scholarly journals Improving Jujube Fruit Tree Yield Estimation at the Field Scale by Assimilating a Single Landsat Remotely-Sensed LAI into the WOFOST Model

2019 ◽  
Vol 11 (9) ◽  
pp. 1119 ◽  
Author(s):  
Tiecheng Bai ◽  
Nannan Zhang ◽  
Benoit Mercatoris ◽  
Youqi Chen
Keyword(s):  
Prediction Accuracy ◽  
Vegetation Index ◽  
Field Experiments ◽  
Remotely Sensed ◽  
Fruit Tree ◽  
Yield Estimation ◽  
Field Scale ◽  
Area Index ◽  
Tree Crops ◽  
Jujube Fruit

Few studies were focused on yield estimation of perennial fruit tree crops by integrating remotely-sensed information into crop models. This study presented an attempt to assimilate a single leaf area index (LAI) near to maximum vegetative development stages derived from Landsat satellite data into a calibrated WOFOST model to predict yields for jujube fruit trees at the field scale. Field experiments were conducted in three growth seasons to calibrate input parameters for WOFOST model, with a validated phenology error of −2, −3, and −3 days for emergence, flowering, and maturity, as well as an R2 of 0.986 and RMSE of 0.624 t ha−1 for total aboveground biomass (TAGP), R2 of 0.95 and RMSE of 0.19 m2 m−2 for LAI, respectively. Normalized Difference Vegetation Index (NDVI) showed better performance for LAI estimation than a Soil-adjusted Vegetation Index (SAVI), with a better agreement (R2 = 0.79) and prediction accuracy (RMSE = 0.17 m2 m−2). The assimilation after forcing LAI improved the yield prediction accuracy compared with unassimilated simulation and remotely sensed NDVI regression method, showing a R2 of 0.62 and RMSE of 0.74 t ha−1 for 2016, and R2 of 0.59 and RMSE of 0.87 t ha−1 for 2017. This research would provide a strategy to employ remotely sensed state variables and a crop growth model to improve field-scale yield estimates for fruit tree crops.

scholarly journals Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs

2019 ◽  
Vol 11 (20) ◽  
pp. 2456 ◽  
Author(s):  
Wanxue Zhu ◽  
Zhigang Sun ◽  
Yaohuan Huang ◽  
Jianbin Lai ◽  
Jing Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Management Practices ◽  
Near Infrared ◽  
Lookup Table ◽  
Field Scale ◽  
Area Index ◽  
Biophysical Parameter ◽  
Retrieval Accuracy

Leaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (~4 nm) improved the wheat LAI retrieval accuracy (R2 ≥ 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.

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 Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

2019 ◽  
Vol 11 (15) ◽  
pp. 1837 ◽  
Author(s):  
James Brinkhoff ◽  
Brian W. Dunn ◽  
Andrew J. Robson ◽  
Tina S. Dunn ◽  
Remy L. Dehaan
Keyword(s):  
Vegetation Index ◽  
Field Experiments ◽  
N Uptake ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Data Set ◽  
Multispectral Data ◽  
Input Variables ◽  
Better Than

Mid-season nitrogen (N) application in rice crops can maximize yield and profitability. This requires accurate and efficient methods of determining rice N uptake in order to prescribe optimal N amounts for topdressing. This study aims to determine the accuracy of using remotely sensed multispectral data from satellites to predict N uptake of rice at the panicle initiation (PI) growth stage, with a view to providing optimum variable-rate N topdressing prescriptions without needing physical sampling. Field experiments over 4 years, 4–6 N rates, 4 varieties and 2 sites were conducted, with at least 3 replicates of each plot. One WorldView satellite image for each year was acquired, close to the date of PI. Numerous single- and multi-variable models were investigated. Among single-variable models, the square of the NDRE vegetation index was shown to be a good predictor of N uptake (R 2 = 0.75, RMSE = 22.8 kg/ha for data pooled from all years and experiments). For multi-variable models, Lasso regularization was used to ensure an interpretable and compact model was chosen and to avoid over fitting. Combinations of remotely sensed reflectances and spectral indexes as well as variety, climate and management data as input variables for model training achieved R 2 < 0.9 and RMSE < 15 kg/ha for the pooled data set. The ability of remotely sensed data to predict N uptake in new seasons where no physical sample data has yet been obtained was tested. A methodology to extract models that generalize well to new seasons was developed, avoiding model overfitting. Lasso regularization selected four or less input variables, and yielded R 2 of better than 0.67 and RMSE better than 27.4 kg/ha over four test seasons that weren’t used to train the models.

scholarly journals Assessing the Performance of the WOFOST Model in Simulating Jujube Fruit Tree Growth under Different Irrigation Regimes

2019 ◽  
Vol 11 (5) ◽  
pp. 1466 ◽  
Author(s):  
Tiecheng Bai ◽  
Nannan Zhang ◽  
Youqi Chen ◽  
Benoit Mercatoris
Keyword(s):  
Tree Growth ◽  
Irrigation Treatment ◽  
Growth Dynamics ◽  
Cropping System ◽  
Fruit Tree ◽  
Total Biomass ◽  
Full Irrigation ◽  
Area Index ◽  
Jujube Fruit ◽  
Irrigation Treatments

