Installation of minimal IRIS SolarSoft branch (including \(IRIS^\) database.Ĥ.2 Look for the closest (mininum Euclidean distance) inverted RP to the observed profile.Ĥ.3 Look for the closest (mininum Euclidean distance) PCA coefficients of the inverted RP to the PCA coefficients of the observed profile.ĥ.1 Plot observed profile (dashed line) and inverted RP (thick)ĥ.2 Plot the corresponding Representative Model Atmosphere (RMA)Īnalazing, interpreting, and eventually enjoying the results. IRIS² Tutorial Videos: inspecting the inversion results with show_iris2model The \(\chi^2\)- map, map_index_db, and map_mu_db maps
Inspecting the IRIS² model atmosphere (and beyond) IRIS² Tutorial Videos: inverting IRIS Mg h&k lines with iris2 Inversion of a large raster file, a region of interest, or a masked area Inversion of IRIS Mg II h&k lines with iris2 The weights and the weighting windows: \(w_i\) Understanding the quality of the IRIS² results Quick start to invert IRIS Mg II h&k lines with IRIS² IRIS²: IRIS Inversion based on Representative profiles Inverted by STiC These findings have great potential for improving the understanding of the canopy structure and solar angle effects on LCC estimation and facilitating the spectral sampling protocols for crop chlorophyll status monitoring. For the commonly used NS row orientation, this study recommends off-noon spectral measurements to avoid the exposure of sunlit soil to the sensor. The semi-empirical LCC ~ LICI model (LCC = 52.85 × LICI - 16.71) calibrated with the combination of PROSPECT-D and 4SAIL-RowCrop showed the best accuracies among all VIs, with root mean square error (RMSE) values of 5.01 μg/cm 2 and 3.32 μg/cm 2 for ground-based and UAV-based canopy spectra measured at 15:00 h, respectively. Compared to traditional spectral measurements around 12:00 h, LCC estimation could be improved at observation time (15:00 h) that minimized observed sunlit soil fraction for the north-south (NS) row crop orientation. Our results showed linear relationships between LICI and LCC for both synthetic and experimental datasets and negligible sensitivity of those models to LAI. The performance of those models in LCC estimation was evaluated with experimental datasets measured from both ground and unmanned aerial vehicle (UAV) platforms. LICI and 11 traditional vegetation indices (VIs) were investigated to calibrate semi-empirical LCC ~ VI models with canopy spectral simulations. Moreover, this study proposed the leaf area index (LAI)-insensitive chlorophyll index (LICI) to mitigate the canopy structural effect on the LCC ~ LICI relationship.
To achieve these goals, we simulated canopy reflectance spectra covering four typical crop orientations over seasonal and diurnal cycles based on the leaf optical models (PROSPECT-5B and PROSPECT-D) coupled with canopy radiative transfer models (4SAIL and 4SAIL-RowCrop). This study aimed to determine the optimal observation time and to reduce the canopy structural effect for LCC estimation over row-structured crops. However, the effects of solar angle or spectral observation time and canopy structure on LCC estimation for row crops have been poorly understood. Since the canopy reflectance signature of crops is negligibly contaminated by shaded soil, it can be inferred that LCC should be better estimated when the soil background is under shaded conditions than under sunlit conditions. They have also found significant canopy structure and solar angle effects on canopy reflectance spectra, especially for row-structured open crop canopies with varying sunlit and shaded soil backgrounds over seasonal and diurnal cycles. Leaf chlorophyll content (LCC), as an important indicator of photosynthetic capacity and nitrogen status, has been non-destructively estimated from canopy reflectance spectra in recent studies.
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