Retrieving wheat Biomass by using a hyper-spectral device on UAV

Leaf Area of Index (LAI) refers to half of the total leaf area of all crops per unit area. It is an important index to represent the photosynthetic capacity and biomass of crops. To obtain LAI conditions of summer maize in different growth stages quickly and accurately, further guiding field fertilization and irrigation. The Unmanned aerial vehicles (UAV) multispectral data, growing degree days, and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion. The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands. The correlation analysis was conducted to verify the accuracy of the multispectral data. To include many bands as possible, four vegetation indices which included R, G, B, and NIR bands were selected in this study to test the spectral accuracy. There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band. Through correlation analysis of LAI and the vegetation index, vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model. In addition, the Canopy Height Model (CHM) and Growing degree days (GDD) of summer maize were also calculated to build the LAI inversion model. The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model (GLR), Multivariate nonlinear regression model (MNR), and the partial least squares regression (PLSR) models. R² and RMSE were used to assess the accuracy of the model. The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64, which was significant. The Wide Dynamic Range Vegetation Index (WDRVI), Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Plant Biochemical Index (PBI), Optimized Soil-Adjusted Vegetation Index (OSAVI), CHM and GDD have a higher correlation with LAI. By comparing the models constructed by the three methods, it was found that the PLSR has the best inversion effect. It was based on OSAVI, GDD, RVI, PBI, CHM, NDVI, and WDRVI, with the training model’s R² being 0.8663, the testing model’s R² being 0.7102, RMSE was 1.1755. This study showed that the LAI inversion model based on UAV multispectral vegetation index, GDD, and CHM improves the accuracy of LAI inversion effectively. That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly, and this method can provide decision support for maize growth monitoring and field fertilization. Keywords: maize, UAV multispectral, leaf area of index, growing degree day, canopy height model, vegetation index DOI: 10.25165/j.ijabe.20231602.7285 Citation: Pan F L, Guo J K, Miao J C, Xu H Y, Tian B Q, Gong D C, et al. Summer maize LAI retrieval based on multi-source remote sensing data. Int J Agric & Biol Eng, 2023; 16(2): 179-186.

Nitrogen is one of the essential nutrients required for crop growth, and hence should be applied efficiently for attaining optimum yield. To fulfil nitrogen need, absorbed nitrogen in the plant is required to be estimated. Various methods are available to estimate crop nitrogen such as tissue analysis using the methods of Kjeldahl and Dumas, which are accurate, but time-consuming and destructive. Satellite imagery provides a more extensive field view. However, they are limited to their spatial and temporal resolution. Unmanned aerial vehicle (UAV) is emerging as a promising tool that can provide the status of crop nitrogen rapidly with high spatial and temporal resolution. The objective of the study was to evaluate UAV-based imageries to show nitrogen status and predict rice yield at different growth stages. The experiments were conducted using two rice cultivars, six nitrogen applications, two water management practices, and with three replications. Soil plant analysis development (SPAD) meter readings were collected at various growth stages. First, aerial imageries of experimental site were collected using an octocopter UAV equipped with a multispectral sensor that provides reflectance values in four different bands (red, green, red edge, and near-infrared) along with SPAD values for respective flight. Second, aerial images were processed in pix4D software, to identify the most appropriate vegetation index that shows nitrogen status variation in the field and to predict yield using different vegetation indices. Nine vegetation indices were considered: ratio vegetation index, normalized difference vegetation index, normalized green red difference index, red edge difference vegetation index, green ratio vegetation index, green normalized difference vegetation index (GNDVI), wide dynamic range vegetation index, transformed normalized difference vegetation index (TNDVI), and normalized difference red edge. After that, a linear regression model was developed between the representative index and SPAD values. Finally, linear regression models developed by using VI and SPAD values were evaluated and results revealed that GNDVI-based model simulates SPAD values with R2 of 0.49, 0.49, and 0.74 at panicle, milky, and booting stages, respectively. It is also found that TNDVI-based linear regression model predicts yield with R2 of 0.71 at milky stage.

Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to select optimal vegetation indices and regression model for estimating of rice growth using UAV images. This study was conducted using a fixed- wing UAV (Model : Ebee) with Cannon S110 and Cannon IXUS camera during farming season in 2016 on the experiment field of National Institute of Crop Science. Before heading stage of rice, there were strong relationships between rice growth parameters (plant height, dry weight and LAI (Leaf Area Index)) and NDVI (Normalized Difference Vegetation Index) using natural exponential function (R≥0.97). After heading stage, there were strong relationships between rice dry weight and NDVI, gNDVI (green NDVI), RVI (Ratio Vegetation Index), CI-G (Chlorophyll Index-Green) using quadratic function (R≤-0.98). There were no apparent relationships between rice growth parameters and vegetation indices using only Red-Green-Blue band images. RVI (Ratio vegetation index), NDVI (Normalized difference vegetation index), CVI (Chlorophyll vegetation index), gNDVI (Greent normalized difference vegetation index, CI-G (Chlorophyll Index- Geen), NGRDI (Normalized green red difference index), GLI (Green leaf index), VARI (Visible atmospherically resistant index). Relationship between vegetation indices and rice growth parameter (LAI).

The objective in this study was to develop proper vegetation indices for prediction of soil irrigation demanding under vegetation covering conditions. The traditional method for the winter wheat yellow rust field survey is time consuming. It was discussed of the selection method of characteristic spectral bands and the establishing of inversion model to monitor winter wheat yellow rust using hyperspectral data in this study. The correlation coefficients between selected vegetation index and disease incidence (DI) at infected stages. Inversion models between DI and vegetation index such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), transformed vegetation index (TVI) were used to monitor yellow rust. The multi-temporal hyperspectral airborne images were acquired from winter booting stage to milking stage, and the yellow rust disease of winter wheat was analyzed using hyperspectral images. Compared with healthy wheat, spectral reflectance of disease wheat was higher in 560-670 nm bands but lower in near infrared bands and the absorption depth of chlorophyll in red band and reflectance peak in green band are relatively reduced. A novel spectral index for yellow rust indices was presented, and the degree and area of yellow rust disease were successfully remotely sensed from the multi-temporal hyperspectral data based on spectral index

This study evaluated seven vegetation indices for the monitoring of a cherry tomato crop using an unmanned aerial vehicle with a multispectral camera that measures in the green, red, and near-infrared spectral bands. A photogrammetric flight plan was designed to capture the spectral images every 2 weeks in two agricultural parcels identified as Treatment 1 ( T 1 {T}_{1} ) and Treatment 2 ( T 2 {T}_{2} ). The corresponding orthophotographs were obtained using digital photogrammetry techniques. Subsequently, vegetation indices were calculated for these orthophotographs. The mean and standard deviation of these indices were extracted, and a statistical analysis was performed to compare the vegetation indices and to analyze their behavior over time. Analysis of variance showed that the ratio vegetation index (RVI), green vegetation index (GVI), normalized difference vegetation index (NDVI), infrared percentage vegetation index (IPVI), green normalized difference vegetation index (GNDVI), and optimized soil-adjusted vegetation index (OSAVI) indices showed significant variation (P-value <0.05) over time. No statistically significant differences between the two treatments were found. IPVI, NDVI, and OSAVI showed less variation in pixel values. RVI, GVI, NDVI, IPVI, GNDVI, and OSAVI proved to be valuable tools for monitoring field crops since these indices responded to the crop growth kinetics.

