Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture

doi:10.1016/0034-4257(89)90076-X

Remote Sensing of Environment

Volume 29, Issue 1, July 1989, Pages 25-37

https://doi.org/10.1016/0034-4257(89)90076-X Get rights and content

Abstract

The simplified reflectance model described earlier (Clevers, 1988b) for estimating leaf area index (LAI) is further simplified. In this model the nearinfrared reflectance was corrected for soil background (in particular differences in soil moisture content) and subsequently used for estimating LAI by applying the inverse of a special case of the Mitscherlich function. In the specific situation that the ratio of the reflectances of bare soil in the red and near-infrared is constant for a given soil background, the corrected near-infrared reflectance is now ascertained as a weighted difference of total measured near-infrared and red reflectances (socalled WDVI = weighted difference vegetation index). The validity of this approach is confirmed by simulations with the SAIL model. The above concept was tested at the experimental farm of the Agricultural University Wageningen, by using reflectance factors measured in field trials by means of multispectral aerial photography. The soil type at the experimental farm yielded constant ratios between green, red, and near-infrared reflectances independent of soil moisture content (that is, as a function of time). The difference between measured near-infrared and red reflectances provided a satisfactory approximation of the corrected near-infrared reflectance. The estimation of LAI by this corrected near-infrared reflectance for real field data yielded good results in this study, resulting in the ascertainment of treatment effects with larger precision than by means of the LAI measured in the field by conventional field sampling methods.

References (20)

J.G.P.W. Clevers
The derivation of a simplified reflectance model for the estimation of leaf area index
Remote Sens. Environ.
(1988)
A.R. Huete et al.
Soil and atmosphere influences on the spectra of partial canopies
Remote Sens. Environ.
(1988)
A.R. Huete et al.
Soil spectral effects on 4-space vegetation discrimination
Remote Sens. Environ.
(1984)
A.R. Huete et al.
Spectral response of a plant canopy with different soil backgrounds
Remote Sens. Environ.
(1985)
E.B. Knipling
Physical and physiological basis for the reflectance of visible and n near-infrared radiation from vegetation
Remote Sens. Environ.
(1970)
W. Verhoef
Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model
Remote Sens. Environ.
(1984)
J.G.P.W. Clevers
Application of remote sensing to agricultural field trials, thesis
J.G.P.W. Clevers
Multispectral aerial photography as a new method in agricultural field trial analysis
Int. J. Remote Sens.
(1988)
H.R. Condit
The spectral reflectance of American soils
Photogramm. Eng.
(1970)
H.W. Gausman
Leaf reflectance of near-infrared
Photogramm. Eng.
(1974)

There are more references available in the full text version of this article.

Cited by (306)

