

Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Vegetation lndex （NDVI） derived from the Moderate Resolution Imaging Spectroradiometer （MODIS） data. Owing to the high contrast of greenness between wet season and dry season, the monsoon forest can be easily discriminated from other forests by combining the maximum and minimum annual NDVI. The MODIS-based monsoon forest maps （MODMF） from 2000 to 2009 are derived and evaluated using the ground-truth dataset. The MODMF achieves an average producer accuracy of 80.0% and the Kappa statistic of 0.719. The variability of MODMF among different years is compared with that calculated from MODIS land cover products （MCD 12Q 1）. The results show that the coefficient of variation of total monsoon forest area in MODMF is 7.3%, which is far lower than that in MCD12Q1 with 24.3%. Moreover, the pixels in MODMv which can be identified for 7 to 9 times between 200l and 2009 account for 53.1%, while only 7.9% ofMCD12QI pixels have this frequency. Additionally, the monsoon forest areas estimated in MODMF, Global Land Cover 2000 （GLC2000）, MCDI2Q1 and University of Maryland （UMD） products are compared with the statistical dataset at national level, which reveals that MODMv has the highest R^2 of 0.95 and the lowest RMSE of 14 014 km^2. This algorithm is simple but reliable for mapping the monsoon forests without complex classification techniques.

A MODIS Time Series Data Based Algorithm for Mapping Forest Fire Burned Area

Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and cli- mate change and the effect of forest fire on carbon budgets. This study proposed an algorithm to map forest fire burned area using the Moderate-Resolution Imaging Spectroradiameter （MODIS） time series data in Heilongjiang Province, China. The algorithm is divided into two steps： Firstly, the ＇core＇ pixels were extracted to represent the most possible burned pixels based on the comparison of the tem- poral change of Global Environmental Monitoring Index （GEMI）, Burned Area Index （BAI） and MODIS active fire products between pre- and post-fires. Secondly, a 15-km distance was set to extract the entire burned areas near the ＇core＇ pixels as more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fires, which are represented by the MODIS fire products and the spectral index, respectively. Tahe, Mohe and Huma counties of Heilongjiang Province, China were chosen as the study area for burned area mapping and a time series of burned maps were produced from 2000 to 2011. The results show that the algorithm can extract burned areas more accurately with the hiehest accuracy of 96.61%.

Steady increase in water clarity in Jiaozhou Bay in the Yellow Sea from 2000 to 2018:Observations from MODIS

The Moderate Resolution Imaging Spectroradiometer(MODIS)surface reflectance data were used to analyze the temporal and spatial distribution characteristics of water clarity(Z_(sd))in the Jiaozhou Bay,Qingdao,China,in the Yellow Sea from 2000 to 2018.Z_(sd)retrieval models were regionally optimized using in-situ data with coincident MODIS images,and then were used to retrieve the Z_(sd) products in Jiaozhou Bay from 2000-2018.The analysis of the Z_(sd) results suggests that the spatial distribution of relative Z_(sd) spatial characteristics in Jiaozhou Bay was stable,being higher Z_(sd) in the southeast and a lower Z_(sd) in the northwest.The annual mean Z_(sd) in Jiaozhou Bay showed a significant upward trend,with an annual increase of approximately 0.02 m.Water depth and wind speed were important factors affecting the spatial distribution and annual variation of Z_(sd) in Jiaozhou Bay,respectively.

Global Water Vapor Content Decreases from 2003 to 2012： An Analysis Based on MODIS Data

