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 Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

2020 ◽  
Vol 12 (13) ◽  
pp. 2148 ◽  
Author(s):  
Adnan Rajib ◽  
I Luk Kim ◽  
Heather E. Golden ◽  
Charles R. Lane ◽  
Sujay V. Kumar ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Dynamics ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Area Index ◽  
Daily Streamflow ◽  
Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

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.

Added-value of satellite soil moisture assimilation in hydrological modelling: an evaluation through a large experiment over Europe

2020 ◽  
Author(s):  
Domenico De Santis ◽  
Christian Massari ◽  
Stefania Camici ◽  
Sara Modanesi ◽  
Luca Brocca ◽  
...  
Keyword(s):  
Surface Layer ◽  
Soil Moisture ◽  
Winter Season ◽  
Assimilation Efficiency ◽  
Remotely Sensed ◽  
Open Loop ◽  
Added Value ◽  
Observation Error ◽  
Loop Model ◽  
Rainfall Runoff

<p>The increasing availability of remotely sensed soil moisture (SM) observations has brought great interest in their use in data assimilation (DA) frameworks in order to improve streamflow simulations. However, the added-value of assimilating satellite SM into rainfall-runoff models is still difficult to be quantified, and much more research is needed to fully understand benefits and limitations.</p><p>Here, an extensive evaluation of remotely sensed SM assimilation on hydrological model performances was carried out, involving 775 catchments across Europe. Satellite observations for over a decade from the three ESA CCI SM products (ACTIVE, PASSIVE and COMBINED) were assimilated in a lumped rainfall-runoff model which includes a thin surface layer in its soil schematization, by using the Ensemble Kalman Filter (EnKF). Observations were mapped into the space of modelled surface layer SM through a monthly CDF-matching prior to DA, while the observation error variance was calibrated in every catchment in order to maximize the assimilation efficiency.</p><p>The implemented DA procedure, aimed at reducing only random errors in SM variables, generally resulted in limited runoff improvements, although with some variability within the study domain. Factors emerging as relevant for the assessment of assimilation impact were: i) the open-loop (OL) model performance; ii) the remotely sensed SM accuracy for hydrological purposes; iii) the sensitivity of the catchment response to soil moisture dynamics; and also iv) issues in DA implementation (e.g., violations in theoretical assumptions).</p><p>The open-loop model results contributed significantly to explain differences in assimilation performances observed within the study area as well as at the seasonal scale; overall, the high OL efficiency is the main cause of the slight improvements here observed after DA. The integration of satellite SM information, showing greater skills in correspondence of poorer streamflow simulations, confirmed a potential in reducing the effects of rainfall inaccuracies.</p><p>The variability in satellite SM accuracy for hydrological purposes was also found to be relevant in DA assessment. The ACTIVE product assimilation generally provided the best streamflow results within the study catchments, followed by COMBINED and PASSIVE ones, while factors affecting the SM retrieval such as vegetation density and topographic complexity were not found to have a decisive effect on DA results.</p><p>Low assimilation performances were obtained when runoff was dominated by snow dynamics (e.g., in the northern areas of the study domain, or in winter season at medium latitudes), due to the SM conditions having a negligible effect on the hydrological response.</p><p>Finally, in basins where SM was persistently near the saturation value, deteriorations in hydrological simulations were observed, mainly attributable to violation of error normality hypothesis in EnKF due to the bounded nature of soil moisture.</p><p>In conclusion, the added-value of assimilating remotely sensed SM into rainfall-runoff models was confirmed to be linked to multiple factors: understanding their contribution and interactions deserves further research and is fundamental to take full advantage of the potential of satellite SM retrievals, in parallel with their progress in terms of accuracy and resolutions.</p>

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 Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts

2014 ◽  
Vol 7 (3) ◽  
pp. 931-946 ◽  
Author(s):  
C. Szczypta ◽  
J.-C. Calvet ◽  
F. Maignan ◽  
W. Dorigo ◽  
F. Baret ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Interannual Variability ◽  
Land Surface ◽  
Root Zone ◽  
Mediterranean Area ◽  
Remotely Sensed ◽  
Climatic Conditions ◽  
Leaf Life Span ◽  
Moisture Profile ◽  
Area Index