Comparative analysis of multi-source data for machine learning-based LAI estimation in Argania spinosa
2024, Advances in Space Research
In this study, we conducted a comprehensive assessment of Leaf Area Index (LAI) estimation using three distinct sources of satellite data: Sentinel-2 imagery, drone imagery (UAVs), and Mohammed VI satellite data. The main objective was to identify the most reliable and precise dataset for predicting LAI, with a focus on evaluating the performance of Random Forest models. For Sentinel-2 imagery, our Random Forest model achieves a robust R-squared (R²) value of 0.89, signifying a strong alignment between predicted and measured LAI values. The associated root-mean-square error (RMSE) is 0.4, indicating high predictive accuracy. In the context of UAVs, our Random Forest model excels, exhibiting an impressive R² value of 0.93, highlighting a substantial correlation between predicted and measured LAI. The RMSE for drone imagery stands at 0.37, showcasing exceptional predictive accuracy. Finally, the Random Forest model trained on Mohammed VI satellite data yields an R² value of 0.92, underlining its strong fit with measured LAI values. The RMSE for Mohammed VI imagery is 0.39, further underscoring the model's exceptional predictive accuracy. This comparative analysis underscores the importance of selecting the most suitable satellite data source for LAI estimation in Argania spinosa. UAV imagery emerges as the most accurate choice, closely followed by Mohammed VI Satellite and Sentinel-2 imagery. These findings offer valuable insights for effective monitoring of Argania spinosa and advancing sustainable land management practices in rural ecosystems.
Mapping aboveground biomass of Moso bamboo (Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress using Sentinel-2 imagery
2024, Ecological Indicators
Moso bamboo (Phyllostachys pubescens) stands as a pivotal economic bamboo species globally, holding substantial potential for carbon sequestration. Accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is crucial due to its close ties with the ecosystem's carbon cycle. Despite the maturation of monitoring techniques for Pantana phyllostachysae Chao, a significant pest of Moso bamboo, its interplay with AGB in these forests remains enigmatic. This study addressed this gap by categorizing P. phyllostachysae's impact on Moso bamboo forests into four levels: healthy, mild damage, moderate damage, and severe damage. By scrutinizing field data, we delved into the shifts in Moso bamboo leaf biomass under P. phyllostachysae stress. Leveraging Sentinel-2A/B imagery, we extracted diverse correlation factors, including original wave bands, vegetation indices, texture attributes, and vegetation's physical and chemical parameters. Subsequently, machine learning algorithms-namely, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) were employed to achieve remote sensing inversion of AGB in Moso bamboo forests, accounting for the presence of insect pests. We analyzed the response of Moso bamboo biomass sensitive factors and to further clarify the changes of AGB of Moso bamboo forests under insect pest stress at the remote sensing level ultimately. The results showed that (1) the degree of Moso bamboo leaf biomass damage was positively related to the damage level, which gradually increased from 15.15 % to 59.42 %; (2) the RF algorithm excelled in estimating Moso bamboo forest AGB, particularly in May, and inclusion of insect pest considerations enhanced AGB estimation accuracy; (3) among the four factor types, Band information and vegetation indices emerged as most impactful, and Band5, Band11, Band12, NDVI68a and MSAVI were selected the most often; (4) at the remote sensing level, AGB in Moso bamboo forests significantly varies under P. phyllostachysae stress. Healthy areas demonstrate an AGB of 66.9037 Mg ha⁻¹, while heavily affected regions drop to 52.6591 Mg ha⁻¹. It can be seen that combining pest factors for Moso bamboo biomass estimation solves the problem of rough biomass estimation, and this study provides a more promising method for forest growth monitoring.
Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations
2023, Remote Sensing of Environment
Vegetation indices (VIs) have been used extensively for qualitative and quantitative remote sensing monitoring of vegetation vigor and growth dynamics. However, the saturation phenomenon of VIs (i.e., insignificant change at moderate to high vegetation densities) poses a known limitation to their ability to characterize surface vegetation over the dense canopy. Although the mechanisms underlying saturation are relatively straightforward and several VIs have been proposed to mitigate the saturation effect, the assessment of the saturation effect of VIs remains insufficient. Notably, no unified metric has been proposed to quantify the VI saturation phenomenon, limiting VI selection in practical applications. In this study, we proposed two indicators to describe the saturation phenomenon and utilized a well-validated three-dimensional (3D) canopy radiative transfer (RT) model large-scale remote sensing data and image simulation framework (LESS) to simulate the bidirectional reflectance factor (BRF) of six forests scenes and assessed the variations in VIs in relation to leaf area index (LAI) values over different backgrounds, sun-sensor geometries, and spatial distribution types. The saturation characteristics of 36 VIs were evaluated in combination with simulation results and satellite observations from multiple sensors. The ranking of VI saturation from simulated and satellite results revealed a good agreement. Our results indicated that the simple ratio vegetation index (SR) performed best with the highest saturation point and can well characterize the surface vegetation condition until LAI reaches 4. Besides, we found that the saturation effect of VIs was influenced by soil brightness, sun-sensor geometry, and canopy structure. SR, modified simple ratio (MSR) and normalized green red difference index (NGRDI) were the most susceptible to these disturbing factors, although they had higher resistance to saturation. Modified triangular vegetation index 1 (MTVI1), modified non-linear vegetation index (MNLI), triangular greenness index (TGI), and triangular vegetation index (TriVI) performed well overall, combining the ability to resist saturation and disturbance factors. Appropriate application of VIs can help better understand vegetation responses to climate change and accurately assess ecosystem status. Our results contribute to the understanding of the VI saturation effect and provide a combined model and satellite data experimental workflow in appropriate VI selection to accurately characterize vegetation.
