With significant developments in remote-sensing technology, a variety of satellites and sensors with different spatial, spectral, and temporal resolutions have been quickly developed. The launch of the Earth Resources Technology Satellite “ERTS-1” (i.e., Landsat-1) in the early 1970s is considered a milestone in the history of the development of satellite remote sensing. SEASAT, launched in 1978, was the first civilian satellite designed for remote sensing of the Earth’s oceans with the first spaceborne synthetic aperture radar (SAR). In 1999, the first commercial satellite (IKONOS) collecting very high resolution imagery was launched. By far, more than 6000 satellites have been launched, over 60% of which serve military purposes. Those for civilian and scientific use mainly include the series of Landsat, meteorological, ocean, geodetic, astronomical observation, and communications satellites. These satellites provide sufficient spatial and temporal coverage of high quality data at various scales. As a result, the potential of using remote-sensing techniques as a monitoring tool for forest ecosystems has been adequately recognized. Multi-source optical and radar data, including the widely recognized Landsat records [1
], high spatial resolution Quickbird imagery [2
], and time-series SPOT (Systeme Probatoire d’Observation de la Terre)-VEGETATION images [3
] as well as L-band PALSAR data [4
], have been widely utilized to monitor the forest status and its dynamics under various circumstances.
All remote sensing-based modeling can be traditionally classified into two major categories: physical and statistical. Physical models always follow the physical mechanisms of the remote-sensing systems and can be continuously improved by adding necessary knowledge. However, it is usually challenging to make clear the physical mechanisms of the interaction between surface objects and remote sensing signals, with the appropriate models also potentially being quite complex. On the other hand, statistical models are based on the correlation relationships between remotely sensed variables and land surface measurements. These types of models are usually site-limited. However, there are clear advantages in terms of the convenience for development, effectiveness for calculations, and fewer demands for input data [5
In consideration of the limitations of specific physical models in data sources as well as input variable requirements, the statistical modeling approaches are suited for applications under different environmental conditions and imaging systems due to the above-mentioned advantages [6
]. Thus, the simple linear regression model was chosen in this study for accuracy comparisons among different optical sensors with varied resolutions, as well as between optical and SAR sensors.
The leaf forms the main surface for matter and energy exchange between the vegetation canopy and the atmosphere. Leaf area index (LAI), which is the ratio of foliage area to ground area, is proposed as a key variable in the study of forest ecosystems and their development [7
]. Chen and Black proposed using half of the total green leaf area in order to take into account the effective photosynthetic area in the case of non-flat leaves [8
]. LAI can be used to characterize the canopy–atmosphere interface of an ecosystem and is related to precipitation, radiation extinction, canopy microclimate, atmospheric nutrient deposition, and interception as well as water, carbon, and energy exchanges with the atmosphere [9
]. It plays a key role in the studies of various fields, including climate change, environment management [10
], and vegetation surveys [11
]. It can both be retrieved from remote-sensing data and measured using developed canopy analyzer devices [8
]. In this study, it was selected as the field measured variable to correlate with remote sensing-based spectral reflectance or its transformations.
With regard to the spectral characteristics of green vegetation in specific bands, they reflect their own biophysical features and environmental impacts. Thus, the surface reflectance of some specific spectral bands can be used to establish regression models with biophysical parameters [5
]. Nevertheless, considering the differences in the wavelength range and bandwidth among different optical sensors, direct comparisons using single-band reflectance are limited and spectral indices can be derived and applied to improve the accuracy. As demonstrated in many previous studies, vegetation index (VI), a combination of single band of remote sensing data, can be treated as a simple, effective, and experienced characterization of ground vegetation conditions [12
]. Previous studies have proven that the VI usually shows a good correlation with a variety of physiological and ecological parameters and hence can be widely used to diagnose a range of biophysical vegetation parameters, including canopy structural parameters [13
], LAI [14
], fractional vegetation cover [15
], and above-ground biomass (AGB) [16
]. Consequently, the VI was chosen as the remote-sensing extracted variable to form those linear regression models.
There is remote-sensing data from a wide variety of optical and radar sensors which had been adopted for forest detection, monitoring, and management. For example, Arroyo et al. integrated LiDAR (Light Detection and Ranging) data and high spatial resolution satellite imagery (Quickbird-2) to estimate riparian biophysical parameters and land cover types in Queensland, Australia [17
]. Andersen et al. utilized a combination of ground plots, LiDAR strip sampling, multispectral and radar imagery, as well as classified land cover information to estimate forest biomass resources in interior Alaska [18
]. Furthermore, MODIS (Moderate Resolution Imaging Spectroradiometer), ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar) and Landsat TM (Thematic Mapper) data had been selected to estimate forest LAI [19
], growing stock volume [20
], and aboveground carbon storage [21
], respectively. The optical images and SAR datasets were also used for the forest disturbance detection and recovery monitoring [22
]. As shown, these data have considerably different temporal coverage, spatial resolution, and even imaging mechanisms, hence demonstrating quite different performances among the various sensors. In the actual forest monitoring and management, we usually do not need the remote sensing data with the highest spatial resolution. In some cases, data with 30 m spatial resolution can meet the requirements, thus data with a spatial resolution of 10 m or even 1 m are unnecessary. Furthermore, remote sensing data with higher spatial resolution usually have higher acquisition costs. Thus, considering this, the actual needs and accuracy requirements should be fully evaluated to seek the most appropriate remote sensing data.
To make the statistical modeling accuracies evaluation and comparison among various sensors more direct and targeted, in this case, linear regression modeling was conducted using field measured LAI values and remote-sensing indices. These indices were calculated from four sources of data: ALOS AVNIR-2, Landsat-5 TM, MODIS NBAR (Nadir BRDF-Adjusted Reflectance), and ALOS PALSAR. These data were acquired at relatively close dates in 2010. During the modeling, six diverse optical vegetation indices were selected for the evaluation and comparison among the three optical sensors with different spatial resolutions. Following this, two radar indices defined in a similar form of corresponding optical indices were determined for the comparison between AVNIR-2 and PALSAR sensors. Both single-variable-based and multiple-variable-based modeling were performed, with the results being verified by cross-validation and then compared.