scholarly journals The Impact of Multi-Sensor Data Assimilation on Plant Parameter Retrieval and Yield Estimation for Sugar Beet

2015 ◽  
Vol XL-7/W3 ◽  
pp. 19-25 ◽  
Author(s):  
M. Hodrius ◽  
S. Migdall ◽  
H. Bach ◽  
T. Hank
Keyword(s):  
Data Assimilation ◽  
Plant Growth ◽  
Sugar Beet ◽  
Leaf Area ◽  
Satellite Imagery ◽  
Crop Growth ◽  
Sensor Data ◽  
Landsat 8 ◽  
Yield Estimation ◽  
The Impact

Yield Maps are a basic information source for site-specific farming. For sugar beet they are not available as in-situ measurements. This gap of information can be filled with Earth Observation (EO) data in combination with a plant growth model (PROMET) to improve farming and harvest management. The estimation of yield based on optical satellite imagery and crop growth modelling is more challenging for sugar beet than for other crop types since the plants’ roots are harvested. These are not directly visible from EO. In this study, the impact of multi-sensor data assimilation on the yield estimation for sugar beet is evaluated. Yield and plant growth are modelled with PROMET. This multi-physics, raster-based model calculates photosynthesis and crop growth based on the physiological processes in the plant, including the distribution of biomass into the different plant organs (roots, stem, leaves and fruit) at different phenological stages. <br><br> The crop variable used in the assimilation is the green (photosynthetically active) leaf area, which is derived as spatially heterogeneous input from optical satellite imagery with the radiative transfer model SLC (Soil-Leaf-Canopy). Leaf area index was retrieved from RapidEye, Landsat 8 OLI and Landsat 7 ETM+ data. It could be shown that the used methods are very suitable to derive plant parameters time-series with different sensors. The LAI retrievals from different sensors are quantitatively compared to each other. Results for sugar beet yield estimation are shown for a test-site in Southern Germany. The validation of the yield estimation for the years 2012 to 2014 shows that the approach reproduced the measured yield on field level with high accuracy. Finally, it is demonstrated through comparison of different spatial resolutions that small-scale in-field variety is modelled with adequate results at 20 m raster size, but the results could be improved by recalculating the assimilation at a finer spatial resolution of 5 m.

scholarly journals Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

2020 ◽  
Vol 12 (20) ◽  
pp. 3376 ◽  
Author(s):  
Giovanni Romano ◽  
Giovanni Francesco Ricci ◽  
Francesco Gentile
Keyword(s):  
Winter Wheat ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Riparian Vegetation ◽  
Deciduous Forests ◽  
Sensor Data ◽  
Mountain Stream ◽  
Landsat 8 ◽  
Area Index ◽  
Sentinel 2

In recent decades, technological advancements in sensors have generated increasing interest in remote sensing data for the study of vegetation features. Image pixel resolution can affect data analysis and results. This study evaluated the potential of three satellite images of differing resolution (Landsat 8, 30 m; Sentinel-2, 10 m; and Pleiades 1A, 2 m) in assessing the Leaf Area Index (LAI) of riparian vegetation in two Mediterranean streams, and in both a winter wheat field and a deciduous forest used to compare the accuracy of the results. In this study, three different retrieval methods—the Caraux-Garson, the Lambert-Beer, and the Campbell and Norman equations—are used to estimate LAI from the Normalized Difference Vegetation Index (NDVI). To validate sensor data, LAI values were measured in the field using the LAI 2200 Plant Canopy Analyzer. The statistical indices showed a better performance for Pleiades 1A and Landsat 8 images, the former particularly in sites characterized by high canopy closure, such as deciduous forests, or in areas with stable riparian vegetation, the latter where stable reaches of riparian vegetation cover are almost absent or very homogenous, as in winter wheat fields. Sentinel-2 images provided more accurate results in terms of the range of LAI values. Considering the different types of satellite imagery, the Lambert-Beer equation generally performed best in estimating LAI from the NDVI, especially in areas that are geomorphologically stable or have a denser vegetation cover, such as deciduous forests.

scholarly journals Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3161 ◽  
Author(s):  
Haizhu Pan ◽  
Zhongxin Chen ◽  
Allard de Wit ◽  
Jianqiang Ren
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Crop Growth ◽  
Open Loop ◽  
Yield Estimation ◽  
Spatiotemporal Resolution ◽  
Sentinel 2

It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.

