Vegetation water content retrieval using MERIS and AATSR data over the Loess Plateau

Vegetation water content is an important indicator of the degree of stress experienced by plants. This paper explored the potential of using the remote sensing data of MERIS （Medium Resolution Imaging Spectrometer） and AATSR （Advanced Along-Track Scanning Radiometer） collected during the Loess Plateau land surface process field Experiment 2005 （LOPEX05） to map and monitor vegetation water content for corn canopies. By comparing with the daily ground observation to validate the satellite reflectance data, we established relationships between the vegetation water content and satellite remotely sensed indices. The two indices studied were the NDVI （Normalized Different Vegetation Index） from MERIS and the NDWI （Normalized Different Water Index） from AATSR. We used the daily ground observation to demonstrate that the NDVI was saturated during the study period while the NDWI continued to reflect the changes in VWC. We found that NDWI, based on near infrared channel （0.855-0.875μm） and short wave infrared wavelength channel （1.58-1.64μm）, is suggested to be more suitable and robust approach for retrieval of vegetation water content. The proposed method was validated with experimental field data with biases that are 1.0314 kg/m2 and 0.9413 kg/m2 respectively. Therefore the NDWI was recommended to retrieval the vegetation water content.

Inversion of surface vegetation water content based on GNSS-IR and MODIS data fusion

Obtaining high-precision,long-term sequences of vegetation water content(VWC)is of great significance for assessing surface vegetation growth,soil moisture,and fire risk.In recent years,the global navigation satellite system-interferometric reflection(GNSS-IR)has become a new type of remote sensing technology with low cost,all-weather capability,and a high temporal resolution.It has been widely used in the fields of snow depth,sea level,soil moisture content,and vegetation water content.The normalized microwave reflectance index(NMRI)based on GNSS-IR technology has been proven to be effective in monitoring changes in VWC.This paper considers the advantages and disadvantages of remote sensing technology and GNSS-IR technology in estimating VWC.A point-surface fusion method of GNSS-IR and MODIS data based on the GA-BP neural network is proposed to improve the accuracy of VWC estimation.The vegetation index products(NDVI,GPP,LAI)and the NMRI that unified the temporal and spatial resolution were used as the input and output data of the training model,and the GA-BP neural network was used for training and modeling.Finally,a spatially continuous NMRI product was generated.Taking a particular area of the United States as a research object,experiments show that(1)a neural network can realize the effective fusion of GNSS-IR and MODIS products.By comparing the GA-BP neural network,BP neural network,and multiple linear regression(MLR),the three models fusion effect.The results show that the GA-BP neural network has the best modeling effect,and the r and RMSE between the model estimation result and the reference value are 0.778 and 0.0332,respectively;this network is followed by the BP neural network,in which the r and RMSE are 0.746 and 0.0465,respectively.MLR has the poorest effect,with r and RMSE values of 0.500 and 0.0516,respectively.(2)The spatiotem-poral variation in the 16 days/500 m resolution NMRI product obtained by GA-BP neural network fusion is consistent with that in the experimental area.Through the testing of GNSS stations that did not participate in the modeling,the r between the estimated value of the NMRI and the reference value is greater than 0.87,and the RMSE is less than 0.049.Therefore,the method proposed in this paper is optional and effective.The spatially continuous NMRI products obtained by fusion can reflect the changes in VWC in the experimental area more intuitively.