scholarly journals Assessment of Leaf Area Index of Rice for a Growing Cycle Using Multi-Temporal C-Band PolSAR Datasets

2019 ◽  
Vol 11 (22) ◽  
pp. 2640 ◽  
Author(s):  
Ze He ◽  
Shihua Li ◽  
Yong Wang ◽  
Yueming Hu ◽  
Feixiang Chen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Period ◽  
Estimation Accuracy ◽  
Area Index ◽  
Acceptable Error ◽  
Lower Estimation ◽  
Wide Availability ◽  
Multi Temporal ◽  
Phenological Cycle

C-band polarimetric synthetic aperture radar (PolSAR) data has been previously explored for estimating the leaf area index (LAI) of rice. Although the rice-growing cycle was partially covered in most of the studies, details for each phenological phase need to be further characterized. Additionally, the selection and exploration of polarimetric parameters are not comprehensive. This study evaluates the potential of a set of polarimetric parameters derived from multi-temporal RADARSAT-2 datasets for rice LAI estimation. The relationships of rice LAI with backscattering coefficients and polarimetric decomposition parameters have been examined in a complete phenological cycle. Most polarimetric parameters had weak relationships (R2 < 0.30) with LAI at the transplanting, reproductive, and maturity phase. Stronger relationships (R2 > 0.50) were observed at the vegetative phase. HV/VV and RVI FD had significant relationships (R2 > 0.80) with rice LAI for the whole growth period. They were utilized to develop empirical models. The best LAI inversion performance (RMSE = 0.81) was obtained when RVI FD was used. The acceptable error demonstrated the possibility to use the decomposition parameters for rice LAI estimation. The HV/VV-based model had a slightly lower estimation accuracy (RMSE = 1.29) but can be a practical alternative considering the wide availability of dual-polarized datasets.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

Study on the applicability of leaf area index estimation model in an aspen forest over a growth period

2016 ◽  
Vol 36 (8) ◽  
Author(s):  
王龑 WANG Yan ◽  
田庆久 TIAN Qingjiu ◽  
王琦 WANG Qi ◽  
王磊 WANG Lei
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Period ◽  
Estimation Model ◽  
Area Index ◽  
Aspen Forest ◽  
Index Estimation

scholarly journals Sugarcane leaf area index modeling under different soil water conditions

1997 ◽  
Vol 54 (spe) ◽  
pp. 39-44 ◽  
Author(s):  
D.A. Teruel ◽  
V. Barbieri ◽  
L.A. Ferraro Jr.
Keyword(s):  
Water Stress ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Period ◽  
Plant Growth And Development ◽  
Area Index ◽  
Stress Levels ◽  
Crop Cycle ◽  
Soil Water Conditions ◽  
Growth Deficit

The knowledge of the Leaf Area Index (LAI) variation during the whole crop cycle is essential to the modeling of the plant growth and development and, consequently, of the crop yield. Sugarcane LAI evolution models were developed for different crop cycles, by adjusting observed LAI values and growing degree-days summation data on a power-exponential function. The resultant equations simulate adequately the LAI behavior during the entire crop cycle. The effect of different water stress levels was calculated in different growth periods, upon the LAI growth The LAI growth deficit was correlated with the ratio between actual evapotranspiration and máximum evapotranspiration, and a constant named kuu was obtained hi each situation. It was noticed that the kLAI must be estimated not Just for different growth periods, but also for different water stress levels in each growth period.

scholarly journals A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index

2019 ◽  
Vol 12 (1) ◽  
pp. 16 ◽  
Author(s):  
Naichen Xing ◽  
Wenjiang Huang ◽  
Qiaoyun Xie ◽  
Yue Shi ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Saturation Effect ◽  
Estimation Accuracy ◽  
Growth Monitoring ◽  
Area Index ◽  
Multispectral Data ◽  
Red Edge

Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.

Derivation of leaf area index for grassland within alpine upland using multi-temporal RapidEye data

2013 ◽  
Vol 34 (23) ◽  
pp. 8628-8652 ◽  
Author(s):  
Sarah Asam ◽  
Heiko Fabritius ◽  
Doris Klein ◽  
Christopher Conrad ◽  
Stefan Dech
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Multi Temporal

scholarly journals Simulating the Leaf Area Index of Rice from Multispectral Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3663
Author(s):  
Shenzhou Liu ◽  
Wenzhi Zeng ◽  
Lifeng Wu ◽  
Guoqing Lei ◽  
Haorui Chen ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Estimation Accuracy ◽  
Gradient Boosting ◽  
Accurate Estimation ◽  
Area Index ◽  
Red Edge ◽  
Extreme Gradient Boosting

Accurate estimation of the leaf area index (LAI) is essential for crop growth simulations and agricultural management. This study conducted a field experiment with rice and measured the LAI in different rice growth periods. The multispectral bands (B) including red edge (RE, 730 nm ± 16 nm), near-infrared (NIR, 840 nm ± 26 nm), green (560 nm ± 16 nm), red (650 nm ± 16 nm), blue (450 nm ± 16 nm), and visible light (RGB) were also obtained by an unmanned aerial vehicle (UAV) with multispectral sensors (DJI-P4M, SZ DJI Technology Co., Ltd.). Based on the bands, five vegetation indexes (VI) including Green Normalized Difference Vegetation Index (GNDVI), Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Optimization Soil-Adjusted Vegetation Index (OSAVI) were calculated. The semi-empirical model (SEM), the random forest model (RF), and the Extreme Gradient Boosting model (XGBoost) were used to estimate rice LAI based on multispectral bands, VIs, and their combinations, respectively. The results indicated that the GNDVI had the highest accuracy in the SEM (R2 = 0.78, RMSE = 0.77). For the single band, NIR had the highest accuracy in both RF (R2 = 0.73, RMSE = 0.98) and XGBoost (R2 = 0.77, RMSE = 0.88). Band combination of NIR + red improved the estimation accuracy in both RF (R2 = 0.87, RMSE = 0.65) and XGBoost (R2 = 0.88, RMSE = 0.63). NDRE and LCI were the first two single VIs for LAI estimation using both RF and XGBoost. However, putting more than one VI together could only increase the LAI estimation accuracy slightly. Meanwhile, the bands + VIs combinations could improve the accuracy in both RF and XGBoost. Our study recommended estimating rice LAI by a combination of red + NIR + OSAVI + NDVI + GNDVI + LCI + NDRE (2B + 5V) with XGBoost to obtain high accuracy and overcome the potential over-fitting issue (R2 = 0.91, RMSE = 0.54).