Cropping system models are widely employed to evaluate plant water requirements and growth situations. However, these models rarely focus on growth studies of perennial fruit trees. The aim of this study was to evaluate the performance of the WOFOST (WOrld FOod STudies) model in simulating jujube fruit tree growth under different irrigation treatments. The model was calibrated on data obtained from full irrigation treatments in 2016 and 2017. The model was validated on four deficit percentages (60%, 70%, 80%, and 90%) and one full irrigation treatment from 2016 to 2018. Calibrated R2 and RMSE values of simulated versus measured soil moisture content, excluding samples on the day of irrigation and first day after irrigation, reached 0.94 and 0.005 cm3 cm−3. The model reproduced growth dynamics of the total biomass and leaf area index, with a validated R2 = 0.967 and RMSE = 0.915 t ha−1, and R2 = 0.962 and RMSE = 0.160 m2 m−2, respectively. The model also showed good global performance, with R2 = 0.86 and RMSE = 0.51 t ha−1, as well as good local agreement (R2   ≥   0.8 ) and prediction accuracy (RMSE ≤   0.62 t ha−1) for each growth season. Furthermore, 90% of full irrigation can be recommended to achieve a balance between jujube yields and water savings (average decline ratio of yield ≤ 3.8%).

scholarly journals Assimilation of Wheat and Soil States into the APSIM-Wheat Crop Model: A Case Study

2021 ◽  
Vol 14 (1) ◽  
pp. 65
Author(s):  
Yuxi Zhang ◽  
Jeffrey P. Walker ◽  
Valentijn R. N. Pauwels ◽  
Yuval Sadeh
Keyword(s):  
Soil Moisture ◽  
Remotely Sensed ◽  
Crop Productivity ◽  
Open Loop ◽  
Wheat Crop ◽  
Spatial And Temporal Variability ◽  
Baseline Scenario ◽  
State Variables ◽  
Yield Estimation ◽  
Area Index

Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.

scholarly journals Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1108 ◽  
Author(s):  
Jiayi Zhang ◽  
Xia Liu ◽  
Yan Liang ◽  
Qiang Cao ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Vegetation Index ◽  
Field Experiments ◽  
Growth Parameters ◽  
Leaf Nitrogen ◽  
Crop Growth ◽  
Nitrogen Accumulation ◽  
Wheat Cultivars ◽  
Area Index

Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013–2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68–70%, 10–12%, and 67–73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77–85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor.

Image based remote sensing method for modeling black-eyed beans (Vigna unguiculata) Leaf Area Index (LAI) and Crop Height (CH) over Cyprus

2013 ◽  
Vol 5 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Giorgos Papadavid ◽  
Dionysia Fasoula ◽  
Michael Hadjimitsis ◽  
P. Skevi Perdikou ◽  
Diofantos Hadjimitsis
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Theoretical Background ◽  
Crop Canopy ◽  
Remotely Sensed Data ◽  
Area Index ◽  
Crop Height ◽  
Statistical Relationships

AbstractIn this paper, Leaf Area Index (LAI) and Crop Height (CH) are modeled to the most known spectral vegetation index — NDVI — using remotely sensed data. This approach has advantages compared to the classic approaches based on a theoretical background. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data for estimating a spectral vegetation index (NDVI), for establishing a semiempirical relationship between black-eyed beans’ canopy factors and remotely sensed data. Such semi-empirical models can be used then for agricultural and environmental studies. A field campaign was undertaken with measurements of LAI and CH using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric (GER1500) measurements between May and June of 2010. Field spectroscopy and remotely sensed imagery have been combined and used in order to retrieve and validate the results of this study. The results showed that there are strong statistical relationships between LAI or CH and NDVI which can be used for modeling crop canopy factors (LAI, CH) to remotely sensed data. The model for each case was verified by the factor of determination. Specifically, these models assist to avoid direct measurements of the LAI and CH for all the dates for which satellite images are available and support future users or future studies regarding crop canopy parameters.

The relationship between effective plant area index and Landsat spectral response across elevation, solar insolation, and spatial scales in a northern Idaho forest

2004 ◽  
Vol 34 (2) ◽  
pp. 465-480 ◽  
Author(s):  
Amy L Pocewicz ◽  
Paul Gessler ◽  
Andrew P Robinson
Keyword(s):  
Spatial Scale ◽  
Complex Terrain ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Solar Insolation ◽  
Area Index ◽  
Plant Area Index ◽  
Plant Area ◽  
Northern Idaho ◽  
The Relationship

