scholarly journals How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?

2020 ◽  
Vol 12 (6) ◽  
pp. 925 ◽  
Author(s):  
Andreas Tewes ◽  
Holger Hoffmann ◽  
Manuel Nolte ◽  
Gunther Krauss ◽  
Fabian Schäfer ◽  
...  
Keyword(s):  
Water Stress ◽  
Soil Texture ◽  
Crop Model ◽  
Limiting Factor ◽  
Modeling Framework ◽  
Area Index ◽  
Field Level ◽  
Local Soil ◽  
Field Heterogeneity ◽  
Sentinel 2

The combination of Sentinel-2 derived information about sub-field heterogeneity of crop canopy leaf area index (LAI) and SoilGrids-derived information about local soil properties might help to improve the prediction accuracy of crop simulation models at sub-field level without prior knowledge of detailed site characteristics. In this study, we ran a crop model using either soil texture derived from samples that were taken spatially distributed across a field and analyzed in the lab (AS) or SoilGrids-derived soil texture (SG) as model input in combination with different levels of LAI assimilation. We relied on the LINTUL5 model implemented in the SIMPLACE modeling framework to simulate winter wheat biomass development in 40 to 60 points in each field with detailed measured soil information available, for 14 fields across France, Germany, and the Netherlands during two growing seasons. Water stress was the only growth-limiting factor considered in the model. The model performance was evaluated against total aboveground biomass measurements at harvest with regard to the average per-field prediction and the simulated spatial variability within the field. Our findings showed that a) per-field average biomass predictions of SG-based modeling approaches were not inferior to those using AS-texture as input, but came with a greater prediction uncertainty, b) relying on the generation of an ensemble without LAI assimilation might produce results as accurate as simulations where LAI is assimilated, and c) sub-field heterogeneity was not reproduced well in any of the fields, predominantly because of an inaccurate simulation of water stress in the model. We conclude that research should be devoted to the testing of different approaches to simulate soil moisture dynamics and to the testing in other sites, potentially using LAI products derived from other remotely sensed imagery.

scholarly journals Assimilation of Sentinel-2 Estimated LAI into a Crop Model: Influence of Timing and Frequency of Acquisitions on Simulation of Water Stress and Biomass Production of Winter Wheat

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1813
Author(s):  
Andreas Tewes ◽  
Carsten Montzka ◽  
Manuel Nolte ◽  
Gunther Krauss ◽  
Holger Hoffmann ◽  
...  
Keyword(s):  
Water Stress ◽  
Winter Wheat ◽  
Biomass Production ◽  
Aboveground Biomass ◽  
Stem Elongation ◽  
Simulation Models ◽  
Development Stage ◽  
Crop Model ◽  
Area Index ◽  
Sentinel 2

The Sentinel-2 (S2) Toolbox permits for the automated retrieval of leaf area index (LAI). LAI assimilation into crop simulation models could aid to improve the prediction accuracy for biomass at field level. We investigated if the combined effects of assimilation date and corresponding growth stage plus observational frequency have an impact on the crop model-based simulation of water stress and biomass production. We simulated winter wheat growth in nine fields in Germany over two years. S2 LAI estimations for each field were categorized into three phases, depending on the development stage of the crop at acquisition date (tillering, stem elongation, booting to flowering). LAI was assimilated in every possible combinational setup using the ensemble Kalman filter (EnKF). We evaluated the performance of the simulations based on the comparison of measured and simulated aboveground biomass at harvest. The results showed that the effects on water stress remained largely limited, because it mostly occurred after we stopped LAI assimilation. With regard to aboveground biomass, we found that the assimilation of only one LAI estimate from either the tillering or the booting to flowering stage resulted in simulated biomass values similar or closer to measured values than in those where more than one LAI estimate from the stem elongation phase were assimilated. LAI assimilation after the tillering phase might therefore be not necessarily required, as it may not lead to the desired improvement effect.

scholarly journals Modeling the monthly mean soil-water balance with a statistical-dynamical ecohydrology model as coupled to a two-component canopy model

2010 ◽  
Vol 14 (10) ◽  
pp. 2099-2120 ◽  
Author(s):  
J. P. Kochendorfer ◽  
J. A. Ramírez
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Water Balance ◽  
Soil Water ◽  
Soil Texture ◽  
Root Zone ◽  
Soil Water Balance ◽  
Area Index ◽  
Two Component ◽  
Canopy Model