scholarly journals Combining Spectral and Texture Features of UAS-Based Multispectral Images for Maize Leaf Area Index Estimation

2022 ◽  
Vol 14 (2) ◽  
pp. 331
Author(s):  
Xuewei Zhang ◽  
Kefei Zhang ◽  
Yaqin Sun ◽  
Yindi Zhao ◽  
Huifu Zhuang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Rapid Development ◽  
Texture Features ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Growth Monitoring ◽  
Multispectral Images ◽  
Area Index

The leaf area index (LAI) is of great significance for crop growth monitoring. Recently, unmanned aerial systems (UASs) have experienced rapid development and can provide critical data support for crop LAI monitoring. This study investigates the effects of combining spectral and texture features extracted from UAS multispectral imagery on maize LAI estimation. Multispectral images and in situ maize LAI were collected from test sites in Tongshan, Xuzhou, Jiangsu Province, China. The spectral and texture features of UAS multispectral remote sensing images are extracted using the vegetation indices (VIs) and the gray-level co-occurrence matrix (GLCM), respectively. Normalized texture indices (NDTIs), ratio texture indices (RTIs), and difference texture indices (DTIs) are calculated using two GLCM-based textures to express the influence of two different texture features on LAI monitoring at the same time. The remote sensing features are prescreened through correlation analysis. Different data dimensionality reduction or feature selection methods, including stepwise selection (ST), principal component analysis (PCA), and ST combined with PCA (ST_PCA), are coupled with support vector regression (SVR), random forest (RF), and multiple linear regression (MLR) to build the maize LAI estimation models. The results reveal that ST_PCA coupled with SVR has better performance, in terms of the VIs + DTIs (R2 = 0.876, RMSE = 0.239) and VIs + NDTIs (R2 = 0.877, RMSE = 0.236). This study introduces the potential of different texture indices for maize LAI monitoring and demonstrates the promising solution of using ST_PCA to realize the combining of spectral and texture features for improving the estimation accuracy of maize LAI.

scholarly journals Multi-temporal monitoring of leaf area index of rice under different nitrogen treatments using UAV images

2018 ◽  
Vol 1 (1) ◽  
pp. 7-12
Author(s):  
Xiaoyue Du ◽  
Liang Wan ◽  
Haiyan Cen ◽  
Shuobo Chen ◽  
Jiangpeng Zhu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Multi Temporal ◽  
Uav Images ◽  
Nitrogen Treatments

scholarly journals Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods

2020 ◽  
Vol 12 (13) ◽  
pp. 2110
Author(s):  
Zhulin Chen ◽  
Kun Jia ◽  
Chenchao Xiao ◽  
Dandan Wei ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Energy Utilization ◽  
Estimation Accuracy ◽  
Soil Conditions ◽  
Area Index ◽  
Estimation Models

Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on evaluating GF-5 data for LAI estimation. Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies, but these abound data also easily result in a dimensionality curse. Therefore, feature selection (FS) is necessary to reduce data redundancy to achieve more reliable estimations. Currently, machine learning (ML) algorithms have been widely used for FS. Moreover, the same ML algorithm is usually conducted for both FS and regression in LAI estimation. However, no evidence suggests that this is the optimal solution. Therefore, this study focuses on evaluating the capacity of GF-5 spectral reflectance for estimating LAI and the performances of different combination of FS and ML algorithms. Firstly, the PROSAIL model, which coupled leaf optical properties model PROSPECT and the scattering by arbitrarily inclined leaves (SAIL) model, was used to generate simulated GF-5 reflectance data under different vegetation and soil conditions, and then three FS methods, including random forest (RF), K-means clustering (K-means) and mean impact value (MIV), and three ML algorithms, including random forest regression (RFR), back propagation neural network (BPNN) and K-nearest neighbor (KNN) were used to develop nine LAI estimation models. The FS process was conducted twice using different strategies: Firstly, three FS methods were conducted to search the lowest dimension number, which maintained the estimation accuracy of all bands. Then, the sequential backward selection (SBS) method was used to eliminate the bands having minimal impact on LAI estimation accuracy. Finally, three best estimation models were selected and evaluated using reference LAI. The results showed that although the RF_RFR model (RF used for feature selection and RFR used for regression) achieved reliable LAI estimates (coefficient of determination (R2) = 0.828, root mean square error (RMSE) = 0.839), the poor performance (R2 = 0.763, RMSE = 0.987) of the MIV_BPNN model (MIV used for feature selection and BPNN used for regression) suggested using feature selection and regression conducted by the same ML algorithm could not always ensure an optimal estimation. Moreover, RF selection preserved the most informative bands for LAI estimation so that each ML regression method could achieve satisfactory estimation results. Finally, the results indicated that the RF_KNN model (RF used as feature selection and KNN used for regression) with seven GF-5 spectral band reflectance achieved the better estimation results than others when validated by simulated data (R2 = 0.834, RMSE = 0.824) and actual reference LAI (R2 = 0.659, RMSE = 0.697).

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