Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity

Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue.The resulting model,the‘Humidity calibrated Drought Condition Index’(HcDCI)was applied for the years 2001 to 2019 in form of a case study to Weihai County,Shandong Province in East China.Design and development are based on a linear combination of the Vegetation Condition Index(VCI),the Temperature Condition Index(TCI),and the Rainfall Condition Index(RCI)using multi-source satellite data to create a basic Drought Condition Index(DCI).VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer)data,while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data.For reasons of accuracy,the decisive coefficients were determined by the relative humidity of soils at depth of 10-20 cm of particular areas collected by an agrometeorological ground station.The correlation between DCI and soil humidity was optimized with the factors of 0.53,0.33,and 0.14 for VCI,TCI,and RCI,respectively.The model revealed,light agricultural droughts from 2003 to 2013 and in 2018,while more severe droughts occurred in 2001 and 2002,2014-2017,and 2019.The droughts were most severe in January,March,and December,and our findings coincide with historical records.The average temperature during 2012-2019 is 1℃ higher than that during the period 2001-2011 and the average precipitation during 2014-2019 is 192.77 mm less than that during 2008-2013.The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities.The model thus,demonstrates its capability to reveal drought periods in detail,its transferability to other regions and its usefulness to take future measures.

Research on Monitoring of Soil Humidity Based on AMSR-E Data

[Objective] The aim was to establish AMSR-E soil humidity monitoring model to realize the real-time monitoring of soil humidity.[Method] By dint of evaporation(small type) in Guangxi,daily precipitation,daily average maximum temperature,daily minimum relative humidity,≤ 5 mm precipitation day,as well as AMSR-E soil humidity data,with Stepwise regression method,soil humidity real-time monitoring was studied based on GIS technology,and monitoring result.[Result] The low soil humidity in Guangxi on September 23 in 2005 mainly distributed in northeast Guangxi and central Guangxi,including Guilin City,Liuzhou City,Laibin City,Hezhou City,Guigang City,Nanning City,Hechi City and Baise City.[Conclusion] The AMSR-E statistical model based on meteorological observation data can be applied in real-time monitoring of soil humidity.

Application of the Reciprocal Analysis for Sensible and Latent Heat Fluxes with Evapotranspiration at a Humid Region

Evapotranspiration acts an important role in hydrologic cycle and water resources planning. But the estimation issue still remains until nowadays. This research attempts to make clear this problem by the following way. In a humid region, by applying the Bowen ratio concept and optimum procedure on the soil surface, sensible and latent heat fluxes are estimated using net radiation (Rn) and heat flux into the ground (G). The method uses air temperature and humidity at a single height by reciprocally determining the soil surface temperature (Ts) and the relative humidity (rehs). This feature can be remarkably extended to the utilization. The validity of the method is confirmed by comparing of observed and estimated latent (lE) and sensible heat flux (H) using the eddy covariance method. The hourly change of the lE, H, Ts and rehs on the soil surface, yearly change of lE and H and relationship of estimated lE and H versus observed are clarified. Furthermore, monthly evapotranspiration is estimated from the lE. The research was conducted using hourly data of FLUXNET at a site of Japan, three sites of the United States and two sites of Europe in humid regions having over 1000 mm of annual precipitation.

Multivariate Analysis, Description, and Ecological Interpretation of Weed Vegetation in the Summer Crop Fields of Anhui Province, China

Two surveys were conducted to investigate weed vegetation in a 153-hm^2 sampling area of summer crop fields from Anhui Province, China, through visual scoring of the level of weed infestation compared with summer crops on a seven-class scale. In total, 155 sampling sites were selected in the field based on crops, tillage, rotation systems, geographical regions, and soil types across the province. Data on weed communities and environmental factors were collected and analyzed through principal component analysis （PCA） and canonical correspondence analysis （CCA）, and the output was interpreted ecologically. Results showed that the main factors influencing the structure and distribution of weed communities in summer crop fields were the soil submersion period, latitude, and soil type and pH. The CCA indicated a significant relationship between weed dominance and soil submersion duration, latitude, and soil pH. From the result of the PCA and CCA ordination, the 155 sampling sites could be divided into three groups based on geographic and floristic composition, as well as weed abundance. The southern dry land group, which was characterized by a double-cropping system in the hilly regions of southern and central Anhui Province with a continuous summer crop and an autumn dry land crop, was dominated by Galium aparine Linn. var. tenerum （Gren. et Godr） Robb., Avenafatua L., and Veronica persica Poir. The northern dry land group, which had the same cropping system as the southern dry land group, was dominated by G. aparine var. tenerum, Galium tricorne Stokes, Descurainia sophia （L.） Schur., and Lithospermum arvense L. in the North Anhui Province, China. These two dry land groups could be combined into one large dry land group, in which the Galium weed vegetation type dominated. The third group was the paddy soil group, which was characterized by a continu- ous summer crop and double- or triple-cropping systems of rice, and prevailed in the south and central areas of Anhui Province; Alopecurus aequalis Sobol. was the dominant weed in this group. Other main weeds in this group included Malachium aquaticum （L.） Fries, Stellaria alsine Grimm, Alopecurusjaponicus Steud., and Lapsana apogonoides Maxim. Thus, the weed community distributions in this group were described as the Alopecurus weed vegetation type. The paddy soil group could be divided into two subgroups, one southern and one central paddy soil subgroup. A strategy for integrated weed management is suggested according to the weed distribution pattern. The present study serves as a good example of how a quantitative research method was used to associate a visual estimate of weed infestation with multivariate analyses, such as PCA and CCA, and how this method can be applied to the study of weed vegetation on arable land.

STUDY ON MIXED MODEL OF NEURAL NETWORK FOR FARMLAND FLOOD/DROUGHT PREDICTION

The paper concerns a flood/drought prediction model involving the continuation of time series of a predictand and the physical factors influencing the change of predictand.Attempt is made to construct the model by the neural network scheme for the nonlinear mapping relation based on multi-input and single output.The model is found of steadily higher predictive accuracy by testing the output from one and multiple stepwise predictions against observations and comparing the results to those from a traditional statistical model.