Crop Yield Estimation Using Multi-source Satellite Image Series and Deep Learning

2020 ◽  
Author(s):  
Gohar Ghazaryan ◽  
Sergii Skakun ◽  
Simon König ◽  
Ehsan Eyshi Rezaei ◽  
Stefan Siebert ◽  
...  
Keyword(s):  
Time Series ◽  
Agricultural Production ◽  
Remotely Sensed ◽  
Growth Conditions ◽  
Crop Growth ◽  
County Level ◽  
Yield Estimation ◽  
Field Level ◽  
The Impact ◽  
Level Analysis

&lt;p&gt;Timely monitoring of agricultural production and early yield predictions are essential for food security. Crop growth conditions and yield are related to climate variability and extreme events. Remotely sensed time-series can be used to study the variability in crop growth and agricultural production. However, the choice of remotely sensed data and methods is still an issue, as different datasets have different spatiotemporal characteristics. Thus, our primary goal was to study the impact of applying different remotely sensed time series on yield estimation in U.S. at the county and field scale. Furthermore, the impact of crop growth conditions on yield variability was assessed. For county-level analysis, MODIS-based surface reflectance, Land Surface Temperature, and Evapotranspiration time series were used as input datasets. Whereas field-level analysis was carried out using NASA&amp;#8217;s Harmonized Landsat Sentinel-2 (HLS) product. 3D convolutional neural network (CNN) and CNN followed by long-short term memory (LSTM) were used. For county-level analysis, the CNN-LSTM model had the highest accuracy, with a mean percentage error of 10.3% for maize and 9.6% for soybean. This model presented robust results for the year 2012, which is considered a drought year. In the case of field-level analysis, all models achieved accurate results with R&lt;sup&gt;2 &lt;/sup&gt;exceeding 0.8 when data from mid growing season were used. The results highlight the potential of yield estimation at different management scales.&lt;/p&gt;

scholarly journals Effect of Cowpea severe mosaic virus on Crop Growth Characteristics and Yield of Cowpea

Plant Disease ◽  
2005 ◽  
Vol 89 (5) ◽  
pp. 515-520 ◽  
Author(s):  
H. M. Booker ◽  
P. Umaharan ◽  
C. R. McDavid
Keyword(s):  
Mosaic Virus ◽  
Leaf Area ◽  
Growth Phase ◽  
Field Experiments ◽  
Yield Loss ◽  
Crop Growth ◽  
Growth And Yield ◽  
Exponential Growth Phase ◽  
Area Index ◽  
The Impact

Field experiments were carried out in St. Augustine, Trinidad & Tobago, West Indies to determine the effects of time of inoculation of Cowpea severe mosaic virus (CPSMV) and cultivar on crop growth and yield in cowpea (Vigna unguiculata). Crop growth and yield loss were investigated through growth analysis and yield component analysis on three cultivars in two seasons (wet and dry). Time of inoculation had the most profound impact on yield. Inoculations during the early log phase (seedling stage), 12 days after seeding (DAS), consistently had the greatest impact (50 to 85% yield loss) compared with those inoculated during the exponential growth phase (24 DAS; 22 to 66% yield loss) or linear growth phase (35 DAS; 2 to 36% yield loss). The effects were particularly pronounced in the dry season and in the more determinate cultivar, H8-8-27. Reduction in maximum leaf area index, leaf area duration, or maximum vegetative dry matter explained reductions in yield. Yield reductions resulted primarily from reduced pod number per plant and, to a lesser extent, from reduced average pod dry weight. The results show that CPSMV control measures should be aimed at delaying infection by CPSMV to minimize the impact on cowpea yield.

scholarly journals Monitoring of specially protected natural territories of forest-steppe landscapes of the Stavropol upland by means of remote sensing data

2021 ◽  
Vol 27 (1) ◽  
pp. 364-376
Author(s):  
Tigran Shahbazyan
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Anthropogenic Factors ◽  
Forest Steppe ◽  
Landsat 8 ◽  
Sensing Data ◽  
Ndvi Index ◽  
The Impact

The article considers the methodology of monitoring specially protected natural areas using remote sensing data. The research materials are satellite images of the Landsat 5 and Landsat 8 satellites, obtained from the resource of the US Geological Survey. The key areas of the study were 3 specially protected areas located within the boundaries of the forest-steppe landscapes of the Stavropol upland, the reserves «Alexandrovskiy», «Russkiy Les», «Strizhament». The space survey materials were selected for the period 1991–2020, and the data from the summer seasons were used. The NDVI index is chosen as the method of processing the spectral channels of satellite imagery. To integrate long-term satellite imagery into a single raster image, the method of variance of the variation series for the NDVI index was used. The article describes an algorithm for processing satellite images, which allows us to identify the features of the dynamics of the vegetation state of the studied territory for the period 1991–2020. The bitmap image constructed by means of the variance of the NDVI index was classified by the quantile method, to translate numerical values into classes with qualitative characteristics. There were 4 classes of the territory according to the degree of dynamism of the vegetation state: “stable”, “slightly variable”, “moderately variable”, “highly variable”. The paper highlights the factors of landscape transformation, including natural and anthropogenic ones. In the course of the study, the determining influence of anthropogenic factors of transformation was noted. The greatest impact is on the reserve «Alexandrovskiy», the least on the reserve «Russkiy Les», in the reserve «Strizhament» the impact is expressed locally. The paper identifies the leading anthropogenic factors of vegetation transformation, based on their influence on vegetation.

