Search Results (2)

The Hapke two-layer medium model is an efficient way of simulating the spectra of dusty leaves. However, the simulation accuracy is low when the amount of dustfall is small. To solve this problem, we introduced the dust coverage factor and the linear spectral mixing model, to improve the accuracy of the Hapke two-layer medium model. Firstly, based on the assumption of spherical dust particles, the arrangement and accumulation mode of the particles were set, and the coverage factor and accumulation thickness of particles in the leaf area were calculated. Then, the coverage factor was used as an abundance. Endmembers were the spectra of dust-free leaves (measured) and dust-covered leaves (simulated by model), and the final simulated spectra were calculated using linear spectral mixing theory. This study presents the following findings: (1) When the coverage factor was calculated using the exponential model, the maximum difference between the corrected simulated spectra and the measured spectra was 3.4%, and the maximum difference between the original simulated spectra and the measured spectra was 15.2%. The accuracy of the corrected spectra is much higher than that of the original simulated spectra. (2) In this study, the physical thickness and optical thickness calculated by the Hapke two-layer medium model are equivalent, which is quite different from the actual dust accumulation. When the linear spectral mixing model is introduced, to modify the simulation value when the number of dust particles accumulated is less than one layer, the spectral endmember value of the simulated dust leaf is replaced by the simulation spectrum when the number of dust particles accumulated is exactly one layer. The calculated cor-rection spectrum has high rationality and credibility. This finding may be beneficial for monitoring amounts of dustfall accurately using remote sensing in mining areas. Full article

Chlorophyll is the dominant pigment in the photosynthetic light-harvesting complexes that is related to the physiological function of leaves and is responsible for light absorption and energy transfer. Dust pollution has become an environmental problem in many areas in China, indicating that accurately estimating chlorophyll content of vegetation using remote sensing for assessing the vegetation growth status in dusty areas is vital. However, dust deposited on the leaf may affect the chlorophyll content retrieval accuracy. Thus, quantitatively studying the dustfall effect is essential. Using selected vegetation indices (VIs), the medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI), and the double difference index (DD), we studied the retrieval accuracy of chlorophyll content at the leaf scale under dusty environments based on a laboratory experiment and spectra simulation. First, the retrieval accuracy under different dustfall amounts was studied based on a laboratory experiment. Then, the relationship between dustfall amount and fractional dustfall cover (FDC) was experimentally analyzed for spectra simulation of dusty leaves. Based on spectral data simulated using a PROSPECT-based mixture model, the sensitivity of VIs to dust under different chlorophyll contents was analyzed comprehensively, and the MTCI was modified to reduce its sensitivity to dust. The results showed that (1) according to experimental investigation, the DD model provides low retrieval accuracy, the MTCI model is highly accurate when the dustfall amount is less than 80 g/m², and the retrieval accuracy decreases significantly when the dustfall amount is more than 80 g/m²; (2) a logarithmic relationship exists between FDC and dustfall amount, and the PROSPECT-based mixture model can simulate the leaf spectra under different dustfall amounts and different chlorophyll contents with a root mean square error of 0.015; and (3) according to numerical investigation, MTCI’s sensitivity to dust in the chlorophyll content range of 25 to 60 μg/cm² is lower than in other chlorophyll content ranges; DD’s sensitivity to dust was generally high throughout the whole chlorophyll content range. These findings may contribute to quantitatively understanding the dustfall effect on the retrieval of chlorophyll content and would help to accurately retrieve chlorophyll content in dusty areas using remote sensing. Full article