Abstract. Two new remotely sensed leaf area index (LAI) and surface soil moisture (SSM) satellite-derived products are compared with two sets of simulations of the ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) land surface models. We analyse the interannual variability over the period 1991–2008. The leaf onset and the length of the vegetation growing period (LGP) are derived from both the satellite-derived LAI and modelled LAI. The LGP values produced by the photosynthesis-driven phenology model of ISBA-A-gs are closer to the satellite-derived LAI and LGP than those produced by ORCHIDEE. In the latter, the phenology is based on a growing degree day model for leaf onset, and on both climatic conditions and leaf life span for senescence. Further, the interannual variability of LAI is better captured by ISBA-A-gs than by ORCHIDEE. In order to investigate how recent droughts affected vegetation over the Euro-Mediterranean area, a case study addressing the summer 2003 drought is presented. It shows a relatively good agreement of the modelled LAI anomalies with the observations, but the two models underestimate plant regrowth in the autumn. A better representation of the root-zone soil moisture profile could improve the simulations of both models. The satellite-derived SSM is compared with SSM simulations of ISBA-A-gs only, as ORCHIDEE has no explicit representation of SSM. Overall, the ISBA-A-gs simulations of SSM agree well with the satellite-derived SSM and are used to detect regions where the satellite-derived product could be improved. Finally, a correspondence is found between the interannual variability of detrended SSM and LAI. The predictability of LAI is less pronounced using remote sensing observations than using simulated variables. However, consistent results are found in July for the croplands of the Ukraine and southern Russia.

Regional Wheat Yield Estimation by Integration of Remotely Sensed Soil Moisture into a Crop Model

2019 ◽  
Vol 45 (6) ◽  
pp. 770-781 ◽  
Author(s):  
Muhammad Fahad ◽  
Ishfaq Ahmad ◽  
Mariam Rehman ◽  
Muhammad Mohsin Waqas ◽  
Farhana Gul
Keyword(s):  
Soil Moisture ◽  
Wheat Yield ◽  
Remotely Sensed ◽  
Crop Model ◽  
Yield Estimation

scholarly journals Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts

2013 ◽  
Vol 6 (4) ◽  
pp. 5553-5594 ◽  
Author(s):  
C. Szczypta ◽  
J.-C. Calvet ◽  
F. Maignan ◽  
W. Dorigo ◽  
F. Baret ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Interannual Variability ◽  
Land Surface ◽  
Root Zone ◽  
Mediterranean Area ◽  
Remotely Sensed ◽  
Climatic Conditions ◽  
Leaf Life Span ◽  
Moisture Profile ◽  
Area Index

Abstract. Two new remotely sensed Leaf Area Index (LAI) and Surface Soil Moisture (SSM) satellite products are compared with two sets of simulations of the ORCHIDEE and ISBA-A-gs land surface models to investigate how recent droughts affected vegetation over the Euro-Mediterranean area. We analyze the interannual variability over the period 1991–2008. The leaf onset and the Length of the vegetation Growing Period (LGP) are derived from the satellite-derived LAI and from the modelled LAI. The LGP values produced by the photosynthesis-driven phenology model of ISBA-A-gs are closer to the satellite-derived LAI LGP than those produced by ORCHIDEE. In the latter, the phenology is based on a growing degree-day model for leaf onset, and on both climatic conditions and leaf life span for senescence. Further, the interannual variability of LAI is better captured by ISBA-A-gs than by ORCHIDEE. The summer 2003 drought case study shows a relatively good agreement of the modelled LAI anomalies with the observations, but the two models underestimate plant regrowth in the autumn. A better representation of the root-zone soil moisture profile could improve the simulations of both models. The satellite-derived SSM is compared with SSM simulations of ISBA-A-gs, only, as ORCHIDEE has no explicit representation of SSM. Overall, the ISBA-A-gs simulations of SSM agree well with the satellite-derived SSM and are used to detect regions where the satellite product could be improved. Finally, a correspondence is found between the interannual variability of detrended SSM and LAI. The predictability of LAI is less pronounced using remote sensing observations than using simulated variables. However, consistent results are found in July for the croplands of Ukraine and southern Russia.