Leaf area index (LAI) is an important forest characteristic related to photosynthesis and carbon sequestration, and gains in efficiency for LAI measurements are possible using remotely sensed imagery. However, the potential effects of complex topography on this measurement system are not well understood. Our objective was to understand how complex terrain and measurement aggregation influence the relationship between LAI and remotely sensed vegetation indices across a mountainous conifer forest. We identified NDVIc, a middle-infrared (MIR) correction to NDVI (Normalized Difference Vegetation Index), as the vegetation index providing the best prediction of effective plant area index (PAIe), used to approximate LAI. We tested formal hypotheses to identify how elevation and solar insolation gradients and spatial scale of measurement aggregation affected the PAIe–NDVIc relationship and found that it changed across elevation at one spatial scale. Comparisons of NDVIc with NDVI revealed that vegetation index choice is important in complex terrain, and we concluded that the MIR correction improves the PAIe–NDVI relationship by explaining variation related to solar insolation. Our results suggest that NDVIc calculated from Landsat ETM+ provides a practical estimate of PAIe across our northern Idaho study area and potentially other conifer forests in complex terrain.

scholarly journals WORLDVIEW-2 IMAGERY VEGETATION INDEX CALCULATION FOR OIL PALM YIELD ESTIMATION

2016 ◽  
Vol 24 (3) ◽  
pp. 143-156
Author(s):  
Dhimas Wiratmoko ◽  
Hartono Hartono ◽  
Sigit Heru Murti BS
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Yield Estimation ◽  
Estimation Model ◽  
Area Index ◽  
Average Value ◽  
Used Oil ◽  
Red Edge ◽  
Value Range ◽  
Dependent Factor

Remote sensing application that used integrated with environmentally factors for oil palm yield estimating using Worldview-2 Imagery vegetation index (VI) was done. The aims of this study to get : 1) Red Edge Normalized Different Vegetation Index (RENDVI) and C h l o r o p h y l l I n d e x R e d E d g e ( C IRE ) ; 2) Correlation both of VI and oil palm yield; 3) oil palm yield estimation. The methods that used in this study were VI calculation by using RENDVI [(λNIR -λRED EDGE)/(λNIR +λRED EDGE )] and CIRE = [(λNIR /λRED EDGE )-1]. Oil EDGE NIR RED EDGE NIR RED EDGE palm yield estimation done by using linier regression and multiple linier regression. Linier regression used oil palm yield as dependent factor (Y) and VI as independent factor. Multiple linier regression used oil palm yield as dependent factor (Y), vegetative factors (oil palm yield, population per hectars, leaf area index) and environmentally factor (% clay, soil fertility index, altitude and water balance) as independent factors. The results of this study were: 1) the RENDVI value range -1 to 0.493 with average 0.30; while the CIRE value range -1 until 1.845 with average value 0.85. 2) The RENDVI dan CIRE have low positive linier correlation with oil pal yield rendah (rRENDVI = 0.355 dan rCIRE = 0.354); 3) Oil palm RENDVI CIRE yield estimation that using RENDVI and CIRE , vegetatation factors, environmentally factors data integration have similar correlation (r=0.763). Overall estimation model accuration get more than 90% estimation accuration on current month.

A Protocol for Establishing Soil Moisture Observations at the Complex Mountainous Region.

2020 ◽  
Author(s):  
Jaehwan Jeong ◽  
Seongkeun Cho ◽  
Seungcheol Oh ◽  
Jongjin Baik ◽  
Minha Choi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Complex Terrain ◽  
Field Experiments ◽  
Cosmic Ray ◽  
Remotely Sensed ◽  
Time Domain Reflectometry ◽  
Surface Model ◽  
Mountainous Region ◽  
Field Scale

&lt;p&gt;Soil moisture, controlling the fraction of the water between grounds and atmosphere, has been observed from various measurements to understand the hydrological cycle. Remotely sensing techniques using active and passive microwaves are regarded as an effective method for monitoring soil moisture at the regional scale. To evaluate remotely sensed soil moisture products, ground measurements including Time Domain Reflectometry (TDR) or Frequency Domain Reflectometry (FDR), and Cosmic-Ray Neutron Probe (CRNP) are widely used. In other words, field experiments considering the characteristics of sensors and soil must be preceded to retrieve soil moisture using remote sensing data. Especially, it is more even more important when applying remote sensing in complex terrain such as a mountainous region. Although there are still many challenges in the use of remote sensing technology in complex terrain, monitoring of inaccessible areas is one of the advantages of remote sensing. Therefore this study aimed to establish the soil moisture station, which employs the integration of a CRNP and FDR sensors installed within the CRNP footprint at multiple measurement depths (10, 20, 30, and 40 cm) at the mountainous region. The CRNP was firstly calibrated and subsequently combined with field average soil moisture based on a simple merging framework, to provide a field-scale soil moisture product at each corresponding layer. It was used to evaluate for large scale soil moisture validation by comparing with several model and satellite-based soil moisture products including GLDAS, SMAP, AMSR2, ASCAT, and SAR Sentinel-1. From the preliminary application of field-scale soil moisture for remotely sensed soil moisture evaluation indicated a reasonable accuracy with the highest correlation to GLDAS soil moisture product (0.87 at 40 cm), suggesting the potential of this station. An introduced protocol for estimating soil moisture in the complex mountainous region is expected to provide a better understanding of terrain impacts on soil moisture variability by assimilating field data and satellite-based products through Land Surface Model for improving soil moisture measurements.&lt;/p&gt;

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