Dr. Shunlin Liang has led a group of research scientists and graduate students to conduct the agricultural remote sensing in the following areas: 1) crop growth monitoring using the Global LAnd Surface Satellite (GLASS) products, 2) drought monitoring based on evapotranspiration, 3) crop productivity, and 4 climate effects.
1.Crop growth monitoring
We have been developing the state-of-the-art inversion algorithms for estimating crop biophysical parameters from various satellite data, such as leaf area index (LAI)(Wang et al. 2010b; Xiao et al. 2014b), fraction of absorbed photosynthetically active radiation (FAPAR)(Tao and Liang 2014; Xiao et al. 2014a), incident PAR(Liang et al. 2006; Wang et al. 2010a; Zhang et al. 2014; Zheng and Liang 2011), surface energy budget components (Liang et al. 2013a). Many of these parameters are highly related to crop growth conditions, for example, LAI and FAPAR are good indicators of crop growth, and incident PAR is a critical input to the crop growth models.
Some of the developed algorithms have been implemented to generate the long-term high-quality global satellite products, called the Global Land Surface Satellite (GLASS) products. Phase-1 GLASS products include LAI, shortwave albedo, longwave emissivity, incident shortwave radiation, and incident PAR (Liang et al. 2013b; Liang et al. 2013c), which are being distributed at http://www.glcf.umd.edu/ and http://glass-product.bnu.edu.cn/. Phase-2 GLASS products, currently under production, also include fractional vegetation cover, FAPAR, evapotranspiration (ET), surface skin temperature, longwave net radiation, all-wave net radiation, and gross primary productivity (GPP).
2.Drought monitoring based on evapotranspiration
The ratio of actual ET to the potential ET is a good indicator of the crop drought conditions. Different actual ET remote sensing estimation algorithms have been developed (Chen et al. 2014; Li et al. 2014; Sun et al. 2013; Sun et al. 2012; Wang et al. 2010c, d; Wang and Liang 2008; Xia et al. 2014; Yao et al. 2014a; Yao et al. 2010a; Yao et al. 2014b; Yao et al. 2013; Yao et al. 2010b; Yao et al. 2012) (Xu et al. 2014).
Another research direction for estimating ET is to assimilate satellite products into land surface models by using advanced data assimilation methodology. The related results are published in the recent publications (Bateni and Liang 2012; Qin et al. 2009; Qin et al. 2007; Xu et al. 2015; Xu et al. 2014; Xu et al. 2011a; Xu et al. 2011b).
3.Crop productivity
Two research directions have been pursued. The first is to estimate the gross primary productivity of vegetation plants, including crops, using the radiation efficiency models from satellite observations (Cai et al. 2014; Li et al. 2013). The second is to estimate crop yields by assimilating satellite products into the crop growth models (Fang et al. 2011; Fang et al. 2013; Fang et al. 2008).
4.Climatic effects of agricultural irrigation
Assessing the climatic impacts of agricultural irrigation has been carried out mainly from remote sensing (Zhu et al. 2012; Zhu et al. 2011).
>