scholarly journals Spatial and temporal variability of biophysical variables in Southwestern France from airborne L-band radiometry

2012 ◽  
Vol 9 (1) ◽  
pp. 895-936
Author(s):  
E. Zakharova ◽  
J.-C. Calvet ◽  
S. Lafont ◽  
C. Albergel ◽  
J.-P. Wigneron ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Standard Deviation ◽  
Temporal Correlation ◽  
Microwave Emission ◽  
Spatial And Temporal Variability ◽  
Grid Cells ◽  
Spatial Correlations ◽  
Satellite Mission ◽  
Area Index ◽  
L Band

Abstract. In 2009 and 2010 the L-band microwave Cooperative Airborne Radiometer for Ocean and Land Studies (CAROLS) campaign was performed in Southwestern France to support the calibration and validation of the new Soil Moisture and Ocean Salinity (SMOS) satellite mission. The L-band Microwave Emission of the Biosphere (L-MEB) model was used to retrieve Surface Soil Moisture (SSM) and the Vegetation Optical Depth (VOD) from the CAROLS brightness temperature measurements. The CAROLS SSM was compared with in situ observations at 11 sites of the SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Network Integrated Application) network of Météo-France. For eight of them, significant correlations were observed (0.51 ≤ r ≤ 0.82), with standard deviation of differences ranging from 0.039 m3 m−3 to 0.141 m3 m−3. Also, the CAROLS SSM was compared with SSM values simulated by the A-gs version of the Interactions between Soil, Biosphere and Atmosphere (ISBA-A-gs) model along twenty flight lines, at a resolution of 8 km × 8 km. A significant spatial correlation between these two datasets was observed for all the flights (0.36 ≤ r ≤ 0.85). The CAROLS VOD presented significant spatial correlations with the vegetation water content (VWC) derived from the spatial distribution of vegetation types used in ISBA-A-gs and from the Leaf Area Index (LAI) simulated for low vegetation. On the other hand, the CAROLS VOD presented little temporal changes, and no temporal correlation was observed with the simulated LAI. For low vegetation, the ratio of VOD to VWC tended to decrease, from springtime to summertime. For 83% of ISBA-A-gs grid cells (8 km × 8 km), sampled every 5 m by CAROLS observations at a spatial resolution of about 2 km, the standard deviation of the sub-grid CAROLS SSM was lower than 0.05 m3 m−3. The presence of small water bodies within the ISBA-A-gs grid cells tended to increase the CAROLS SSM spatial variability, up to 0.10 m3 m−3. Also, the grid cells characterised by a high vegetation cover heterogeneity presented higher standard deviation values, for both SSM and VOD.

scholarly journals Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2613 ◽  
Author(s):  
Carlos Echeverría ◽  
Guiomar Ruiz-Pérez ◽  
Cristina Puertes ◽  
Luis Samaniego ◽  
Brian Barrett ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Validation Period ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Spatio Temporal ◽  
Calibration Approach

The aim of this study was to implement an eco-hydrological distributed model using only remotely sensed information (soil moisture and leaf area index) during the calibration phase. Four soil moisture-based metrics were assessed, and the best alternative was chosen, which was a metric based on the similarity between the principal components that explained at least 95% of the soil moisture variation and the Nash-Sutcliffe Efficiency (NSE) index between simulated and observed surface soil moisture. The selected alternative was compared with a streamflow-based calibration approach. The results showed that the streamflow-based calibration approach, even presenting satisfactory results in the calibration period (NSE = 0.91), performed poorly in the validation period (NSE = 0.47) and Leaf Area Index (LAI) and soil moisture were neither sensitive to the spatio-temporal pattern nor to the spatial correlation in both calibration and validation periods. Hence, the selected soil moisture-based approach showed an acceptable performance in terms of discharges, presenting a negligible decrease in the validation period (ΔNSE = 0.1) and greater sensitivity to the spatio-temporal variables’ spatial representation.

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