Abstract. The statistical-dynamical annual water balance model of Eagleson (1978) is a pioneering work in the analysis of climate, soil and vegetation interactions. This paper describes several enhancements and modifications to the model that improve its physical realism at the expense of its mathematical elegance and analytical tractability. In particular, the analytical solutions for the root zone fluxes are re-derived using separate potential rates of transpiration and bare-soil evaporation. Those potential rates, along with the rate of evaporation from canopy interception, are calculated using the two-component Shuttleworth-Wallace (1985) canopy model. In addition, the soil column is divided into two layers, with the upper layer representing the dynamic root zone. The resulting ability to account for changes in root-zone water storage allows for implementation at the monthly timescale. This new version of the Eagleson model is coined the Statistical-Dynamical Ecohydrology Model (SDEM). The ability of the SDEM to capture the seasonal dynamics of the local-scale soil-water balance is demonstrated for two grassland sites in the US Great Plains. Sensitivity of the results to variations in peak green leaf area index (LAI) suggests that the mean peak green LAI is determined by some minimum in root zone soil moisture during the growing season. That minimum appears to be close to the soil matric potential at which the dominant grass species begins to experience water stress and well above the wilting point, thereby suggesting an ecological optimality hypothesis in which the need to avoid water-stress-induced leaf abscission is balanced by the maximization of carbon assimilation (and associated transpiration). Finally, analysis of the sensitivity of model-determined peak green LAI to soil texture shows that the coupled model is able to reproduce the so-called "inverse texture effect", which consists of the observation that natural vegetation in dry climates tends to be most productive in sandier soils despite their lower water holding capacity. Although the determination of LAI based on complete or near-complete utilization of soil moisture is not a new approach in ecohydrology, this paper demonstrates its use for the first time with a new monthly statistical-dynamical model of the water balance. Accordingly, the SDEM provides a new framework for studying the controls of soil texture and climate on vegetation density and evapotranspiration.

scholarly journals Studying the Feasibility of Assimilating Sentinel-2 and PlanetScope Imagery into the SAFY Crop Model to Predict Within-Field Wheat Yield

2021 ◽  
Vol 13 (12) ◽  
pp. 2395
Author(s):  
V.S. Manivasagam ◽  
Yuval Sadeh ◽  
Gregoriy Kaplan ◽  
David J. Bonfil ◽  
Offer Rozenstein
Keyword(s):  
High Resolution ◽  
Spatial Information ◽  
Wheat Yield ◽  
Simple Algorithm ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Yield Prediction ◽  
Area Index ◽  
Crop Models ◽  
Sentinel 2

Spatial information embedded in a crop model can improve yield prediction. Leaf area index (LAI) is a well-known crop variable often estimated from remote-sensing data and used as an input into crop models. In this study, we evaluated the assimilation of LAI derived from high-resolution (both spatial and temporal) satellite imagery into a mechanistic crop model, a simple algorithm for yield estimate (SAFY), to assess the within-field crop yield. We tested this approach on spring wheat grown in Israel. Empirical LAI models were derived from the biophysical processor for Sentinel-2 LAI and spectral vegetation indices from Sentinel-2 and PlanetScope images. The predicted grain yield obtained from the SAFY model was compared against the harvester’s yield map. LAI derived from PlanetScope and Sentinel-2 fused images achieved higher yield prediction (RMSE = 69 g/m2) accuracy than that of Sentinel-2 LAI (RMSE = 88 g/m2). Even though the spatial yield estimation was only moderately correlated to the ground truth (R2 = 0.45), this is consistent with current studies in this field, and the potential to capture within-field yield variations using high-resolution imagery has been demonstrated. Accordingly, this is the first application of PlanetScope and Sentinel-2 images conjointly used to obtain a high-density time series of LAI information to model within-field yield variability.

scholarly journals Modeling the monthly mean soil-water balance with a statistical-dynamical ecohydrology model as coupled to a two-component canopy model

2008 ◽  
Vol 5 (2) ◽  
pp. 579-648
Author(s):  
J. P. Kochendorfer ◽  
J. A. Ramírez
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Water Balance ◽  
Soil Water ◽  
Soil Texture ◽  
Root Zone ◽  
Soil Water Balance ◽  
Area Index ◽  
Two Component ◽  
Canopy Model

