Remote sensing from optical radiometers in space offers a nondestructive approach to estimating above ground biomass (AGB) with high spatial and temporal resolution, but the application is challenged by cloud cover and differences in soil background and crop phenology. We present a framework based on Sentinel-2 imagery for relating the adjusted summed NDVI measurements to the AGB. The resulting R² values for the measured and estimated AGB ranged from 0.79 to 0.98 for individual paddocks, and the R² from a pooled dataset (multiple crops, years, and locations) was 0.86. Application of the pooled dataset model to a separate validation dataset resulted in an R² of 0.88; however, there was a bias that resulted in the underestimation of the measured biomass. Analysis of the impacts of the gaps in the time series showed a decrease of 0.43% per gap day for the summed NDVI values. To address the impacts of clouds, we demonstrate the use of active optical and additional satellite imagery to fill the gaps due to clouds in the Sentinel-2 imagery. The framework presented results of the spatial daily estimates of the AGB and crop growth rates.

This study explored the relationships between moderate resolution imaging spectroradiometer (MODIS) NDVI observations with both measured and simulated fractional green cover (FGrC), leaf area index (LAI), and above ground biomass (AGB) for dryland wheat in Australia. A total of 37 paddocks in north-western Victoria, Australia, were sampled during 2003-2006 for AGB at anthesis, and for FGrC, NDVI (from an active optical sensor), and AGB during 2012. The 2012 FGrC and NDVI measurements were fitted to MODIS NDVI, resulting in positive, linear relationships when the MODIS NDVI values were ≤ 0.80. Measured AGB was also positively, linearly related to MODIS summed NDVI, resulting in an overall R ² of 0.81 and root mean square error (RMSE) of 1397 kg/ha. Crop simulations were run for the fourteen paddocks from 2003 to 2006, and six paddocks from 2012. Four crop phenological points were selected to extract corresponding NDVI and simulated crop parameters: emergence, peak LAI, the mid-point between emergence and peak LAI, and anthesis. Linear models were fit between the MODIS NDVI and simulated values of FGrC, LAI, and AGB. Overall, the highest R ² values corresponded to using all of the dates for FGrC (R ² = 0.82) and AGB (R ² = 0.92), and anthesis dates for LAI (R ² = 0.74). For FGrC and AGB, the RMSE with simulated parameters were comparable or better than the equivalent results from the in situ measurements (note that there were no LAI in situ measurements to compare with). The results support the notion for extending the value of the MODIS NDVI using crop simulation models. The combination of remotely sensed and simulation data might offer regional maps of spatial AGB and ultimately grain yield, which would have high value for research, resource management, policy, and potentially, crop management.

Frost damage to broadacre crops can cause up to an 85% loss in productivity. Although growers have few options for crop protection from frost, a rapid method for assessing frost-induced sterility would allow for timely management decisions (e.g., cutting for hay and altering marketing strategies). Spectral mixture analysis (SMA) has shown success in mapping landscape components and was used with hyperspectral data collected on the canopy, heads, and leaves of wheat at different sites to determine if this could quantify frost damage. Spectral libraries were assembled from canopy components collected from local field sites to generate spectral libraries for SMA from which a series of fraction sets was derived. The frost (Fr) fraction was then used to estimate final yield as a means of measuring frost damage. The best-fitting Fr fractions to yield were derived from the same data set as the source Fr spectra, and these ranged over R² = 0.58–0.75 at the canopy scale. It was clear that spectral signatures need to be collected at scale to assess frost damage. While Fr fractions were able to estimate yield there was no “universal” endmember set from which a Fr fraction could be derived. The normalized difference vegetation index (NDVI) was not able to estimate frost damage consistently. Future work requires determining whether there is a “universal” set of endmembers and a minimum set of targeted wavebands that could lead to multispectral instruments for frost assessment for use in ground and aerial sensors.

Accurately monitoring Canopy Nitrogen Concentration (CNC) is a prerequisite for precision nitrogen (N) fertiliser management at the farm scale with carbon and N budgeting across the landscape and ecosystems. While many spectral indices have been proposed for CNC monitoring, their applicability and accuracy are often adversely affected by confounding factors such as aboveground biomass (AGB), crop type, growth stages, and environmental conditions, limiting their broader application and adoption; with AGB being one of the most dominant signals and confounding factors at canopy scale. The confounding effect can become more challenging as AGB is also physiologically linked with CNC across the growth stages. Additionally, the interplay between index form, selection of optimal wavebands and their bandwidths remains poorly understood for CNC index design. This study proposes robust and cost-effective 2- and 4-waveband multispectral (MS) CNC indices applicable across a wide range of crop conditions. We collected 449 canopy reflectance spectra (400–980 nm) together with corresponding CNC and AGB measurements across four growth stages of ryegrass (winter and summer), and five growth stages of barley (winter-spring) in Victoria, Australia, in 2018 and 2019. All possible waveband (400–980 nm) combinations revealed that the best combination varied between seasons and crop types. However, the visible spectrum, particularly the blue region, presented high and consistent performance. Bandwidths of 10–40 nm outperformed either very narrow (2 nm) or very broad bandwidths (80 nm). The newly developed 2-waveband index (416 and 442 nm with 10-nm bandwidth; R² = 0.75 and NRMSE = 0.2) and 4-waveband index (512, 440, 414 and 588 nm with 40-nm bandwidth; R² = 0.81 and NRMSE = 0.17) exhibited the best performance, while validation with an independent dataset (from a different growing period to those used in the model development) obtained NRMSE values of 0.25 and 0.24, respectively. The 4-waveband index provides enhanced performance and permits use of broader bandwidths than its 2-waveband counterpart.