Plant and soil influences on estimating biomass of wheat in plant breeding plots using field spectral radiometers

1996 ◽  
Vol 47 (7) ◽  
pp. 1017 ◽  
Author(s):  
SM Bellairs ◽  
NC Turner ◽  
PT Hick ◽  
RCG Smith
Keyword(s):  
Vegetation Index ◽  
Coefficient Of Determination ◽  
Wheat Crop ◽  
Growth Stages ◽  
Experimental Conditions ◽  
Area Index ◽  
Operational Conditions ◽  
Normalised Difference Vegetation Index ◽  
Early Vigour ◽  
Dark Soil

Field spectral radiometers were used to estimate the biomass of wheat at early growth stages, as wheat breeders require a rapid, non-destructive technique to rank wheat genotypes for early vigour. Under experimental conditions, good relationships were obtained between reflectance and biomass prior to the wheat crop achieving a green area index of 1.5. When used above different soil types, good results were achieved on very uniform dark and light soils under experimental conditions, but greater differentiation between plots differing in biomass was achieved on darker soils. Similarly, under operational conditions in wheat breeders' plots, the best results were achieved against a dark soil background. Structural differences between plants also influenced solar radiation reflectance. At the Merredin site with the dark soil background, where the best correlation between reflectance and biomass was achieved, the relationship was much stronger for the more uniform genotypes at the second stage of selection than for the more heterogeneous genotypes at the first stage of selection. On these plots, the vegetation spectral indices NDVI (normalised difference vegetation index) and TSAVI (transformed soil-adjusted vegetation index) had a coefficient of determination 90-95% as good as the best regression using two wavebands. To optimise the field spectroradiometry technique for estimating early biomass, it should be applied at a weed-free site, with a uniform dark soil background and on material that is relatively homogenous in structure. We conclude that, unless these precautions are taken, the technique will have limited utility in breeding programs.

scholarly journals Combining Spectral and Texture Features of UAV Images for the Remote Estimation of Rice LAI throughout the Entire Growing Season

2021 ◽  
Vol 13 (15) ◽  
pp. 3001
Author(s):  
Kaili Yang ◽  
Yan Gong ◽  
Shenghui Fang ◽  
Bo Duan ◽  
Ningge Yuan ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Morphological Changes ◽  
Texture Features ◽  
Growing Season ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Spectral Information ◽  
Growth Stages ◽  
Area Index ◽  
Texture Information

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R2) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth.

scholarly journals Evapotranspiration simulated by CRITERIA and AquaCrop models in stony soils

2015 ◽  
Vol 10 (2) ◽  
pp. 67 ◽  
Author(s):  
Pasquale Campi ◽  
Francesca Modugno ◽  
Alejandra Navarro ◽  
Fausto Tomei ◽  
Giulia Villani ◽  
...  
Keyword(s):  
Statistical Tests ◽  
Efficiency Index ◽  
Balance Model ◽  
Coefficient Of Determination ◽  
Wheat Crop ◽  
Area Index ◽  
Aquacrop Model ◽  
Daily Evapotranspiration ◽  
Daily Scale ◽  
The Impact

The performance of a water balance model is also based on the ability to correctly perform simulations in heterogeneous soils. The objective of this paper is to test CRITERIA and AquaCrop models in order to evaluate their suitability in estimating evapotranspiration at the field scale in two types of soil in the Mediterranean region: non-stony and stony soil. The first step of the work was to calibrate both models under the non-stony conditions. The models were calibrated by using observations on wheat crop (leaf area index or canopy cover, and phenological stages as a function of degree days) and pedo-climatic measurements. The second step consisted in the analysing the impact of the soil type on the models performances by comparing simulated and measured values. The outputs retained in the analysis were soil water content (at the daily scale) and crop evapotranspiration (at two time scales: daily and crop season). The model performances were evaluated through four statistical tests: normalised difference (D%) at the seasonal time scale; and relative root mean square error (RRMSE), efficiency index (EF), coefficient of determination (r<sup>2</sup>) at the daily scale. At the seasonal scale, values of D% were less than 15% in stony and on-stony soils, indicating a good performance attained by both models. At the daily scale, the RRMSE values (2) indicate the inadequacy of AquaCrop to simulate correctly daily evapotranspiration. The higher performance of CRITERIA model to simulate daily evapotranspiration in stony soils, is due to the soil submodel, which requires the percentage skeleton as an input, while AquaCrop model takes into account the presence of skeleton by reducing the soil volume.

scholarly journals Use of Unmanned Aerial Vehicles (UAVs) Imagery in Phenotyping of Bambara Groundnut

