Acreage Extraction And Biomass Estimation Of Paddy Rice Based On Microwave Remote Sensing And Methane Emissions Simulation From Paddy Fields

Paddy rice is one of the most important crops in the world, and the major staple food inChina. It accounts for more than 40% of food production for the population of 1.3 billions.Rice yield is one of the major concerns related to farm practice and national security. Remotesensing on rice plays important role in yield estimation, and provides key information foranalyzing and assessing the impact of paddy field on agriculture and environmentsustainability, food and water security and greenhouse gas mitigation. Fuyang City andpartition of Haining City of Zhejiang Province were selected as study area. The objective ofthe study is to integrate microwave remote sensing technology and ecology model forextracting paddy rice area, retrieving rice biophysical parameters and evaluating impacts ofpaddy fields on climate environment.Conventional methods in estimating rice area are not only time and labor consuming butalso questioned in accuracy. With its advantage of acquiring geo-information in timely,speedy and objective way over large areas, remote sensing is one powerful approach inextracting rice area to conventional methods. Otherwise, quantitative retrieval of rice structureparameters by remote sensing technology is a key topic in rice biomass and yield estimation.Attempt was made to develop one first-order radiation transfer model for simulating ricescattering character and retrieving physical parameters, which is a new means to apply remotesensing technology in rice biomass estimation. Analyzing the impacts of paddy field onclimate currently becomes a hot topic in ecology community. A process-based ecologicalmodel, DNDC (Denitrification and Decomposition) model was utilized to simulate methaneemissions from rice field during the growing season. With scenario analysis, feasible farmmanagement method was proposed for methane mitigation, which provides scientific basis toreduce and control methane emission from paddy field. Specific contents and results weresummarized below: (1) Pseudo-color composite imagery derived from three-date ALOS/PALSAR imagesacquired in key dates during rice growing season in Fuyang City was used to discriminatedifferent land cover with support vector machine algorithm (SVM-Support Vector Machine).Temporal variation of backscattering coefficient during rice growing season was seriouslytaken into account in extracting the target object due to unique character of paddy rice, whichvaries greater than other land cover. Vector segmentation was taken in temporal dimensionspace to extract rice planting area. The derived map of rice planting area has high accuracy,which was validated by ALOS/PRISM imagery with a spatial resolution of 2.5 meter, routinesurvey data of land use/cover and in situ field investigation. The study results indicated thatpaddy rice could be extracted with accuracy of around 90%.(2) The SVM classifier used above and stepwise manual method were integrated toextract paddy rice in partition of Haining City, Zhejiang Province. And then, the rice thematicmap was taken as a mask to subset the raw PALSAR image for obtaining backscatter image.Some field measured structure parameters acquired synchronically with ALOS satelliteoverpassing the study area were used to modify the inter parameters of a 1^st-order forestcanopy scattering model for developing rice backscattering coefficients simulation model.The simulated results were validated by PALSAR imagery-based backscattering coefficients,good agreement was obtained.(3) Taken paddy rice imagery backscattering coefficients and radar signal beam incidenceangle as inputs, the modified 1^st-order radiation transfer model and genetic optimizationalgorithm were utilized to retrieve two-rice structure parameters, plant height and density, ofpaddy rice in this study area at the environment of Matlab. One empirical allometric equationwas derived from in situ measurements, which was used to estimate and map rice biomass atspatial scale in the study area. This result would also provide one key parameters for remotesensing estimation of rice yield and carbon balance in agricultural ecosystem quantification.(4) The rice planting area extracted from multispectral ALOS/AVNIR-2 imagery coveringHaining and farm management practice information were taken as inputs to run thebiogeochemical DNDC model for simulating rice paddy ecology process. Furthermore,scenario analysis and quantitative estimation were taken to estimate methane emission frompaddy fields given various farm management practice. Efficient method was also taken up with for controlling and mitigating methane emissions from paddy fields.In summary, this study showed the feasibility of using new multi-temporal microwaveRadar ALOS data acquired in key dates during rice growing season to extract paddy ricegrowing area was tested. A modified rice backscatter model was developed on the basis oforiginal radiation transfer model for simulating paddy rice backscattering characeteristics. Themodified model and Genetic Algorithm Optimization Tool were synergized to reversephysical parameters and map rice biomass at spatial scale in the study area. At last, on thebasis of paddy rice planting area and farming practice data, biogeochemical DNDC modelwas utilized to simulate quantitatively estimating methane (CH₄) emission from paddy fieldsand represented some specific mitigation scheme.