Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

Abstract

Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (R 2 = 0.73, root-mean-square error (RMSE) = 30.61 g/m 2 ), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R 2 = 0.77, RMSE = 17.22 g/m 2 ), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management.

Mapping Above-Ground Biomass of Winter Oilseed Rape Using High Spatial Resolution Satellite Data at Parcel Scale under Waterlogging Conditions

“…In this stage, although the majority of the oilseed rape pods were green, the proportion of oilseed rape pods was high and the canopy became lager at this stage showing relatively large scattering, which may be due to the rapidly growth of the plant canopy biomass or the uncertainty of measured AGB with respect to tiny gap probabilities of the sampling. Moreover, many previous study have used NDVI time series derived from Landsat and MODIS data for forest and grassland AGB estimation [22,68].…”

“…For instance, the Advanced Very High Resolution Radiometer (AVHRR) (1.1 km spatial resolution at nadir), Moderate-resolution Imaging Spectroradiometer (MODIS) (two bands are imaged at a nominal resolution of 250 m at nadir, five bands at 500 m, and the remaining 29 bands at 1 km), and Landsat TM/ETM+ (30 m spatial resolution) have been used to estimate the canopy condition [22][23][24]. However, in many developing countries, smallholder farming is the main livelihood support for the majority of the population.…”

Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series

“…It exploits the complementary aspects of data collected by the MODIS and Landsat sensors to produce fused data with Landsat resolution from MODIS imagery [17]. It has been applied and has performed well in several studies of subjects as diverse as public health [18], forest disturbance and regrowth monitoring [19,20], vegetation phenology analysis [21], efforts to improve land cover classification accuracy [22], and estimation of biophysical parameters such as evapotranspiration [23], leaf area index [24], plant biomass [25], and gross primary productivity [26]. STARFM is not restricted to MODIS and Landsat data, but use of other data sources appears to have been rarely reported [27].…”

Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

“…However, recently, efforts have been made to merge data properties to obtain images with high spatial and temporal resolutions by using algorithms, such as the spatial and temporal adaptive reflectance fusion model (STARFM) (Zhang et al, 2016), which could support photosynthetic activity evaluations with higher temporal and spatial resolutions that partially maintain the original coarse data. Therefore, based on a method for estimating the growth index at a moderate scale using the Modis database, Xu et al (2013) observed a predominance of areas with stable or balanced growth in temperate grassland development.…”