2020 ◽  
Vol 12 (6) ◽  
pp. 12
Author(s):  
Tengku Adhwa Syaherah Tengku Mohd Suhairi ◽  
Siti Sarah Mohd Sinin ◽  
Eranga M. Wimalasiri ◽  
Nur Marahaini Mohd Nizar ◽  
Anil Shekar Tharmandran ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Ground Level ◽  
Bambara Groundnut ◽  
Growth Stages ◽  
Remotely Sensed Data ◽  
Nutrition Security ◽  
Lack Of Information ◽  
Normalised Difference Vegetation Index ◽  
Underutilised Crops ◽  
Crop Parameters

In this experiment, proximal measurements and Unmanned Aerial Vehicle (UAV) imagery was used to determine growth stages for bambara groundnut (Vigna subterranea (L.) Verdc.). The crop is a high potential crop due to its ability to yield in marginal environments, but neglected and underutilised due to lack of information on its growth in different environments. This study evaluated the correlation between Normalised Difference Vegetation Index (NDVI) derived from the ground as well as airborne sensors to test the ability of remotely sensed data to identify growth stages. NDVI and chlorophyll content of bambara groundnut leaves were measured at ground level at 18, 32, 46 and 88 days after planting (DAP) comprising vegetative, flowering, pod formation and maturity growth stages. The UAV imagery for the experimental plots was acquired with 0.2m resolution at maturity. The result showed a significant (p &lt; 0.05) linear relationship between proximal NDVI and chlorophylls content at all growth stages ofgrowth. The R2 varied from 0.57 in the vegetative stage to 0.78 in the flowering stage. Furthermore, NDVI derived from proximal measurements and UAV data showed a significant (p &lt; 0.05) correlation. The observed high correlation between proximal sensors, UAV data and crop parameters suggest that remote sensing technologies can be used for rapid phenotyping to hasten the development of models to assess the performance of underutilised crops for food and nutrition security.

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

2020 ◽  
Vol 12 (18) ◽  
pp. 2982 ◽  
Author(s):  
Christelle Gée ◽  
Emmanuel Denimal
Keyword(s):  
Biomass Production ◽  
Precision Agriculture ◽  
Weed Management ◽  
Vegetation Index ◽  
Management Strategies ◽  
Weed Competition ◽  
Growth Stages ◽  
Area Index ◽  
Crop Weed Competition ◽  
The Impact

In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.

scholarly journals Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6732
Author(s):  
Haixia Qi ◽  
Bingyu Zhu ◽  
Zeyu Wu ◽  
Yu Liang ◽  
Jianwen Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Field Experiments ◽  
Predictive Accuracy ◽  
Vegetation Indices ◽  
Planting Density ◽  
Coefficient Of Determination ◽  
Multispectral Images ◽  
Area Index

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

scholarly journals The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4937 ◽  
Author(s):  
Ziqing Xia ◽  
Yiping Peng ◽  
Shanshan Liu ◽  
Zhenhua Liu ◽  
Guangxing Wang ◽  
...  
Keyword(s):  
Regression Models ◽  
Vegetation Index ◽  
Southern China ◽  
Regional Scale ◽  
Growth Stages ◽  
Maturity Stage ◽  
Cultivated Land ◽  
Flowering Stage ◽  
Area Index ◽  
Land Quality

This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.

scholarly journals Analysis of meteorological variations on wheat yield and its estimation using remotely sensed data. A case study of selected districts of Punjab Province, Pakistan (2001-14)

2017 ◽  
Vol 12 (3) ◽  
Author(s):  
Rafia Mumtaz ◽  
Shahbaz Baig ◽  
Iram Fatima
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Crop Yield ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Least Square ◽  
Wheat Crop ◽  
Main Challenge ◽  
Normalised Difference Vegetation Index ◽  
The Impact