This study aims to determine the differences and comparison of the results of the estimated area, the distribution of clove vegetation using the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) and to choose a vegetation index that is more suitable for clove vegetation analysis in Buleleng district, Bali. The method used is to compare statistically descriptive area and distribution class produced by the NDVI and RVI models with area data from the Forestry and Plantation Service (FPS), Buleleng regency, Bali in 2014, amounting to 7622.32 ha. The estimated area of ??clove vegetation by the NDVI model was 7852.68 ha and the RVI model was 7669.44 ha. There is an estimated difference in the area of ??clove vegetation of 183.24 ha and a difference in the broad class category of 2453.85 ha for the Rare class (NDVI > RVI) category, for the Medium class of 1611.45 ha (RVI > NDVI), and for the Dense class of 659.16 ha (RVI > NDVI). Comparison of the area with FPS data obtained 97.07% for the NDVI model and 99.39% for the RVI model. This shows that the RVI model vegetation index is more suitable for use in the estimation of the area and class of clove vegetation distribution in Buleleng regency, Bali.

Precision agriculture has the potential to improve crop yield and resource use efficiency while protecting the environment. Effective monitoring of crop biomass during the growing season is important for making in-season management decisions. One commonly used active crop canopy sensor for non-destructive estimation of crop biomass is the GreenSeeker sensor. The GreenSeeker normalized difference vegetation index (NDVI) can become saturated under medium to high biomass conditions. The Crop Circle ACS-430 is a new active canopy sensor with three spectral bands (red, red edge and NIR), and previous studies have indicated red edge-based vegetation indices have the potential to overcome the saturation problem of NDVI. So far, studies comparing these two sensor systems for estimation of spring maize biomass have been very limited. Therefore, the objective of this study was to evaluate the potential of the Crop Circle ACS-430 sensor to improve in-season estimation of spring maize biomass at different growth stages. A field experiment involving different N rates and planting densities was conducted in 2015 in Lishu County, Jilin Province in Northeast China. The GreenSeeker and Crop Circle ACS-430 sensors were used to collect spring maize canopy reflectance data at three growth stages (V5-6, V8-9, and V12-13). Plant samples were collected after sensor data measurements and aboveground biomass was determined. The results indicated that NDVI and the ratio vegetation index (RVI) of both GreenSeeker and Crop Circle ACS-430 sensorsexplained 89–92% variability in aboveground biomass across three growth stages. No obvious saturation effect was found with RVI compared with NDVI for both sensors. At the V5-V6 stages with low plant height, the vegetation indices (NDVI and RVI) of both sensors performed similarly. From V8 toV13 stage with aboveground biomass and plant height increased, the vegetation indices (NDVI and RVI) calculated from Crop Circle sensor explained 7–24% more variability in aboveground biomass than vegetation indices (NDVI and RVI) obtained with GreenSeeker sensor. The light intensity of GreenSeeker sensor decreases with measuring distance from the crop canopy. The Crop Circle sensor performance is not affected by measurement height at the range of 0.25 to 2 m above crop canopy. Based on these results, both GreenSeeker and Crop Circle ACS-430 sensors can be used to estimate aboveground biomass of spring maize at V5-V6 stages, while the Crop Circle ACS-430 sensor is more suitable for stages V8-V13.

Continuous monitoring leaf area index (LAI) of field crops in a growing season has a great challenge. The development of remote sensing technology provides a good tool for timely mapping LAI regionally. In this study, hyperspectral reflectance data (405-835 nm) obtained from an airborne hyperspectral imager (Pushbroom Hyperspectral Imager) were used to model LAI of winter wheat canopy in the 2002 crop growing season. LAI was modeled based on its semi-empirical relationships with six vegetation indices (VIs), including ratio vegetation index (RVI), modified simple ratio index (MSR), normalized difference vegetation index (NDVI), a newly proposed index NDVI-like (which resembles NDVI), modified triangular vegetation index (MTVI2), and modified soil adjusted vegetation index (MSAVI). To assess the performance of these VIs, root mean square errors (RMSEs) and determination coefficient (R-2) between estimated LAI and measured LAI were reported. Our result showed that NDVI-like was the most accurate predictor of LAI. The inclusion of a green band in MTVI2 trended to give a rise to a much quicker saturation with increase of LAI (e. g., over 3.5). MSAVI and MTVI2 showed comparable but lower potential than NDVI-like in estimating LAI. RVI and MSR demonstrated their lowest prediction accuracy, implying that they are more likely to be affected by environmental conditions such as atmosphere and cloud, thus cannot properly reflect the properties of winter wheat canopy. Our results support the use of VIs for a quick assessment of seasonal variations in winter wheat LAI. Among the indices we tested in this study, the newly developed NDVI-like model created the most accurate and reliable results.

