Study On Integration Of Remote Sensing Information And Crop Model Based On Ensemble Kalman Filter

The food issue is a top priority of agricultural production. Accurate, real-time information on crop monitoring and yield forecasting is an important guarantee of the agricultural management and food security. Crop growth simulation model is a powerful tool to monitor crop growth and development, and estimate crop yield. However, there exist some difficulties in applying the crop model which is developed based on site in a wide range of region, for example, lack of crop parameters, agricultural management, and planting layout information, and not easy to solve the verification problem of model applicability on the regional scale. With the development of remote sensing technology, remote sensing has become main data support to improve crop model simulation. Crop growth model is a simplification of the actual crop growth and development process, not only model errors, there're also errors in the initial conditions and boundary conditions of a crop model. Thus, with the forward integration of crop model, the gap between modeled value and true value will become increasingly large. Remote sensing information can display the spatial distribution of crop canopy elements, that is to provide a kind of objective observations of crop canopy elements. Therefore, it is very important to combine crop model with remote sensing observations. On the one hand, crop growth models are used to constrain remote sensing inversion model; on the other hand, remote sensing data are used to adjust the trajectory of a crop model and the accumulated errors of a crop model will get released. Different sources, spatial and temporal resolution data are integrated to express crop growth and development process on a variety of spatial and temporal scales better. Data assimilation method appears, becomes more practical and provides a feasible way for us to achieve this goal.First, considering the need for regional crop yield estimation, crop distribution patterns in the study area was studied and maize-growing area was extracted by comparing two maize mapping schemes comprehensively. Second, taking into account the problems in the domestic and foreign research and application, a foreign model (PyWOFOST) built on Ensemble Kalman Filter (EnKF) was introduced which coupled remote sensing information and crop model. First, we modified and improved the PyWOFOST model to take LAI as the joint point of the crop model (WOFOST) and remote sensing information and be applicable to the maize-growing area in Northeast China. MODIS LAI data were used as external assimilation data to simulate maize LAI, yield and development stage with the PyWOFOST model on agro-meteorological stations. Compared the results modeled by WOFOST, the results modeled by PyWOFOST which were closer to the observed values reveals the advantages of crop simulation based on data assimilation methods. At the same time, the impact of uncertainty of remote sensing observations (MODIS LAI) and model parameter (TSUM1) on the assimilation simulation results was analyzed deeply in our work. Finally, on the basis of maize mapping, the PyWOFOST model was used to estimate maize yield on a regional scale. Then the regional yield estimation result was verified by comparing with the statistical yield of maize of each prefecture-level city of three provinces in Northeast China. The result shows that the yield estimation scheme based on EnKF is applicable and feasible. The main research work and preliminary conclusions are as follows:1. Study on crop classification by remote sensing based on spectral analysis method and maize mapping.Spectral analysis method (SAM) was used to identify and classify the crop type distribu-tion in the study area, and two mapping schemes were compared comprehensively to extract maize- growing areas in the study area.1) Considering the needs to regional crop yield estimation and the limitations of measured data, MODIS products (including land cover type data, NDVI data and reflectance data) were used to extract time-series NDVI curves of various crops (soybean, maize, rice and wheat) pure pixels. And then used spectral analysis method (SAM) to identify and classify the crop type distribution in study area and extract the maize-growing area further (Mapping Scheme 1).2) The area derived from the crop classification result was compared with the crop planting area from statistical data, and the results showed that the correlation coefficient of soybeans, maize and rice were 0.858,0.715 and 0.927 respectively, and the coefficient of determination of above crops were 0.770,0.710,0.686 respectively. It revealed that the accuracy of crop classification based on spectral analysis method was ideal. In particular, for the accuracy of maize mapping, the correlation coefficient of maize inversion area extracted from crop classification result and statistical area of Heilongjiang, Jilin and Liaoning Province were 0.9527,0.6528,0.3462, with R2=0.8291,0.813,0.6883 respectively. The maize mapping accuracy of Liaoning Province was not satisfactory, so we combined 1km land cover type data from IGSNRR (Institute of Geographic Sciences and Natural Resources Research, CAS) with DEM data from NASA to extract the area with elevation range from 0-400m and dryland area accounted for more than 80% of a pixel area as the maize-growing area of Liaoning Province (Mapping Scheme 2).2. Applicability of PyWOFOST Model Based on Ensemble Kalman Filter in Simulating Maize Yield in Northeast China.The result showed that, the modeled results (mainly maize LAI, yield, development stage) after assimilating MODIS LAI were closer to observed values by comparing with the results modeled by WOFOST.1) The errors of maize production before and after assimilation at different uncertainty levels of TSUM1(0,10,20,30℃) of 20 agro-meteorological stations without the impact of meteorological disasters were 14.04%,12.71%,11.91%,10.44% and 10.48% respectively. The correlation coefficient and determination coefficient between modeled yield before assimilation and observed yield of maize was 0.681 and 0.597 respectively; the correlation coefficient between modeled yield after assimilation and observed yield of maize were all above 0.7 at different uncertainty levels of TSUM1(0,10,20,30℃)and the determination coefficient were 0.631,0.678,0.724 and 0.697 respectively. In a word, the modeled results after assimilation were better than before.2) LAI modeled by PyWOFOST which more in line with the change trend of maize LAI were generally closer to observed LAI than LAI modeled by WOFOST, part of LAI values after assimilation nearly coincided with observed LAI values.3) The mean error of development stage between modeled value of WOFOST and observed value was 3.33d; the mean error of development stage between modeled value of PyWOFOST at different uncertainty levels of TSUM1(0,10,20,30℃) and observed value were 3.42,4.29,5.0 and 5.54d respectively.4) The modeled results after assimilation (LAI, yield, development stage) all fully proved that PyWOFOT model was applicable in monitoring crop growth and estimating yield in maize-growing area in Northeast China, also revealed the advantages of assimilating remote sensing information into crop model to model LAI and estimate yield based on EnKF.5) Assimilation simulation results of PyWOFOST were better than WOFOST simulation results, but it did not exist an uncertainty level on which assimilation simulation results (yield and LAI) of all sites were superior to other uncertainty levels.6) The modeled ability of crop model directly determines the modeled result with assimilation whether good or not. The crop model itself is not very ideal for simulating crop growth and development under serious disaster conditions. Although the modeled crop yield after assimilation has improved than before, it still exist a gap between the modeled and observed value under serious disaster conditions.3. Regional maize production estimation based on PyWOFOST model1) Taking the maize-growing area in Northeast China as the study area, used MODIS LAI data as external assimilation data to estimate maize yield in the study area. Then verified the regional maize yield result based on EnKF by comparing with the statistical yield of 35 prefecture-level cities. The results showed that, the error of maize yield with assimilation were less than 15% in 57.14% of the study area; the correlation coefficient and coefficient of determination between modeled yield with assimilation and statistical yield were 0.875 and 0.806 respectively; the minimum and maximum standard deviation of modeled yield were 76.16(kg/ha) of Tieling city and 1856.45(kg/ha) of Siping city respectively. Overall, the accuracy of regional crop yield estimation was ideal.2) The genetic parameters of crop varieties has larger effect on regional crop yield estimation, it's better to choose finer genetic parameters on regional scale; the accuracy of crop mapping was also one of the main factors for affecting the accuracy of regional crop yield estimation, consequently, more accurate crop type distribution map should be used.3) Overall, it was feasible to estimate regional crop yield under normal crop growth or mild-disaster conditions with assimilating LAI remote-sensing observations into crop model based on EnKF, but it was still not very satisfactory for simulating crop growth and estimating crop yield under serious disaster conditions.