VNAI-NDVI-space and polar coordinate method for assessing crop leaf chlorophyll content and fractional cover

Abstract

Crop leaf chlorophyll content (CC) and fractional cover (fc) are critical parameters for assessing crop growth and dynamic vegetation changes at regional and global scales. As CC and fc are the key factors dominating crop canopy reflectance, the confounding effects of CC and fc on canopy vegetation spectra and spectral indices (SIs) limit their remote sensing estimation. We combined the Normalized Difference Vegetation Index (NDVI) and Visible and near-infrared (NIR) Angle Index (VNAI) to understand the vegetation canopy SI differences caused by CC and fc (abbreviated as VNAI-NDVI-space). The VNAI-NDVI-space is approximately fan-shaped with high-CC and low-CC edges; the pixels inside the fan-shaped space represent crops with various CC and fc. We proposed a Polar Coordinate method (PCM) for assessing CC and fc. The performance of the VNAI-NDVI-space in assessing CC and fc was tested using a field-based spectrometer and an unmanned aerial vehicle (UAV)-based imaging spectrometer measurement for three soybean fields and six growth stages. Analysis of this imaging led to three conclusions: (i) The VNAI-NDVI-space can be used to analyze the confounding effects of CC and fc on crop canopy SIs; (ii) The VNAI-NDVI-space can track critical crop growth features; the VNAI-NDVI-space is not saturated at medium-to-high vegetation cover; (iii) the (a) confounding effect of CC on fc estimation; and (b) the confounding effect of fc on CC estimation was mitigated by the proposed VNAI-NDVI-space and PCM. The proposed PCM was successfully used to estimate CC and fc from the simulated dataset (CC: r = 0.97, RMSE = 4.10 μg/cm2; fc: r = 0.99, RMSE = 0.10), the field measurements (CC: r = 0.82, RMSE = 5.61 Dualex-units; fc: r = 0.78, RMSE = 0.08), and the UAV measurements (CC: field-PJ, r = 0.87, RMSE = 5.99 Dualex-units, field-PB, r = 0.66, RMSE = 7.29 Dualex-units; fc: r = 0.46, RMSE = 0.11). Thus, VNAI-NDVI-space and PCM are promising for assessing crop CC and fc.