The quantitative, convenient and non-destructive monitoring of leaf dry weight (LDW) is critical for precise management in double cropping rice production. The objective of this study is to verify the accuracy of crop growth monitoring and diagnosis apparatus (CGMD, a passive multi-spectral sensor containing 810 nm and 720 nm wavelengths) in monitoring growth index of double cropping rice, and establish the model for monitoring LDW of double cropping rice based on CGMD. Plot experiments were conducted in Jiangxi province in 2016 and 2017, including eight early and late rice cultivars and four nitrogen application rates. The normalized difference vegetation index (NDVI), differential vegetation index (DVI), and ratio vegetation index (RVI) were measured at tillering, jointing, booting, heading and filling stages with two spectrometers, CGMD and analytical spectral devices field-spec handheld 2 (ASD FH2, a passive hyper-spectral sensor containing 325 nm to 1075 nm wavelengths). In order to verify the measurement precision of CGMD, the correlation relationship of vegetation indices between CGMD and ASD FH2 was analyzed. The LDW monitoring models of double cropping rice were established based on CGMD from an experimental dataset and then validated using an independent dataset involving different early and late rice cultivars and nitrogen application rates. The results indicated that the LDW of early and late rice were increased with the increase of nitrogen application rate at different growth stages, and exhibited “low-high-low” dynamic variation trend with early and late rice development progress. The NDVI, DVI, and RVI from CGMD and ASD FH2 were significantly correlation. The correlation coefficient (<italic>r</italic>) of NDVI, DVI, and RVI from CGMD and ASD FH2 were 0.9535-0.9972, 0.9099-0.9948, and 0.9298-0.9926, respectively. This result indicated that there was highly consistent of vegetation indices from CGMD and ASD FH2, and the CGMD could replace expensive ASD FH2 to measure NDVI, DVI and RVI. Compared with the three vegetation indices based on CGMD, the correlation between RVI_CGMD and LDW was the highest. The power function model based on RVI_CGMD could accurate monitoring LDW with a determination coefficient (<italic>R</italic>²) in the range of 0.8604-0.9216, the root mean square error (RMSE), relative root mean square error (RRMSE), and <italic>r</italic> of model validation in the range of 12.97-17.87 g m^-2, 4.88%-16.79%, and 0.9951-0.9992, respectively. Compared with the manual sampling measure LDW, CGMD method can timely and accurately measure the LDW dynamic variation of double cropping rice, which had a potential to be widely applied for growth precision diagnosis and high yield and high efficiency cultivation in double cropping rice production.

Pine wilt disease (PWD) is caused by the pine wilt nematode and is a tremendous threat to coniferous forests. Remote sensing, particularly hyperspectral remote sensing, has been utilized to identify PWD. However, most studies have focused on distinguishing the spectra between infected and healthy pine trees and ignored further visualization of spectral symptoms, which could greatly improve the pre-visual diagnosis of PWD. This research used the false color feature maps (FCFMs) synthesized using the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) calculated from selected feature bands to analyze the changes in the spectral and image dimensions of the hyperspectral data. Our main findings were (1) the confirmed feature bands were 440, 550, 672, 752, 810, and 958 nm; and (2) NDVI (810, 440), NDVI (810, 672), NDVI (550, 672), RVI (810, 550), RVI (810, 672), and RVI (550, 672) were suitable to synthesize the FCFMs. As PWD developed, the color of the infected needles changed from blue and white to red on the NDVI-based feature maps and from blue to red on the RVI-based feature maps. Importantly, the color changes were captured by the FCFMs when the symptoms were not visible on the true color images, indicating the ability to identify PWD during the early infection stage.

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.