RSARE: A physically-based vegetation index for estimating wheat green LAI to mitigate the impact of leaf chlorophyll content and residue-soil background
2023, ISPRS Journal of Photogrammetry and Remote Sensing
The green leaf area index (LAI) is an important structural parameter that can be used for monitoring plant transpiration and carbon cycling over large spatial extents. LAI is also critical for understanding biophysical processes and predicting crop productivity. However, the retrieval of wheat green LAI based on canopy reflectance is always affected by the background material. For example, in the Yangtze River Basin of China, rice residues are typically returned to rice–wheat rotation fields. Thus the background for observing wheat canopy varies from a mixture of traditional wheat-soil components to wheat-residue or wheat-residue-soil, posing a significant challenge for accurate LAI estimation at the early stages of crop growth. Furthermore, when current vegetation indices (VIs) are used to estimate LAI, leaf chlorophyll content (LCC) and field moisture are two additional confounding factors. To resolve these issues, we propose a novel residue-soil adjusted red edge difference index (RSARE), which draws on concepts from a simple physical algorithm, i.e., linear spectral mixture analysis (LSMA). The results of field, satellite and modeling experiments demonstrate that the RSARE-LAI model is generally not sensitive to LCC, or to variations in residue-soil and traditional soil backgrounds, and estimates LAI with high accuracy ( ${RMSE}_{val}$ = 0.55, ${RRMSE}_{val}$ =20.71%). In comparison to traditional VI-LAI maps, RASRE-LAI maps are less sensitive to confounding factors associated with the background components, field moisture, LCC, and both inter-annual and geographical differences. This novel residue-soil background-resistant RSARE particularly improves the accuracy of wheat LAI estimation at low LAI levels, thus facilitating large-scale mapping of LAI in the early growing season.
Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index
2023, Agricultural Water Management
In cotton, an optimal balance between vegetative and reproductive growth will lead to high yields and water-use efficiency. Remote sensing estimations of vegetation variables such as crop coefficient (K_c), Leaf Area Index (LAI), and crop height during plant development can improve irrigation management. Optical and Synthetic Aperture Radar (SAR) satellite imagery can be a useful data source since they provide synoptic cover at fixed time intervals. Furthermore, they can better capture the spatial variability in the field compared to point measurements. Since clouds limit optical observations at times, the combination with SAR can provide information during cloudy periods. This study utilized optical imagery acquired by Sentinel-2 and SAR imagery acquired by Sentinel-1 over cotton fields in Israel. The Sentinel-2-based vegetation indices that are best suited for cotton monitoring were identified, and the most robust Sentinel-2 models for K_c, LAI, and height estimation achieved R² = 0.879, RMSE= 0.0645 (MERIS Terrestrial Chlorophyll Index, MTCI); R² = 0.9535, RMSE= 0.8 (MTCI); and R² = 0.8883, RMSE= 10 cm (Enhanced Vegetation Index, EVI), respectively. Additionally, a model based on the output of the SNAP Biophysical Processor LAI estimation algorithm was superior to the empirical LAI models of the best-performing vegetation indices (R² =0.9717, RMSE=0.6). The most robust Sentinel-1 models were obtained by applying an innovative local incidence angle normalization method with R² = 0.7913, RMSE= 0.0925; R² = 0.6699, RMSE= 2.3; R² = 0.6586, RMSE= 18 cm for the K_c, LAI, and height estimation, respectively. This work paves the way for future studies to design decision support systems for better irrigation management in cotton, even at the sub-plot level, by monitoring the heterogeneous development of the crop from space and adapting the irrigation accordingly to reach the target development at different growth stages during the season.
UAV-based vegetation monitoring for assessing the impact of soil loss in olive orchards in Brazil
2022, Geoderma Regional
Vegetation cover is one of the most critical factors in soil erosion processes. Notably, olive orchards have been cultivated in shallow and sloping soils, with low vegetation cover and increasing the soil exposure to raindrop impact. In the tropics, considerable care is required to adequately use cover crops to control water erosion in new frontiers of olive plantations. In this context, we proposed a new technique to correlate the cover-management factor (C-factor) with vegetation indices from images obtained by unmanned aerial vehicle (UAV) and evaluate soil erosion losses under natural rainfall. We studied the relationship between different cover indices (vegetation cover index, non-photosynthetic vegetation cover index, and total cover index) with the C-factor of the USLE/RUSLE. This study was carried out in standard erosion plots with different vegetation cover systems associated with olive cultivation. UAV images were classified by Random Forest algorithm, and soil losses were quantified by sampling after each erosive rainfall event. Results showed a good performance in UAV image classification: average user's accuracy of 94% for vegetation class and 91% for bare soil. The Total cover index presented a better performance in predicting soil loss and determining the C-factor for exponential model (R² = 0.87). UAV-based imaging demonstrates promising potential in monitoring vegetation cover crops and their impact on soil erosion. Total cover index performs better in estimating C-factor and predicting soil loss. However, the result of response surface analysis suggested that the association between total cover index and rainfall erosivity using second-order model presented the best prediction (R² = 0.98), positive correlation between rainfall erosivity and C-Factor, and negative correlation between C-factor and total cover index and rainfall erosivity.

View all citing articles on Scopus

View full text

Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture

Abstract

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

Application of remote sensing to agricultural field trials, thesis

Multispectral aerial photography as a new method in agricultural field trial analysis

Int. J. Remote Sens.

The spectral reflectance of American soils

Photogramm. Eng.

Leaf reflectance of near-infrared

Photogramm. Eng.