Quantifying the impact of dust retention on maize canopy spectral reflectance and vegetation indices in dust belt regions: A case study in southern Xinjiang, China

doi:10.1016/j.jaridl.2026.01.008

Journal of Arid Land

2026, Vol. 18

Issue (1): 101-130 DOI: 10.1016/j.jaridl.2026.01.008 CSTR: 32276.14.JAL.20250402

Research article

Quantifying the impact of dust retention on maize canopy spectral reflectance and vegetation indices in dust belt regions: A case study in southern Xinjiang, China

MA Baodong^*(

), GAO Shuxian, KANG Ting, CHE Defu, SHU Yang

School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China

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Abstract

Sand dust belts span approximately one-fifth of the global land surface. In these regions, dust tends to settle on vegetation surfaces, altering the observed reflectance and affecting remote sensing detections. To enhance the accuracy of maize growth monitoring in dust-affected regions, this study aims to quantify the effect of sand dust retention on maize during the tasseling stage in the Kashgar Prefecture, Xinjiang Uygur Autonomous Region, China, by analyzing changes in canopy reflectance and vegetation indices. First, field sampling was conducted to measure the key canopy structure parameters and dust retention levels of maize, and laboratory spectral measurements were performed on leaf spectral properties under gradient dust retention. The measured data were then used to drive the LargE-Scale remote sensing data and image Simulation framework (LESS) model for simulating realistic maize canopy spectra across different dust levels, with validation against Sentinel-2 imagery. Second, on the basis of the simulated and satellite-derived spectra, the dust resistance of 36 common vegetation indices was systematically evaluated, and new robust dust-resistant indices were developed. The results showed that compared with dust-free maize, the canopy reflectance of dust-retained maize followed an increase-decrease-increase pattern, with critical turning points at 735 and 1325 nm. The maximum reflectance difference of -0.11755 (change rate: 29.002%) occurred within the 735-1325 nm range at 24 g/m² dust retention, and the minimum reflectance difference of 0.04285 (change rate: 148.950%) was observed in the 350-735 nm range under the same dust retention level. Among the 36 vegetation indices, only the global environment monitoring index (GEMI) and the ratio of transformed chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index (TCARI/OSAVI) exhibited dust resistance, with GEMI being effective below 6 g/m² and TCARI/OSAVI remaining stable across all levels (average ratio: 0.970). The newly developed indices in this study, (RE3-RE2)/(NIR-RE2), (RE3-RE2)/(RE4-RE2), and (NIR-RE2)/(RE4-RE2), retained values within the predefined dust-resistant range over the full dust retention levels of 0-24 g/m², thus showing a more stable dust resistance compared with the commonly used 36 vegetation indices. Specially, (RE3-RE2)/(RE4-RE2) performed the most robustly in Sentinel-2 imagery, that is, 58.020% of pixels were within the dust-resistant range, and an average ratio of 0.937 was obtained for the original-spectra index. This study provides a scientific basis for crop monitoring and management in dust-affected regions.

Key words： sand dust retention canopy spectral reflectance LargE-Scale remote sensing data and image Simulation framework (LESS) model dust-resistant vegetation indices tasseling-stage maize Sentinel-2 imagery

Received: 26 August 2025 Published: 31 January 2026

Corresponding Authors: ^*MA Baodong (E-mail: mabaodong@mail.neu.edu.cn)

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	MA Baodong
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Cite this article:

MA Baodong, GAO Shuxian, KANG Ting, CHE Defu, SHU Yang. Quantifying the impact of dust retention on maize canopy spectral reflectance and vegetation indices in dust belt regions: A case study in southern Xinjiang, China. Journal of Arid Land, 2026, 18(1): 101-130.

URL:

http://jal.xjegi.com/10.1016/j.jaridl.2026.01.008 OR http://jal.xjegi.com/Y2026/V18/I1/101

Fig. 1 Geographical location of the study area. (a), dust belt over the northern hemisphere (from World Imagery Wayback on 31 July 2025; https://livingatlas.arcgis.com/wayback); (b), Kashgar Prefecture (from World Imagery Wayback on 31 July 2025); (c), photo of dust-retained maize plant in the study area; (d)-(e), photos of dust-retained maize leaves in the study area.

Fig. 2 Research flow of this research. LESS, LargE-Scale remote sensing data and image Simulation framework.

Fig. 3 Experimental maize leaves under varying dust retention levels. (a), 0 g/m²; (b), 8 g/m²; (c), 16 g/m²; (d), 24 g/m².

Fig. 4 Spectral measurements of dust-retained leaves under various dust retention levels. (a), adaxial reflectance; (b), abaxial reflectance; (c), transmittance. The SVC HR-1024 spectrometer used in this study is fabricated by the Spectra Vista Corporation (Poughkeepsie, USA).

