Leaf area index (LAI) is a key variable in many land surface models that involve energy and mass exchange between vegetation and the environment. In recent years, extracting vegetation structure parameters from digital photography becomes a widely used indirect method to estimate LAI for its simplicity and ease of use. A Leaf Area Index Sensor (LAIS) system was developed to continuously monitor the growth of crops in several sampling points in Huailai, China. The system applies 3G/WIFI communication technology to remotely collect crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX) algorithm in LAIS. The objective of this study is to primarily verify the LAI estimated from LAIS (Lphoto) through comparing them with the destructive green LAI (Ldest). Ldest was measured across the growing season ntil maximum canopy development while plants are still green. The preliminary verification shows that Lphoto corresponds well with the Ldest (R2=0.975). In general, LAI could be accurately estimated with LAIS and its LAI shows high consistency compared with the destructive green LAI. The continuous LAI measurement obtained from LAIS could be used for the validation of remote sensing LAI products.
Soil moisture is an important parameter in the study of agriculture, ecology and carbon cycle. However, it has great difficulties to retrieve soil moisture content using remote sensing techniques. Even, field measurements can hardly reflect the spatial variation of soil moisture, due to the tremendous heterogeneity in its spatial distribution. Wireless Sensor Network (WSN), as a new technology for ground data collection, has been gradually applied to various fields. This novel technique has great advantages in monitoring soil moisture content, obtaining the soil moisture data in real time from multiple sites and different depths. Taking Huailai remote sensing comprehensive experimental station of Chinese Academy of Sciences for example, this paper introduces the calibration and data validation of soil moisture wireless sensor network. Oven drying method is used to calibrate the soil moisture sensor EC-5. The analysis indicates that the data measured by EC-5 had fairly well accuracy, so that the further calibration is not necessary. Data validation experiments had been taken from three aspects: data validity verification, temporal and spatial validation. It is clear to see that WSN data reveals the changes of soil moisture both in spatial domain and in different depths. Although the soil moisture data measured by WSN still do not have enough absolute accuracy, its continuous real-time data can clearly reflect the temporal and spatial relative variation, and the wide installation of sensors enables the data be obtained by the large amount, which was practically unavailable before.
The ocean surface albedo (OSA) is a deciding factor on ocean net surface shortwave radiation (ONSSR) estimation. Several OSA schemes have been proposed successively, but there is not a conclusion for the best OSA scheme of estimating the ONSSR. On the base of analyzing currently existing OSA parameterization, including Briegleb et al.(B), Taylor et al.(T), Hansen et al.(H), Jin et al.(J), Preisendorfer and Mobley(PM86), Feng’s scheme(F), this study discusses the difference of OSA’s impact on ONSSR estimation in condition of actual downward shortwave radiation(DSR). Then we discussed the necessity and applicability for the climate models to integrate the more complicated OSA scheme. It is concluded that the SZA and the wind speed are the two most significant effect factor to broadband OSA, thus the different OSA parameterizations varies violently in the regions of both high latitudes and strong winds. The OSA schemes can lead the ONSSR results difference of the order of 20 w m-2. The Taylor’s scheme shows the best estimate, and Feng’s result just following Taylor’s. However, the accuracy of the estimated instantaneous OSA changes at different local time. Jin’s scheme has the best performance generally at noon and in the afternoon, and PM86’s is the best of all in the morning, which indicate that the more complicated OSA schemes reflect the temporal variation of OWA better than the simple ones.
The Wireless Sensor Networks of Coarse-resolution Pixel Parameters (CPP-WSN) was established to monitor the heterogeneity of coarse spatial resolution pixel, with consideration of different categories of land surface parameters in Huailai, Hebei province, China (40.349°N, 115.785°E). The observation network of radiation parameters (RadNet) in CPP-WSN was developed for multi-band radiation measurement and consisted of 6 nodes covering 2km*2km area to capture its heterogeneity. Each node employed four sensors to observe the five radiation parameters. The number and location of nodes in RadNet were determined through the representativeness-based sampling method. Thus, the RadNet is a distributed observation system with nodes work synchronously and measurements used together.
The intercomparison experiment for RadNet is necessary and was conducted in Huailai Remote Sensing Station from 5th Aug to 10th Aug in 2012. Time series observations from various sensors were collected and analyzed. The maximum relative differences among sensors of UVR, SWR, LWR, PAR, and LST are 4.83%, 5.3%, 3.71%, 11%, and 0.54%, respectively. Sensor/parameter differences indeed exist and are considerable large for PAR, SWR, UVR, and LWR, which cannot be ignored. The linear normalization and quadratic polynomial normalization perform similar for CUV5/UVR, PQS1/PAR, CNR4/SWR, and SI-111/LST. As for CNR4/LWR, quadratic polynomial normalization show higher accuracy than linear normalization, especially in node2, node4, and node5. Thus, the LWR measured by CNR4 is proved to be nonlinear, and should be normalized with quadratic polynomial coefficients for higher precision.