From the field-scale Aquacrop model to a regional gridded crop model: initial evaluation over Europe

2020 ◽  
Author(s):  
Shannon de Roos ◽  
Gabriëlle de Lannoy ◽  
Dirk Raes
Keyword(s):  
Soil Moisture ◽  
Crop Production ◽  
Cropping Systems ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Crop Models ◽  
Soil Database ◽  
Aquacrop Model ◽  
Spatially Distributed

<p>The pressure on soil and water resources to support the demand for crop production calls for effective water management at the regional scale and a need for regional crop models.</p><p>In our study, the field-based Aquacrop v.6.1 is modified to a gridded crop model that is run spatially over the main part of Europe at 1-km resolution.</p><p>The gridded model simulates spatially distributed soil moisture, crop biomass and yield, given spatial input of meteorological forcings extracted from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and 1-km soil texture information from the Harmonized World Soil Database v1.2 (HWSD v1.2). For the first model evaluation, a hypothetical and uniform crop is implemented, and field management and irrigation practices are not included. We will present preliminary results over Europe by comparing the spatial soil moisture and biomass simulations with remote sensing data.</p><p>This work is part of the SHui project, a H2020 project that aims at improving stakeholder decision-making for water scarcity management in European and Chinese cropping systems.</p>

Using a regional version of the AquaCrop model to simulate crop biomass and soil moisture: an evaluation with remote sensing data products

2021 ◽  
Author(s):  
Shannon de Roos ◽  
Gabrielle De Lannoy ◽  
Dirk Raes
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Management Practices ◽  
Cropping Systems ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Biomass Productivity ◽  
Climatic Conditions ◽  
Aquacrop Model ◽  
Crop Biomass

<p>A shift to more sustainable land cultivation practices is necessary to meet the future crop demand, which faces a vastly growing population and changing climatic conditions. To assess which management practices can be effectively applied at a regional scale, good spatial monitoring techniques are required. With a regional version of the AquaCrop model v6.1, we simulate crop biomass production and soil moisture at a 1-km resolution over Europe. Biomass productivity is compared against the Dry Matter Productivity of the Copernicus Global Land Service, derived from optical satellite sensors, while surface moisture content is evaluated with Sentinel-1 and SMAP microwave satellite retrieval products and inter-compared with in situ data. We show that the AquaCrop model can successfully be applied at a relatively fine resolution over a large scale, using global input data.</p><p>This research is part of a H2020 project, named SHui. SHui is a collaborative effort between Universities from Europe and China, with the overall aim of managing water scarcity in cropping systems for individuals as well as stakeholder organizations.</p>

Relative cost to soil fertility of long-term crop production without fertilization

1996 ◽  
Vol 76 (3) ◽  
pp. 401-406 ◽  
Author(s):  
C. A. Campbell ◽  
F. Selles ◽  
J. T. Harapiak ◽  
G. P. Lafond
Keyword(s):  
Soil Moisture ◽  
Soil Fertility ◽  
Green Manure ◽  
Soil Degradation ◽  
Crop Production ◽  
Cropping Systems ◽  
Triticum Aestivum L ◽  
Growth Chamber ◽  
Rotation Phase ◽  
N Fertility

An earlier analysis of yield trends of stubble-wheat in six cropping systems, over 35 yr, in a thin Black Chernozemic soil at Indian Head, Saskatchewan, showed that fertilizer improved soil quality, while absence of fertilizer, combined with frequent fallowing, led to soil degradation. The inclusion of a legume green manure crop in the rotation failed to maintain soil fertility, apparently because legumes do not supply P. Because the fertility and stored moisture effects were confounded, we conducted a growth chamber experiment to quantify soil responses to N and P in these six cropping systems. Soil from the top 15-cm of the rotation phase that had just grown two successive wheat (Triticum aestivum L.) crops was used. Various factorial combinations of ammonium nitrate-N and triple superphosphate-P were applied at N/P2O5 rates up to 200/200 kg ha−1. Soil moisture was maintained in the available range. Regression analysis showed that the fallow-wheat-wheat (F-W-W) and continuous wheat (Cont W) systems that had not been fertilized in 35 yr, and which had moderate amounts of NaHCO3-P, only responded to N. In contrast, the green manure (GM)- and hay (H)- containing systems, which had also not been fertilized before had low levels of NaHCO3-P and responded to both N and P. In the field, the yields of wheat grown on stubble in 1991 rated: Cont W (N + P) > F-W-W (N + P) > F-W-W-H-H-H > Cont W > GM-W-W > F-W-W. However, in the growth chamber the rating was: Cont W (N + P) > F-W-W-H-H-H > GM-W-W > Cont W > F-W-W (N + P) > F-W-W. We suggest that the growth chamber results more accurately reflect the present fertility status of these soils, because fertility is no longer confounded with soil moisture. Grain yields in the growth chamber were directly proportional to the previously measured initial potential rate of N mineralization, indicating the value of the latter parameter as a useful index of soil N fertility. Key words: Nitrogen, phosphorus, soil degradation, legumes, fertilizers

scholarly journals A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

2020 ◽  
Vol 12 (3) ◽  
pp. 455 ◽  
Author(s):  
Yaokui Cui ◽  
Xi Chen ◽  
Wentao Xiong ◽  
Lian He ◽  
Feng Lv ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
The Tibetan Plateau ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