Table 1 Input parameters used in the LargE-Scale remote sensing data and image Simulation framework (LESS) simulations

No.	Vegetation index	Formula		Definition
1	Ratio vegetation index (RVI)	$ \mathrm{RVI}=\frac{\mathrm{NIR}}{\mathrm{Red}}$		NIR is the reflectance of wavelength at 833 nm (i.e., Band 8 in Sentinel-2); Red is the reflectance of wavelength at 665 nm (i.e., Band 4 in Sentinel-2); Green is the reflectance of wavelength at 560 nm (i.e., Band 3 in Sentinel-2); η is a nonlinear combination parameter of near-infrared (NIR) and Red band reflectance; Blue is the reflectance of wavelength at 490 nm (i.e., Band 2 in Sentinel-2); L is a canopy background adjustment parameter; C₁ is a coefficient for correcting aerosol scattering effects in the red band; C₂ is a coefficient for correcting aerosol scattering effects in the blue band; G is a gain factor; c is a coefficient related to aerosol correction; RE1 is the reflectance of wavelength at 705 nm (i.e., Band 5 in Sentinel-2); RE2 is the reflectance of wavelength at 740 nm (i.e., Band 6 in Sentinel-2); RE3 is the reflectance of wavelength at 783 nm (i.e., Band 7 in Sentinel-2); RE is an abbreviation of Red Edge, which refers to the sharp transition zone in vegetation reflectance spectra; B₇₀₅ is the reflectance of wavelength at 705 nm (corresponding Band 5 in Sentinel-2); B₇₅₀ is the reflectance of wavelength at 750 nm (corresponding Band 6 in Sentinel-2); RE4 is the reflectance of wavelength at 865 nm (Band 8a in Sentinel-2); and B₄₄₅ is the reflectance of wavelength at 445 nm (i.e., Band 1, the aerosol band of Sentinel-2).
2	Normalized difference vegetation index (NDVI)	$ \mathrm{NDVI}=\frac{\mathrm{NIR}-\mathrm{Red}}{\mathrm{NIR}+\mathrm{Red}}$
3	Difference vegetation index (DVI)	$\mathrm{DVI}=\mathrm{NIR}-\mathrm{Red}$
4	Green normalized difference vegetation index (GNDVI)	$\mathrm{GNDVI}=\frac{\mathrm{NIR}-\text { Green }}{\mathrm{NIR}+\text { Green }}$
5	Red-green normalized difference index (RGNDI)	$ \text { RGNDI }=\frac{\text { Red }- \text { Green }}{\text { Red }+ \text { Green }}$
6	Renormalized difference vegetation index (RDVI)	$ \mathrm{RDVI}=\frac{\mathrm{NIR}-\mathrm{Red}}{\sqrt{\mathrm{NIR}+\mathrm{Red}}}$
7	Transformed normalized difference vegetation index (TNDVI)	$ \mathrm{TNDVI}=\sqrt{\frac{\mathrm{NIR}-\mathrm{Red}}{\mathrm{NIR}+\mathrm{Red}}+0.5}$
8	Normalized difference green index (NDGI)	$ \mathrm{NDGI}=\frac{\text { Green }- \text { Red }}{\text { Green }+ \text { Red }}$
9	Modified soil-adjusted vegetation index (MSAVI)	$ \mathrm{MSAVI}=\frac{2 \times \mathrm{NIR}+1-\sqrt{(2 \times \mathrm{NIR}+1)^{2}-8 \times(\mathrm{NIR}-\mathrm{Red})}}{2}$
10	Global environment monitoring index (GEMI)	$ \begin{array}{l} \text { GEMI }=\frac{\eta \times(1-0.25 \times \eta)-(\text { Red }-0.125)}{1-\text { Red }} \\ \eta=\frac{2 \times\left(\mathrm{NIR}^{2}-R^{2}\right)+1.5 \times \mathrm{NIR}+0.5 \times \text { Red }}{\mathrm{NIR}+\text { Red }+0.5} \end{array}$
11	Green vegetation index (GVI)	$ \mathrm{GVI}=\frac{\mathrm{NIR}}{\text { Green }}$
12	Red-green ratio index (RGRI)	$ \mathrm{RGRI}=\frac{\text { Red }}{\text { Green }}$
13	Green-red normalized difference vegetation index (GRNDVI)	$ \text { GRNDVI }=\frac{\text { NIR }-(\text { Red }+ \text { Green })}{\text { NIR }+ \text { Red }+ \text { Green }}$
14	Enhanced vegetation index (EVI)^{错误!未找到引用源。}	$ \begin{array}{l} \mathrm{EVI}=\frac{\mathrm{NIR}-\mathrm{Red}}{\mathrm{NIR}+C_{1} \times \mathrm{Red}-C_{2} \times \mathrm{Blue}+L} \\ \left(L=1.5, \quad C_{1}=6, \quad \text { and } C_{2}=7.5\right) \end{array}$
15	Enhanced vegetation index 2 (EVI2)	$ \begin{array}{l} \mathrm{EVI} 2=G \frac{\mathrm{NIR}-\mathrm{Red}}{\mathrm{NIR}+(6-7.5 / c) \times \mathrm{Red}+1} \\ (G=2.5, \quad c=2.08) \end{array}$