Abstract. The statistical-dynamical annual water balance model of Eagleson (1978) is a pioneering work in the analysis of climate, soil and vegetation interactions. This paper describes several enhancements and modifications to the model that improve its physical realism at the expense of its mathematical elegance and analytical tractability. In particular, the analytical solutions for the root zone fluxes are re-derived using separate potential rates of transpiration and bare-soil evaporation. Those potential rates, along with the rate of evaporation from canopy interception, are calculated using the two-component Shuttleworth-Wallace (1985) canopy model. In addition, the soil column is divided into two layers, with the upper layer representing the dynamic root zone. The resulting ability to account for changes in root-zone water storage allows for implementation at the monthly timescale. This new version of the Eagleson model is coined the Statistical-Dynamical Ecohydrology Model (SDEM). The ability of the SDEM to capture the seasonal dynamics of the local-scale soil-water balance is demonstrated for two grassland sites in the US Great Plains. Sensitivity of the results to variations in peak green Leaf Area Index (LAI) suggests that the mean peak green LAI is determined by some minimum in root zone soil moisture during the growing season. That minimum appears to be close to the soil matric potential at which the dominant grass species begins to experience water stress and well above the wilting point, thereby suggesting an ecological optimality hypothesis in which the need to avoid water-stress-induced leaf abscission is balanced by the maximization of carbon assimilation (and associated transpiration). Finally, analysis of the sensitivity of model-determined peak green LAI to soil texture shows that the coupled model is able to reproduce the so-called "inverse texture effect", which consists of the observation that natural vegetation in dry climates tends to be most productive in sandier soils despite their lower water holding capacity. Although the determination of LAI based on near-complete utilization of soil moisture is not a new approach in ecohydrology, this paper demonstrates its use for the first time with a new monthly statistical-dynamical model of the water balance. Accordingly, the SDEM provides a new framework for studying the controls of soil texture and climate on vegetation density and evapotranspiration.

Citrus Irrigation Management

EDIS ◽  
2017 ◽  
Vol 2017 (5) ◽  
Author(s):  
Davie Mayeso Kadyampakeni ◽  
Kelly T. Morgan ◽  
Mongi Zekri ◽  
Rhuanito Ferrarezi ◽  
Arnold Schumann ◽  
...  
Keyword(s):  
Water Stress ◽  
Physiological Activity ◽  
Irrigation Management ◽  
Fruit Size ◽  
Irrigation Scheduling ◽  
Leaf Expansion ◽  
Tree Canopy ◽  
Limiting Factor ◽  
Irrigation Frequency ◽  
Citrus Production

Water is a limiting factor in Florida citrus production during the majority of the year because of the low water holding capacity of sandy soils resulting from low clay and the non-uniform distribution of the rainfall. In Florida, the major portion of rainfall comes in June through September. However, rainfall is scarce during the dry period from February through May, which coincides with the critical stages of bloom, leaf expansion, fruit set, and fruit enlargement. Irrigation is practiced to provide water when rainfall is not sufficient or timely to meet water needs. Proper irrigation scheduling is the application of water to crops only when needed and only in the amounts needed; that is, determining when to irrigate and how much water to apply. With proper irrigation scheduling, yield will not be limited by water stress. With citrus greening (HLB), irrigation scheduling is becoming more important and critical and growers cannot afford water stress or water excess. Any degree of water stress or imbalance can produce a deleterious change in physiological activity of growth and production of citrus trees.  The number of fruit, fruit size, and tree canopy are reduced and premature fruit drop is increased with water stress.  Extension growth in shoots and roots and leaf expansion are all negatively impacted by water stress. Other benefits of proper irrigation scheduling include reduced loss of nutrients from leaching as a result of excess water applications and reduced pollution of groundwater or surface waters from the leaching of nutrients. Recent studies have shown that for HLB-affected trees, irrigation frequency should increase and irrigation amounts should decrease to minimize water stress from drought stress or water excess, while ensuring optimal water availability in the rootzone at all times.

scholarly journals The effect of colonization dynamics in competition for space in metacommunities

2021 ◽  
Author(s):  
Jorge Arroyo-Esquivel ◽  
Nathan G. Marculis ◽  
Alan Hastings
Keyword(s):  
Continuous Time ◽  
Habitat Suitability ◽  
Limiting Factor ◽  
Modeling Framework ◽  
Competition For Space ◽  
Time Modeling ◽  
Functional Forms ◽  
Colonization Dynamics ◽  
Main Factors ◽  
Metacommunity Dynamics

