Research On Method Of Maize Growth Monitoring Using Light Use Efficiency Model And Quantitative Remote Sensing

Timely and accurate monitoring of crop growth is very important for the decision-making onnational food security such as food pricing, grain reserve and food trade. The crop growth monitoringmethod based on remote sensing vegetation indices through the way of non-contact and long distancedetection can timely get large area crop growth information, is the main method in current cropmonitoring, and plays an important role in the decision making process of the national food security.The monitoring method based on quantitative remote sensing extracts crops and environmentalparameters from spectral information by using radiation transfer model and mathematical method, thenconducts a comprehensive monitoring of crop growth status. It is one of the important directions of cropgrowth monitoring method research, and how to apply more quantitative remote sensing methods andproducts in crop growth monitoring is the main content of this study. Crop light use efficiency modelhas its unique advantages in application of quantitative remote sensing. By improving the algorithm forlight use efficiency (LUE) or fraction of absorbed photosynthetically active radiation (FAPAR) in theLUE model, more data products of quantitative remote sensing can be used in crop growth monitoring,which is an efficient methodology to integrate quantitative remote sensing and LUE model into cropgrowth monitoring. This study defines biomass as the monitor index. The main objective of this study isto improve the method of crop growth monitoring by combining the LUE model and quantitativeremote sensing techniques.A series of the crop growth monitoring field experiments and regional remote sensing experimentswere conducted in North China Plain, which is the main maize growing region in China as well as in theworld. Regarding the lack of modeling of leaf chlorophyll content (LCC) and photosynthetically activeradiation (PAR) density effects in most crop LUE models, new models that biomass responds to LCCand PAR, including LUEChlmodel, FAPARChlmodel, LUEChl+FAPARChlmodel and LUEPARmodel werebuilt by improving the algorithms of LUE or FAPAR. LCC is a key role in estimating LUE and FAPAR,so algorithms of leaf chlorophyll content derived from remote sensing were investigated. CombinedLUEChl+FAPARChlmodel and chlorophyll content inversion method, the author implemented the maizegrowth monitoring using HJ-1multi-spectral remote sensing data. Through this study, the mainconclusions are as follows.1. Compared to the results (RMSE=390.9g/m2，RE=21.5%) of a LUE model that lack of modelingof leaf chlorophyll content effect, LUEChlmodel (RMSE=91.7g/m2，RE=5.1%), FAPARChlmodel(RMSE=173.8g/m2, RE=9.4%), LUEChl+FAPARChlmodel (RMSE=52.9g/m2, RE=2.6%) and LUEPARmodel (RMSE=75.2g/m2, RE=4.1%) significantly reduced biomass estimation error. LUEChl+FAPARChlmodel especially broke through limitations of many traditional LUE models by improving thealgorithms of LUE and FAPAR, is suitable for using chlorophyll content for crop growth monitoring.Compared NPP results of LUEChlmodel and LUEPARmodel, LUEPARmodel can lower NPP errors thatinduce by PAR density, is more accurate for NPP estimation in clear and overcast sky, performs a goodpotential in crop growth monitoring. 2. The study of retrieve LCC using spectral index showed that, MTCI, Datt and SRCI performsgood correlation with LCC at whole growth period, is suitable for retrieve LCC at different periods.Canopy reflectance model inversion research showed that the method that retrieved leaf area index(LAI) firstly according to red and near infrared reflectance then retrieved LCC by LAI and green or rededge reflectance is effective, and the results of red edge reflectance has the same accuracy with greenreflectance in chlorophyll content inversion.3. A monitoring method of maize growth based on a LUE model that LCC parameterized LUE andFAPAR was established for the first time, and realized using HJ-1multispectral images. In this method,the spatial distribution of maize was extracted by normalized difference vegetation index curve thatbuilt by multi-temporal multispectral images, maize LAI and LCC were retrieved based on ACRMmodel and surface reflectivity, finally LUE, FAPAR and biomass response to LCC were estimated.In conclusion, this study established a series of new light use efficiency models that consideredLCC effects, which prepared theory and models for applying more quantitative remote sensing productsto monitoring crop growth. By HJ-1multispectral data, a monitoring method of maize growth based onquantitative remote sensing and a LUE model parameterize by LCC was carry out, which provide agood reference case for quantitative monitoring of crop growth using high resolution multispectral data.