In this paper, we present an identification of rockfall-injured trees based on multiband images obtained by an unmanned aerial vehicle (UAV). A survey with a multispectral camera was performed on three rockfall sites with versatile tree species (Fagus sylvatica L., Larix decidua Mill., Pinus sylvestris L., Picea abies (L.) Karsten, and Abies alba Mill.) and with different characterizations of rockfalls and rockfall-induced injuries. At one site, rockfall injuries were induced in the same year as the survey. At the second site, they were induced one year after the initial injuries, and at the third site, they were induced six years after the first injuries. At one site, surveys were performed three years in a row. Multiband images were used to extract different vegetation indices (VIs) at the tree crown level and were further studied to see which VIs can identify the injured trees and how successfully. A total of 14 VIs were considered, including individual multispectral bands (green, red, red edge, and near-infrared) by using regression models to differentiate between the injured and uninjured groups for a single year and for three consecutive years. The same model was also used for VI differentiations among the recorded injury groups and size of the injuries. The identification of injured trees based on VIs was possible at the sites where rockfall injuries were induced at least one year before the UAV survey, and they could still be identifiable six years after the initial injuries. At the site where injuries were induced only four months before the UAV survey, the identification of injured trees was not possible. VIs that could explain the largest variability (R2 &gt; 0.3) between injured and uninjured trees were: inverse ratio index (IRVI), green–red vegetation index (GRVI), normalized difference vegetation index (NDVI), normalized ratio index (NRVI), and ratio vegetation index (RVI). RVI was the most successful, explaining 40% of the variance at two sites. R2 values only increased by a few percentages (up to 10%) when the VIs of injured trees were observed over a period of three years and mostly did not change significantly, thus not indicating if the vitality of the trees increased or decreased. Differentiation among the injured groups did not show promising results, while, on the other hand, there was a strong correlation between the VI values (RVI) and the size of the injury according to the basal area of the trees (so-called injury index). Both in the case of broadleaves and conifers at two sites, the R2 achieved a value of 0.82. The presented results indicate that the UAV-acquired multiband images at the tree crown level can be used for surveying rockfall protection forests in order to monitor their vitality, which is crucial for maintaining the protective effect through time and space.

Rice leaf folder (Cnaphalocrocis medinalis Guenée) is one of the most important pests that endanger rice development and yield, which has characteristics of large outbreak areas, high occurrence frequencies and heavy damages. At present, the monitoring methods of rice leaf folder damage is based on artificial investigation, which has the advantages of objective truth and high reliability, while there is a drawback of time-consuming, and it cannot used for a wide range of rice damage monitoring. An ASD (Analytical Spectral Devices, Inc.) Hand-held Spectroradiometer was used at jointing stage of rice. The results showed that, reflectance from rice canopy significantly decreased in the green (530-570 nm) and near infrared (700-1000 nm) regions, and significantly increased in the blue (450-520 nm) and red (580-700 nm) regions as the rice leaf folder population increased. Reflectance from rice canopy significantly decreased in the spectral regions from 737 to 1000 nm as the infestation scale of pest population increased, and the most correlation appeared at 941 nm. The more the numbers of rice leaf folder, the higher the changes of such characteristic parameters. The positive correlations were found between the damage of rice leaf folder and the discrepancy of characteristic parameters in these experimental fields. With China Remote Sensing career advancement, a large number of independent researches and development satellites have launched. Among a new generation of high-resolution satellites, GaoFen-1 (GF-1) stands out. It sets high spatial resolution (2 m-16 m), multi-spectral and high temporal resolution (4-day) with 60 km-800 km swath in a fusion technology with strategic significance. In order to explore the adaptability of Chinese GF-1 images in monitoring rice damage from rice leaf folder, nine rice fields were selected by damage severity in Xinghua City, Jiangsu Province at full heading stage in 2015, and the Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), 2-band Enhanced Vegetation Index (EVI2), Soil Adjusted Vegetation Index (SAVI), Optimized Soil Adjusted Vegetation Index (OSAVI) were used to characterize the occurrence of rice leaf folder damages, which were calculated from the satellite GF-1 retrieval data. A series of analyses were performed to disclose the relationship among these six indices and the severity of rice leaf folder. Quantitative correlation analyses showed that NDVI, EVI, EVI2, SAVI, OSAVI and leaf folding population had a highly significant correlation (P<;0.01), and SAVI had a highest correlation of 0.94. While there was no significant correlation between RVI and leaf folding population. Therefore, it was feasible to using hyperspectral data and GF-1 satellite images to monitor and warn the outbreak and development of rice leaf folder, which provided a new possible method to monitor dynamically the damage of rice leaf folder.