AbstractOne of the main factors that determines habitat suitability for sessile and territorial organisms is the presence or absence of another competing individual in that habitat. This type of competition arises in populations occupying patches in a metacommunity. Previous studies have looked at this process using a continuous-time modeling framework, where colonizations and extinctions occur simultaneously. However, different colonization processes may be performed by different species, which may affect the metacommunity dynamics. We address this issue by developing a discrete-time framework that describes these kinds of metacommunity interactions, and we consider different colonization dynamics. To understand potential dynamics, we consider specific functional forms that characterize the colonization and extinction processes of metapopulations competing for space as their limiting factor. We then provide a mathematical analysis of the models generated by this framework, and we compare these results to what is seen in nature and in previous models.

scholarly journals Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

2021 ◽  
Vol 13 (1) ◽  
pp. 432
Author(s):  
Aru Han ◽  
Song Qing ◽  
Yongbin Bao ◽  
Li Na ◽  
Yuhai Bao ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Change ◽  
Fire Severity ◽  
Dynamic Change ◽  
Burned Area ◽  
Fire Event ◽  
Vegetation Dynamic ◽  
Area Index ◽  
Sentinel 2

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude

2021 ◽  
pp. 1-14
Author(s):  
L. S. Sampaio ◽  
R. Battisti ◽  
M. A. Lana ◽  
K. J. Boote
Keyword(s):  
Management Practices ◽  
Field Experiments ◽  
Plant Density ◽  
Crop Model ◽  
Management Scenarios ◽  
Low Latitude ◽  
Area Index ◽  
Sowing Dates ◽  
Plant Densities ◽  
Maturity Groups

Abstract Crop models can be used to explain yield variations associated with management practices, environment and genotype. This study aimed to assess the effect of plant densities using CSM-CROPGRO-Soybean for low latitudes. The crop model was calibrated and evaluated using data from field experiments, including plant densities (10, 20, 30 and 40 plants per m2), maturity groups (MG 7.7 and 8.8) and sowing dates (calibration: 06 Jan., 19 Jan., 16 Feb. 2018; and evaluation: 19 Jan. 2019). The model simulated phenology with a bias lower than 2 days for calibration and 7 days for evaluation. Relative root mean square error for the maximum leaf area index varied from 12.2 to 31.3%; while that for grain yield varied between 3 and 32%. The calibrated model was used to simulate different management scenarios across six sites located in the low latitude, considering 33 growing seasons. Simulations showed a higher yield for 40 pl per m2, as expected, but with greater yield gain increments occurring at low plant density going from 10 to 20 pl per m2. In Santarém, Brazil, MG 8.8 sown on 21 Feb. had a median yield of 2658, 3197, 3442 and 3583 kg/ha, respectively, for 10, 20, 30 and 40 pl per m2, resulting in a relative increase of 20, 8 and 4% for each additional 10 pl per m2. Overall, the crop model had adequate performance, indicating a minimum recommended plant density of 20 pl per m2, while sowing dates and maturity groups showed different yield level and pattern across sites in function of the local climate.

STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region

2021 ◽  
Vol 41 (4) ◽  
Author(s):  
Dominique Courault ◽  
Laure Hossard ◽  
Valérie Demarez ◽  
Hélène Dechatre ◽  
Kamran Irfan ◽  
...  
Keyword(s):  
Crop Model ◽  
Growth And Yield ◽  
Rice Growth ◽  
Sentinel 2

scholarly journals A framework for quantifying hydrologic effects of soil structure across scales

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Sara Bonetti ◽  
Zhongwang Wei ◽  
Dani Or
Keyword(s):  
Soil Texture ◽  
Land Surface ◽  
Soil Structure ◽  
Pedotransfer Functions ◽  
Small Scale ◽  
Spatial Correlations ◽  
Surface Parameterization ◽  
Exchange Processes ◽  
Local Soil ◽  
Spatially Distributed

AbstractEarth system models use soil information to parameterize hard-to-measure soil hydraulic properties based on pedotransfer functions. However, current parameterizations rely on sample-scale information which often does not account for biologically-promoted soil structure and heterogeneities in natural landscapes, which may significantly alter infiltration-runoff and other exchange processes at larger scales. Here we propose a systematic framework to incorporate soil structure corrections into pedotransfer functions, informed by remote-sensing vegetation metrics and local soil texture, and use numerical simulations to investigate their effects on spatially distributed and areal averaged infiltration-runoff partitioning. We demonstrate that small scale soil structure features prominently alter the hydrologic response emerging at larger scales and that upscaled parameterizations must consider spatial correlations between vegetation and soil texture. The proposed framework allows the incorporation of hydrological effects of soil structure with appropriate scale considerations into contemporary pedotransfer functions used for land surface parameterization.

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