Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network

Abstract

Accurate and continuous monitoring of leaf area index (LAI), a widely-used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote-sensing-derived LAI. This paper evaluates the performance of LAI retrieval from multi-source, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloud-free images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel-2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a look-up-table (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remote-sensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel-2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R2 = 0.74, and root mean square error RMSE = 0.53 m2 m−2). At the reproductive stage, a significant underestimation was found (R2 = 0.41, and 0.89 m2 m−2 in terms of RMSE). This study suggests that time-series LAI can be retrieved from multi-source satellite data through model inversion, and the LAINet instrument could be used as a low-cost tool to provide continuous field LAI measurements to support LAI retrieval.

Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops

“…The small fluctuations in the field-measured time series close to the green peak (Figure 3) were also reported in the earlier paper by [68]. There are two potential factors explaining the fluctuations: (1) The changing weather conditions-LAINet uses multiple observations of direct solar light to construct hemispheric gap fraction, and then calculate LAI based on Beer Lambert law [35].…”

Comparison of Machine Learning Methods for Estimating Leaf Area Index and Aboveground Biomass of Cinnamomum camphora Based on UAV Multispectral Remote Sensing Data

“…However, satellite remote sensing images are susceptible to atmospheric influence and limited by spatial and temporal resolution and real time, resulting in the inability to obtain high-quality satellite remote sensing images suitable for precision agriculture [13,14]. For example, Landsat series satellites have a minimum repetition period of 16 days and mainly use optical sensors to acquire remote sensing images; when the signal propagation route is affected by clouds or rainfall, the images will not be applied to the accurate monitoring of plant physiological parameters [15]. Zhao et al estimated the aboveground biomass of alpine grassland quickly and efficiently using the RF algorithm based on MODIS and SRTM data [16].…”

An Investigation of Winter Wheat Leaf Area Index Fitting Model Using Spectral and Canopy Height Model Data from Unmanned Aerial Vehicle Imagery

“…Although data collected from ground-based sensors have high temporal and spatial resolutions, its disadvantages are high labor costs and limited coverage ranges [8]. In contrast, data from spaceborne platforms can cover wide spatial ranges and be obtained from multiple sources [9]. However, it is difficult for spaceborne platforms to acquire multi-temporal data of crops in a timely manner, due to the limitations of the fixed revisit cycle of the spaceborne platforms and weather influence [10].…”