Water vapor in the earth′s upper atmosphere plays a crucial role in the radiative balance, hydrological process, and climate change. Based on the latest moderate-resolution imaging spectroradiometer(MODIS) data, this study probes the spatio-temporal variations of global water vapor content in the past decade. It is found that overall the global water vapor content declined from 2003 to 2012(slope b = –0.0149, R = 0.893, P = 0.0005). The decreasing trend over the ocean surface(b = –0.0170, R = 0.908, P = 0.0003) is more explicit than that over terrestrial surface(b = –0.0100, R = 0.782, P = 0.0070), more significant over the Northern Hemisphere(b = –0.0175, R = 0.923, P = 0.0001) than that over the Southern Hemisphere(b = –0.0123, R = 0.826, P = 0.0030). In addition, the analytical results indicate that water vapor content are decreasing obviously between latitude of 36°N and 36°S(b = 0.0224, R = 0.892, P = 0.0005), especially between latitude of 0°N and 36°N(b = 0.0263, R = 0.931, P = 0.0001), while the water vapor concentrations are increasing slightly in the Arctic regions(b = 0.0028, R = 0.612, P = 0.0590). The decreasing and spatial variation of water vapor content regulates the effects of carbon dioxide which is the main reason of the trend in global surface temperatures becoming nearly flat since the late 1990 s. The spatio-temporal variations of water vapor content also affect the growth and spatial distribution of global vegetation which also regulates the global surface temperature change, and the climate change is mainly caused by the earth's orbit position in the solar and galaxy system. A big data model based on gravitational-magmatic change with the solar or the galactic system is proposed to be built for analyzing how the earth's orbit position in the solar and galaxy system affects spatio-temporal variations of global water vapor content, vegetation and temperature at large spatio-temporal scale. This comprehensive examination of water vapor changes promises a holistic understanding of the global climate change and potential underlying mechanisms.

Assessment of rice fields by GIS／GPS-supported classification of MODIS data

The new Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite image offers a large choice of opportunities for operational applications. The 1-km Advanced Very High Resolution Radiometer (AVHRR) image is not suitable for retrieval of field level parameter and Landsat data are not frequent enough for monitoring changes in crop parameters during the critical crop growth periods.A methodology to map areas of paddy fields using MODIS,geographic information system (GIS) and global position system (GPS) is introduced in this paper. Training samples are selected and located with the help of GPS to provide maximal accuracy.A concept of assessing areas of potential cultivation of rice is suggested by means of GIS integration. By integration of MODIS with GIS and GPS technologies the actual areas of rice fields in 2002 have been mapped. The classification accuracy was 95.7% percent compared with the statistical data of the Agricultural Bureau of Zhejiang Province.

Characteristics and generations of internal wave in the Sulu Sea inferred from optical satellite images

Characteristics and generation of internal waves(IWs)in the Sulu Sea are studied using Moderate-Resolution Imaging Spectroradiometer(MODIS)and Visible Infrared Imaging Radiometer Suite(VIIRS)images taken from October 2016 to September 2019.Satellite observations show that IWs in the Sulu Sea mainly located in the shallower western areas with occasional observations in the deeper eastern regions.The dominant length of wave crest(LWC)of IWs is between 50 and 150 km with the largest LWC reaching over 300 km.The analysis of temporal distributions of IWs shows that March has the most IWs and July has the least.Further analysis shows that the seasonal variation is mainly due to the cloud contamination of optical satellite images.New generation sites of IWs are analyzed using satellite images.Six possible generation sites for IWs in the western Sulu Sea and one generation site for IWs in the eastern Sulu Sea are found using the ray-tracing method.Multi IW sources in the same strait are found,which may be due to the seawater fl ow over the strait in diff erent directions.The analysis shows that IWs with long wave crest in the Sulu Sea is a combined eff ort of all straits between small islands in the Sulu Archipelago.Remote generated IWs with long wave crest in the eastern Sulu Sea are studied,which are generated at the straits around(121.5°E,6°N)by the nonlinear evolution of internal tide originated from the Sulu Archipelago.

Vegetation Phenology in Permafrost Regions of Northeastern China Based on MODIS and Solar-induced Chlorophyll Fluorescence

Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI),extracted from the Moderate Resolution Imaging Spectrometer(MODIS),are widely used to monitor phenology by calculating land surface reflectance.However,the applicability of the vegetation index based on‘greenness'to monitor photosynthetic activity is hindered by poor observation conditions(e.g.,ground shadows,snow,and clouds).Recently,satellite measurements of solar-induced chlorophyll fluorescence(SIF)from OCO-2 sensors have shown great potential for studying vegetation phenology.Here,we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China,exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI.The results show that NDVI has obvious SOS advance and EOS lag,and EVI is closer to SIF.The growing season length based on SIF is often the shortest,while it can represent the true phenology of vegetation because it is closely related to photosynthesis.SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices.We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions.The results show that the length of growing season of vegetation in predominantly continuous permafrost(zone I)is longer than in permafrost with isolated taliks(zone II).Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.

Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products

Clouds can influence climate through many complex interactions within the hydrological cycle. Due to the important effects of cloud cover on climate, it is essential to study its variability over certain geographical areas. This study provides a spatial and temporal distribution of sky conditions, cloudy, partly cloudy, and clear days, in Iran. Cloud fraction parameters were calculated based on the cloud product （collection 6_L2） obtained from the Moderate Resolution Imaging Spectroradiorneter （MODIS） sensors on board the Terra （MOD06） and Aqua （MYD06） satellites. The cloud products were collected daily from January 1, 2003 to December 31, 2014 （12 years） with a spatial resolution of 5 km × 5 km. First, the cloud fraction data were converted into a regular geographic coordinate network over Iran. Then, the estimations from both sensors were analyzed. Results revealed that the maximum annual frequency of cloudy days occurs along the southern shores of the Caspian Sea, while the minimum annual frequency occurs in southeast Iran. On average, the annual number of cloudy and clear-sky days was 88 and 256 d from MODIS Terra, as compared to 96 and 244 d from MODIS Aqua. Generally, cloudy and partly cloudy days decrease from north to south, and MODIS Aqua overestimates the cloudy and partly cloudy days compared to MODIS Terra.

Spatio-temporal Variation of Water Heat Flux Using MODIS Land Surface Temperature Product over Hulun Lake,China During 2001–2018

Heat flux is important for studying interactions between atmosphere and lake.The heat exchange between air-water interfaces is one of the important ways to govern the temperature of the water surface.Heat exchange between the air-water interfaces and the surrounding environment is completed by solar radiation,conduction,and evaporation,and all these processes mainly occur at the air-water interface.Hulun Lake was the biggest lake which is also an important link and an indispensable part of the water cycle in Northeast China.This study mapped surface energy budget to better understand spatial and temporal variations in Hulun Lake in China from 2001 to 2018.Descriptive statistics were computed to build a historical time series of mean monthly heat flux at daytime and nighttime from June to September during 2001–2018.Remote sensing estimation methods we used was suitable for Hulun Lake(R2=0.81).At month scale,shortwave radiation and latent heat flux were decrease from June to September.However,the maximum sensible heat flux appeared in September.Net longwave radiation was the largest in August.The effective heat budget showed that Hulun Lake gained heat in the frost-free season with highest value in June(686.31 W/m2),and then steadily decreased to September(439.76 W/m2).At annual scale,net longwave radiation,sensible heat flux and latent heat flux all show significant growth trend from 2001 to 2018(P<0.01).Wind speed had the well correlation on sensible heat flux and latent heat flux.Water surface temperature showed the highest coefficient in sensitivity analysis.

The study of estimation method of broadband emissivity from EOS/MODIS data

The broadband emissivity is an important parameter for estimating the energy balance of the Earth. This study focuses on estimating the window （8 -12 μm） emissivity from the MODIS （mod- erate-resolution imaging spectroradiometer） data, and two methods are built. The regression method obtains the broadband emissivity from MODllB1 - 5KM product, whose coefficient is developed by using 128 spectra, and the standard deviation of error is about 0.0118 and the mean error is about O. 0084. Although the estimation accuracy is very high while the broadband emissivity is estimated from the emissivity of bands 29, 31 and 32 obtained from MOD11B1 _ 5KM product, the standard deviations of errors of single emissivity in bands 29, 31, 32 are about 0.009 for MOD11B1 5KM product, so the total error is about O. 02 and resolution is about 5km × 5km. A combined radiative transfer model with dynamic learning neural network method is used to estimate the broadband emis- sivity from MODIS 1B data. The standard deviation of error is about 0.016, the mean error is about 0.01, and the resolution is about 1 km x 1 km. The validation and application analysis indicates that the regression is simpler and more practical, and estimation accuracy of the dynamic learning neural network method is higher. Considering the needs for accuracy and practicalities in application, one of them can be chosen to estimate the broadband emissivity from MODIS data.

Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin

The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.

Dynamic Drought Monitoring in Guangxi Using Revised Temperature Vegetation Dryness Index

Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.