Land management for crop production is an essential human activity that supports life on Earth. The main challenge to be faced by the agriculture sector in coming years is to feed the rapidly growing population while maintaining the key resources such as soil fertility, efficient land use, and water. Climate change is also a critical factor that impacts agricultural production. Among others, a major effect of climate change is the potential alterations in the growth cycle of crops which would likely lead to a decline in the agricultural output. Due to the increasing demand for proper agricultural management, this study explores the effects of meteorological variation on wheat yield in Chakwal and Faisalabad districts of Punjab, Pakistan and used normalised difference vegetation index (NDVI) as a predictor for yield estimates. For NDVI data (2001-14), the NDVI product of Moderate Resolution Imaging spectrometer (MODIS) 16-day composites data has been used. The crop area mapping has been realised by classifying the satellite data into different land use/land covers using iterative self-organising (ISO) data clustering. The land cover for the wheat crop was mapped using a crop calendar. The relation of crop yield with NDVI and the impact of meteorological parameters on wheat growth and its yield has been analysed at various development stages. A strong correlation of rainfall and temperature was found with NDVI data, which determined NDVI as a strong predictor of yield estimation. The wheat yield estimates were obtained by linearly regressing the reported crop yield against the time series of MODIS NDVI profiles. The wheat NDVI profiles have shown a parabolic pattern across the growing season, therefore parabolic least square fit (LSF) has been applied prior to linear regression. The coefficients of determination (<em>R</em><sup>2</sup>) between the reported and estimated yield was found to be 0.88 and 0.73, respectively, for Chakwal and Faisalabad. This indicates that the method is capable of providing yield estimates with competitive accuracies prior to crop harvest, which can significantly aid the policy guidance and contributes to better and timely decisions.

The validation of a model estimating the Leaf Area Index of grasslands in southern China

2013 ◽  
Vol 35 (3) ◽  
pp. 245 ◽  
Author(s):  
Chengming Sun ◽  
Zhengguo Sun ◽  
Tao Liu ◽  
Doudou Guo ◽  
Shaojie Mu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Southern China ◽  
Carbon Sink ◽  
Measurement Data ◽  
Light Transmittance ◽  
Area Index ◽  
Normalised Difference Vegetation Index

In order to estimate the leaf area index (LAI) over large areas in southern China, this paper analysed the relationships between normalised difference vegetation index (NDVI) and the vegetation light transmittance and the extinction coefficient based on the use of moderate resolution imaging spectroradiometer data. By using the improved Beer–Lambert Law, a model was constructed to estimate the LAI in the grassy mountains and slopes of southern China with NDVI as the independent variable. The model was validated with field measurement data from different locations and different years in the grassland mountains and slopes of southern China. The results showed that there was a good correlation between the simulated and observed LAI values, and the values of R2 achieved were high. The relative root mean squared error was between 0.109 and 0.12. This indicated that the model was reliable. The above results provided the theoretical basis for the effective management of the grassland resources in southern China and the effective estimation of grassland carbon sink.

scholarly journals Effect of Rhizobium inoculation of seeds and foliar fertilization on productivity of Pisum sativum L.

2013 ◽  
Vol 66 (2) ◽  
pp. 71-78 ◽  
Author(s):  
Tadeusz Zając ◽  
Agnieszka Klimek-Kopyra ◽  
Andrzej Oleksy
Keyword(s):  
Pisum Sativum ◽  
Photosynthetically Active Radiation ◽  
Vegetation Index ◽  
Foliar Application ◽  
Yield Potential ◽  
Grain Legume ◽  
Growth Stages ◽  
Area Index ◽  
Active Radiation ◽  
Experimental Field

Pea (<em>Pisum sativum</em> L.) is the second most important grain legume crop in the world which has a wide array of uses for human food and fodder. One of the major factors that determines the use of field pea is the yield potential of cultivars. Presently, pre-sowing inoculation of pea seeds and foliar application of microelement fertilizers are prospective solutions and may be reasonable agrotechnical options. This research was undertaken because of the potentially high productivity of the 'afila' morphotype in good wheat complex soils. The aim of the study was to determine the effect of vaccination with <em>Rhizobium</em> and foliar micronutrient fertilization on yield of the afila pea variety. The research was based on a two-year (2009–2010) controlled field experiment, conducted in four replicates and carried out on the experimental field of the Bayer company located in Modzurów, Silesian region. experimental field soil was Umbrisol – slightly degraded chernozem, formed from loess. Nitragina inoculant, as a source of symbiotic bacteria, was applied before sowing seeds. Green area index (GAI) of the canopy, photosynthetically active radiation (PAR), and normalized difference vegetation index (NDVI) were determined at characteristic growth stages. The presented results of this study on symbiotic nitrogen fixation by leguminous plants show that the combined application of Nitragina and Photrel was the best combination for productivity. Remote measurements of the pea canopy indexes indicated the formation of the optimum leaf area which effectively used photosynthetically active radiation. The use of Nitragina as a donor of effective <em>Rhizobium</em> for pea plants resulted in slightly higher GAI values and the optimization of PAR and NDVI. It is not recommended to use foliar fertilizers or Nitragina separately due to the slowing of pea productivity.

Sign in / Sign up

Export Citation Format

Share Document