## 1 Introduction  
HJ-1 satellites, with high spatial and moderate temporal and spectral resolution data, are the first small satellite constellations dedicated to environmental and disaster monitoring and forecasting in China [1,2]. HJ-1 charge-coupled device (CCD) data are widely used in land use classification [3], and also used in wetland classification based on object-oriented classification methods, resulting in a low-cost and effective technical means to obtain high-precision wetland distribution information [4]. In other applications, the improved Carnegie-Ames-Stanford Approach (CASA) model is used to estimate the net primary productivity of the vegetation [5,6], and the soil organic matter content in the experimental area was previously monitored by HJ-1 data [7, 8, 9].  
However, due to bad weather, the limited quantity restricts the quality of remote sensing (RS) extraction, the quality of HJ-1 two-level products is poor, geometric calibration is not accurate, and available data are reduced. HJ-1 CCD data are often used in combination with other RS data [10]. Fusing with multi-sensor observations is an effective way to improve the observation frequency [11,12]. The leaf area index (LAI) is usually calculated using the multi-sensor observation data set, which is constructed by sensors of HJ-1 CCD and Landsat 8 OLI data, and results have shown that the fusion data set can produce LAI products with reliable precision and continuous time resolution [13,14].  
The temporal characteristics of surface reflectivity is a key factor for land surface process research, and has a great significance for the prediction of future surface reflectivity,such as hydrological forecasting, crop pest and disease disaster prediction, environmental pollution control, and ecological balance [15]. Compared to traditional surface reflectivity, the nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectivity (NBAR) has advantages of being able to provide comparable dynamic data, thus avoiding the angular impact, and reflecting the dynamic variation patterns of the vegetated target area [16; 17]. [PERSON] et al. provided an effective method to simulate the temporal changes in the MODIS NBAR time series of typical farmland surfaces by means of the season-trend statistics [18], and they also used the improved NBAR data as matching values for the LAI neural network estimation, making the predicted LAI time series more continuous than the MODIS LAI [14; 19].  
In this study, HJ-1 CCD and MODIS data were selected as data sources to invert the HJ-1 NBAR with 30 m resolution based on the MODIS NBAR production algorithm. The inversion data have advantages of high spatial and temporal resolution, as well as meeting the requirements of high quality and quantity of small-scale regional data. This provides a feasibility method for the HJ-1 satellites to produce the secondary products for small-scale RS ground surface research, and provides a reference for dynamic information acquisition and application of small satellite data, contributing to the improvement in RS estimation of surface environment variables. Moreover, the NBAR time series is a special sequence of surface observations, which has a pronounced seasonal change. It contains an important information about the four seasons and solar radiation; its predictions can contribute to early warning of pests and phonological changes, and it can be useful in other studies.