16	Normalized difference red edge index 1 (NDRE1)	$ \mathrm{NDRE} 1=\frac{\mathrm{RE} 1-\mathrm{RE} 2}{\mathrm{RE} 1+\mathrm{RE} 2}$
17	Normalized difference red edge index 2 (NDRE2)	$ \mathrm{NDRE} 2=\frac{\mathrm{NIR}-\mathrm{RE}}{\mathrm{NIR}+R E}$
18	Modified simple ratio index (MSR)	$ \mathrm{MSR}=\frac{\mathrm{NIR} / \mathrm{Red}-1}{\sqrt{\mathrm{NIR} / \mathrm{Red}+1}}$
19	Meris terrestrial chlorophyll index (MTCI)	$ \mathrm{MTCI}=\frac{\mathrm{NIR}-\mathrm{RE}}{\mathrm{RE}+\mathrm{Red}}$
20	Modified chlorophyll absorption in reflectance index (MCARI)	$ \mathrm{MCARI}=[(\mathrm{RE}-\mathrm{Red})-0.2(\mathrm{RE}-\text { Green })] \times \frac{\mathrm{RE}}{\text { Red }}$
21	Ratio of transformed chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index (TCARI/OSAVI)	$ \text { TCARI/OSAVI }=\frac{3 \times[(\text { RE }- \text { Red })-0.2(\text { RE }- \text { Green })(\text { RE } / \text { Red })]}{(1+0.16)(\text { NIR }- \text { Red }) /(\text { NIR }+ \text { Red }+0.16)}$
22	Ratio of transformed chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index at 705 and 750 nm (TCARI/OSAVI_[705,750])	$ \mathrm{TCARI} / \mathrm{OSAVI}_{[705,750]}=\frac{3 \times\left[\left(B_{750}-B_{705}\right)-0.2\left(B_{750}-B_{550}\right)\left(B_{750} / B_{705}\right)\right]}{(1+0.16)\left(B_{750}-B_{705}\right) /\left(B_{750}+B_{705}+0.16\right)}$
23	Ratio of modified chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index (MCARI/OSAVI)	$ \mathrm{MCARI} / \mathrm{OSAVI}=\frac{(\mathrm{RE}-\mathrm{Red})-0.2(\mathrm{RE}-\mathrm{Green})(\mathrm{RE} / \mathrm{Red})}{(1+0.16)(\mathrm{NIR}-\mathrm{Red}) /(\mathrm{NIR}+\mathrm{Red}+0.16)}$
24	Ratio of modified chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index at 705 and 750 nm (MCARI/OSAVI_[705,750])		$ \mathrm{MCARI} / \mathrm{OSAVI}_{[705,750]}=\frac{\left(B_{750}-B_{705}\right)-0.2\left(B_{750}-B_{550}\right)\left(B_{750} / B_{705}\right)}{(1+0.16)\left(B_{750}-B_{705}\right) /\left(B_{750}+B_{705}+0.16\right)}$
25	Chlorophyll index green (CI_green)	$ C I_{\text {green }}=\frac{\mathrm{NIR}}{\text { Green }}-1$
26	Chlorophyll index red edge (CI_rededge)	$ \mathrm{CI}_{\text {redegde }}=\frac{\mathrm{NIR}}{\mathrm{RE}}-1$
27	Normalized difference vegetation index red edge 1 (NDVI_re₁)	$ \mathrm{NDVI}_{r e 1}=\frac{\mathrm{NIR}-\mathrm{RE} 1}{\mathrm{NIR}+\mathrm{RE} 1}$
28	Normalized difference vegetation index narrow red edge 1 (NDVI_re₁_n)	$ \mathrm{NDVI}_{r e 1 n}=\frac{\mathrm{RE} 4-\mathrm{RE} 1}{\mathrm{RE} 4+\mathrm{RE} 1}$
29	Normalized difference vegetation index red edge 2 (NDVI_re₂)	$ \mathrm{NDVI}_{r e 2}=\frac{\mathrm{NIR}-\mathrm{RE} 2}{\mathrm{NIR}+\mathrm{RE} 2}$
30	Normalized difference vegetation index narrow red edge 2 (NDVI_re₂_n)	$ \mathrm{NDVI}_{r e 2 n}=\frac{\mathrm{RE} 4-\mathrm{RE} 2}{\mathrm{RE} 4+\mathrm{RE} 2}$
31	Normalized difference vegetation index red edge 3 (NDVI_re₃)	$ \mathrm{NDVI}_{r e 3 n}=\frac{\mathrm{RE} 4-\mathrm{RE} 3}{\mathrm{RE} 4+\mathrm{RE} 3}$
32	Normalized difference vegetation index narrow red edge 3 (NDVI_re₃_n)	$ \mathrm{NDVI}_{r e 3 n}=\frac{\mathrm{RE} 4-\mathrm{RE} 3}{\mathrm{RE} 4+\mathrm{RE} 3}$
33	Simple ratio index at 705 nm (SR₇₀₅)	$ \mathrm{SR}_{705}=\frac{B_{750}}{B_{705}}$
34	Normalized difference index at 705 nm (ND₇₀₅)	$ \mathrm{ND}_{705}=\frac{B_{750}-B_{705}}{B_{750}+B_{705}}$
35	Modified simple ratio index at 705 nm (mSR₇₀₅)	$ \mathrm{mSR}_{705}=\frac{B_{750}-B_{445}}{B_{705}-B_{445}}$
36	Modified normalized difference index at 705 nm (mND₇₀₅)	$ \mathrm{mND}_{705}=\frac{B_{750}-B_{705}}{B_{750}+B_{705}-2 \times B_{445}}$

