Crop Leaf Area Index Observations With a Wireless Sensor Network and Its Potential for Validating Remote Sensing Products

The collection of ground measurements for validating remotely sensed crop leaf area index (LAI) is labor and time intensive. This paper presents an automatic measuring system that was designed based on a wireless sensor network (WSN). The corn LAI was continuously observed from June 25 to August 24, 2012. Approximately, 42 in situ WSN measurement nodes were used in a area in the Heihe watershed of northwest China. The data were analyzed in three ways: 1) a comparison with LAI-2000, 2) a daily and 5-day aggregated time series analysis, and 3) a comparison with a Moderate Resolution Imaging Spectroradiometer (MODIS) LAI using both a ground LAINet LAI and a scaled-up LAI through inversion of Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data. The preliminary results indicated that the measured LAI values from the LAINet were correlated with the values derived from LAI-2000 (R2 from 0.27 to 0.96 with an average of 0.42). When compared with the daily crop LAI growth trajectory, the performance of the measurement system was improved by using the data that were aggregated over a 5-day window. When compared with MODIS LAI, we found that the spatial aggregation values of the ground LAINet observations and the scaled-up ASTER LAI were identical or similar to the MODIS LAI values over time. With its low-cost and low-energy consumption, the proposed WSN observation system is a promising method for collecting ground crop LAI in flexible time and space for validating the remote sensing land products.