Non-destructive monitoring and diagnosis of plant nitrogen (N) concentration are of significant importance for precise N management and productivity forecasting in field crops. The present study was conducted to identify the common spectra wavebands and canopy reflectance spectral parameters for indicating leaf nitrogen concentration (LNC, mg N g-1 DW) and to determine quantitative relationships of LNC to canopy reflectance spectra in both rice (Oryza sativa L.) and wheat (Triticum aestivum L.). Ground-based canopy spectral reflectance and LNC were measured with seven field experiments consisting of seven different wheat cultivars and five different rice cultivars and varied N fertilization levels across three growing seasons for wheat and four growing seasons for rice. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalized difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC of wheat and rice increased with increasing N fertilization rates. Canopy reflectance, however, was a more complicated relationship under different N application rates. In the near infrared portion of the spectrum (760−1220 nm), canopy spectral reflectance increased with increasing N supply, whereas in the visible region (460−710 nm), canopy reflectance decreased with increasing N supply. For both rice and wheat, LNC was best estimated at 610, 660 and 680 nm. Among all possible RVI, DVI and NDVI of key bands from the MSR16 radiometer, NDVI(1220, 610) and RVI(1220, 610) were most highly correlated to LNC in both wheat and rice. In addition, the correlations of NDVI(1220, 610) and RVI(1220, 610) to LNC were found to be higher than those of individual wavebands at 610, 660 and 680 nm in both wheat and rice. Thus LNC in both wheat and rice could be indicated with common wavebands and vegetation indices, but separate regression equations are necessary for precisely describing the dynamic change patterns of LNC in wheat and rice. When independent data were fit to the derived equations, the root mean square error (RMSE) values for the predicted LNC with NDVI(1220, 610) and RVI(1220, 610) relative to the observed values were 10.50% and 10.52% in wheat, and 13.04% and 12.61% in rice, respectively, indicating a good fit. These results should improve the knowledge on non-destructive monitoring of leaf N status in cereal crops.

The time of forcing of spatial LAI to crop models at single or multiple stages is important to simulate crop biomass and yield in varying agro-climatic conditions and scales. The high temporal resolution (5-day) by Advanced Wide Field Sensor (AWiFS) on-board Resourcesat-1 Satellite IRS-P6 with 56 m spatial resolution and large swath (740 km) has substantially increased the availability of regional clear sky optical remote sensing data. The present study aimed at developing empirical vegetation index VI-LAI models for wheat using AWiFS optical data in four bands and in-situ measurements sampled over five different agro-climatic regions (ACRs) during 2005–2006 followed by validation during 2006–2007. While nonlinear relations exist for all the three normalized indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and Green NDVI, linear relation was the best fit for ratio vegetation index (RVI). Both NDVI and RVI models generally showed better correlation ranges (0.65–0.84 for NDVI and 0.37–0.76 for RVI) than other indices. The common NDVI-LAI model was found to produce lower root mean square errors (RMSE) between 0.5 and 1.1 from pooled model than those between 0.5 and 1.32 from regional models. The rate of substantial increase in errors from NDVI-LAI model (RMSE of modeled LAI: 0.85 to 1.28) as compared to RVI-LAI model (RMSE of modeled LAI: 1.12 to 1.17) at LAI greater than 3, than below 3 revealed the early saturation of NDVI than RVI. It is therefore recommended that LAI estimates can be used to force crop simulation model upto early vegetative stage based on NDVI and maximum vegetative to reproductive stages based on RVI.