scholarly journals Assessing the Impact of LAI Data Assimilation on Simulations of the Soil Water Balance and Maize Development Using MOHID-Land

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1367 ◽  
Author(s):  
Tiago Ramos ◽  
Lucian Simionesei ◽  
Ana Oliveira ◽  
Hanaa Darouich ◽  
Ramiro Neves
Keyword(s):  
Data Assimilation ◽  
Water Balance ◽  
Soil Water ◽  
Hydrological Modeling ◽  
Soil Water Balance ◽  
Landsat 8 ◽  
State Variables ◽  
Area Index ◽  
Final Model ◽  
The Impact

Hydrological modeling at the catchment scale requires the upscaling of many input parameters for better characterizing landscape heterogeneity, including soil, land use and climate variability. In this sense, remote sensing is often considered as a practical solution. This study aimed to access the impact of assimilation of leaf area index (LAI) data derived from Landsat 8 imagery on MOHID-Land’s simulations of the soil water balance and maize state variables (LAI, canopy height, aboveground dry biomass and yield). Data assimilation impacts on final model results were first assessed by comparing distinct modeling approaches to measured data. Then, the uncertainty related to assimilated LAI values was quantified on final model results using a Monte Carlo method. While LAI assimilation improved MOHID-Land’s estimates of the soil water balance and simulations of crop state variables during early stages, it was never sufficient to overcome the absence of a local calibrated crop dataset. Final model estimates further showed great uncertainty for LAI assimilated values during earlier crop stages, decreasing then with season reaching its end. Thus, while model simulations can be improved using LAI data assimilation, additional data sources should be considered for complementing crop parameterization.

scholarly journals Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 255 ◽  
Author(s):  
Francesco Novelli ◽  
Heide Spiegel ◽  
Taru Sandén ◽  
Francesco Vuolo
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Field Data ◽  
Crop Growth ◽  
Wheat Crop ◽  
Calibration Data ◽  
Yield Estimation ◽  
Area Index ◽  
Sentinel 2

Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.

scholarly journals Extending the CSM-CERES-Beet Model to Simulate Impact of Observed Leaf Disease Damage on Sugar Beet Yield

Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1930
Author(s):  
Emir Memic ◽  
Simone Graeff-Hönninger ◽  
Oliver Hensel ◽  
William D. Batchelor
Keyword(s):  
Sugar Beet ◽  
Leaf Area ◽  
Leaf Spot ◽  
Leaf Spot Disease ◽  
Root Weight ◽  
Storage Root ◽  
Cercospora Leaf Spot ◽  
Area Index ◽  
Spot Disease ◽  
The Impact

A CSM-CERES-Beet pest damage routine was modified to simulate the impact of Cercospora leaf spot disease effects on sugar beet yield. Foliar disease effects on sugar beet growth and yield were incorporated as daily damage to leaf area and photosynthesis, which was linked to daily crop growth and biomass accumulation. An experiment was conducted in Southwest Germany (2016–2018) with different levels of disease infection. Data collected included time-series leaf area index, top weight, storage root weight and Cercospora leaf spot disease progress. The model was calibrated using statistical and visual fit for one treatment and evaluated for eight treatments over three years. Model performance of the calibration treatment for all three variables resulted in R2 values higher than 0.82 and d-statistics higher than 0.94. Evaluation treatments for all three observation groups resulted in high R2 and d-statistics with few exceptions mainly caused by weather extremes. Root mean square error values for calibration and evaluation treatments were satisfactory. Model statistics indicate that the approach can be used as a suitable decision support system to simulate the impact of observed Cercospora leaf spot damage on accumulated above-ground biomass and storage root yield on a plot/site-specific scale.

scholarly journals Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

2014 ◽  
Vol 18 (1) ◽  
pp. 173-192 ◽  
Author(s):  
A. L. Barbu ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
S. Lafont
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Land Data Assimilation ◽  
The Impact

Abstract. The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT) backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs) land surface model within the the externalised surface model (SURFEX) modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011). The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA (Soil Moisture Observing System–Meteorological Automatic Network Integrated Application) stations shows improved anomaly correlations for eight stations.

scholarly journals Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

2017 ◽  
Vol 24 (3) ◽  
pp. 553-567 ◽  
Author(s):  
Hazuki Arakida ◽  
Takemasa Miyoshi ◽  
Takeshi Ise ◽  
Shin-ichiro Shima ◽  
Shunji Kotsuki
Keyword(s):  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Model Parameters ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Area Index ◽  
The Real ◽  
Global Vegetation ◽  
The Impact

Abstract. We developed a data assimilation system based on a particle filter approach with the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, simulating the satellite-based LAI. Although we assimilated only LAI as a whole, the tree and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data and obtained promising results.

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