Table 2 Vegetation index formula

Fig. 5 Spectra of dust and the adaxial reflectance, abaxial reflectance, and transmittance of dust-free maize leaf

Fig. 6 Adaxial (a) and abaxial (b) reflectance, and transmittance (c) of maize leaves under varying dust retention levels

Fig. 7 LESS-simulated maize canopy reflectance and comparisons between canopy and leaf scales. (a), LESS-simulated maize canopy reflectance; (b), the leaf-scale reflectance differences and change rates, as well as the canopy-scale counterparts simulated by LESS; (c), error bars of reflectance differences between dust-free and dust-retained conditions.

Fig. 8 Comparison of spectra from Sentinel-2 images and LESS simulation results. The error bars denote standard deviation of reflectance, reflecting the variability among the simulations and Sentinel-2 pixel samples.

Table 3 Squared reflectance difference of Sentinel-2 and LESS-simulated spectra across bands under varying dust retention levels

Fig. 9 Normalized difference vegetation index (NDVI) (a and b) and ratio of transformed chlorophyll absorption in reflectance index to optimized soil-adjusted vegetation index (TCARI/OSAVI) (c and d) images of the study area under dust-free and dust-retained conditions

Table 4 Ratio of vegetation indices in the LESS simulation results under varying dust retention levels

Table 5 Ratio of GEMI and TCARI/OSAVI using Sentinel-2 images

Fig. 10 Change trend (a) and combination (b) of spectral bands under varying dust retention levels. The reflectance curves of near-infrared (NIR) and RedEdge-4 (RE4) are nearly identical and thus difficult to distinguish visually. Aerosol, reflectance of wavelength at 443 nm; Blue, reflectance of wavelength at 490 nm; Green, the reflectance of wavelength at 560 nm; Red, the reflectance of wavelength at 665 nm; RedEdge-1 (RE1), reflectance of wavelength at 705 nm; RedEdge-2 (RE2), reflectance of wavelength at 740 nm; RedEdge-3 (RE3), reflectance of wavelength at 783 nm; NIR, reflectance of wavelength at 842 nm; RE4, reflectance of wavelength at 865 nm; SWIR 1, shortwave infrared 1; SWIR 2, shortwave infrared 2.

Fig. 11 Ratio of new vegetation indices under varying dust retention levels. (RE3-RE2)/(NIR-RE2), (RE3-RE2)/(RE4-RE2), and (NIR-RE2)/(RE4-RE2) represent the original spectral reflectance calculation values; FD(RE3-RE2)/(NIR-RE2), FD(RE3-RE2)/(RE4-RE2), and FD(NIR-RE2)/(RE4-RE2) denote the differential spectral reflectance calculation values.

Fig. 12 Vegetation index ratio of three new constructed vegetation indices.

Fig. 13 Sensitivity analysis of dust retention to maize canopy reflectance

Fig. 14 Pattern of variations of Red, NIR, global environment monitoring index (GEMI), η, and GEMI ratio under varying dust retention levels. η is a nonlinear combination parameter of NIR and Red band reflectance; and GEMI ratio is the percentage ratio of the GEMI index value under a 24 g/m²condition to that under a dust-free condition.

Fig. 15 Correlation between NDVI and new vegetation indices from original (a, c, and e) and differential (b, d, and f) spectra. RSS, residual sum of squares.

Table 6 Multi-year NDVI difference analysis between late July and early August

Table 7 Comparison between narrow- and broad-band vegetation indices obtained from LESS simulations


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