Improving Dryland Crop Simulation by Assimilating Soil Moisture and Vegetation Data

2020 ◽  
Author(s):  
Yang Lu ◽  
Justin Sheffield
Keyword(s):  
Soil Moisture ◽  
Crop Production ◽  
Management Practices ◽  
Water Productivity ◽  
Vegetation Indices ◽  
Screening Method ◽  
Model Performance ◽  
State Parameter ◽  
Aquacrop Model

<p>Global population is projected to keep increasing rapidly in the next 3 decades, particularly in dryland regions of the developing world, making it a global imperative to enhance crop production. However, improving current crop production in these regions is hampered by yield gaps due to poor soils, lack of irrigation and other management practices. Here we develop a crop modelling capability to help understand gaps, and apply to dryland regions where data for parametrizing and testing models is generally lacking. We present a data assimilation framework to improve simulation capability by assimilating in-situ soil moisture and vegetation data into the FAO AquaCrop model. AquaCrop is a water-driven model that simulates canopy growth, biomass and crop yield as a function of water productivity. The key strength of AquaCrop lies in the low requirement for input data thanks to its simple structure. A global sensitivity analysis is first performed using the Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method to identify the key influential parameters on the model outputs. We begin with state-only updates by assimilating different combinations of soil moisture and vegetation data (vegetation indices, biomass, etc.), and different filtering/smoothing assimilation strategies are tested. Based on the state-only assimilation results, we further evaluate the utility of joint state-parameter (augmented-states) assimilation in improving the model performance. The framework will eventually be extended to assimilate remote sensing estimates of soil moisture and vegetation data to overcome the lack of in-situ data more generally in dryland regions.</p>

scholarly journals Use of microwave remote sensing data to monitor spatio temporal characteristics of surface soil moisture at local and regional scales

2005 ◽  
Vol 5 ◽  
pp. 49-56 ◽  
Author(s):  
A. Löw ◽  
R. Ludwig ◽  
W. Mauser
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Electromagnetic Waves ◽  
Regional Scale ◽  
Soil Layer ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Electromagnetic Spectrum ◽  
Envisat Asar ◽  
Spatio Temporal

Abstract. Hydrologic processes, such as runoff production or evapotranspiration, largely depend on the variation of soil moisture and its spatial pattern. The interaction of electromagnetic waves with the land surface can be dependant on the water content of the uppermost soil layer. Especially in the microwave domain of the electromagnetic spectrum, this is the case. New sensors as e.g. ENVISAT ASAR, allow for frequent, synoptically and homogeneous image acquisitions over larger areas. Parameter inversion models are therefore developed to derive bio- and geophysical parameters from the image products. The paper presents a soil moisture inversion model for ENVISAT ASAR data for local and regional scale applications. The model is validated against in situ soil moisture measurements. The various sources of uncertainties, being related to the inversion process are assessed and quantified.

scholarly journals Water Use Effectiveness Is Enhanced Using Film Mulch Through Increasing Transpiration and Decreasing Evapotranspiration

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1153 ◽  
Author(s):  
Qianxi Shen ◽  
Risheng Ding ◽  
Taisheng Du ◽  
Ling Tong ◽  
Sien Li
Keyword(s):  
Water Use ◽  
Crop Production ◽  
Northwest China ◽  
Crop Growth ◽  
Soil Evaporation ◽  
Growth And Yield ◽  
Plastic Film ◽  
Evaporation Coefficient ◽  
Crop Models ◽  
Aquacrop Model

Water shortage is a main limitation of crop growth and yield in drought northwest China, which is an important area of seed maize growth. Plastic film mulch is widely adopted to reduce soil evaporation (E) and conserve water resources, which changes evapotranspiration (ET) and its components, E and transpiration (Tr) and crop growth. The AquaCrop model, one of widely used crop models powered by water, can well simulate crop ET components and growth. However, there are few studies that examine ET partitioning and growth with and without plastic film mulch. The calibrated AquaCrop model was used to partition ET and simulate growth of seed maize with and without plastic film mulch in a drought region of northwest China in 2014 and 2015. The AquaCrop model can well simulate canopy cover curve (CC), and the dynamic and accumulated courses of ET and ET components. Plastic film mulch could advance the growth stage of seed maize and reduce seasoned ET. The initial stage with plastic film mulch was 37–42 days, while it was 46–48 days for no-mulch. Plastic film mulch increased Tr by 14.16% and 14.48% and significantly decreased E by 57.25% and 34.28% in 2014 and 2015, respectively, resulting in the reduction of seasonal total ET. Plastic film mulch increased averaged mid-season crop coefficient for transpiration (Kc Tr) by 0.88% and decreased soil evaporation coefficient (Ke) by 62.50%. Collectively, the results suggest that, in comparison with no-mulch, plastic film mulch advanced crop growth, and decreased total ET and increased Tr related with crop production, i.e., improve water use effectiveness.

A data-driven approach for mapping global surface soil moisture at 100 m using high-resolution remote sensing data and land surface parameters

2021 ◽  
Author(s):  
Jingyi Huang ◽  
Ankur Desai ◽  
Jun Zhu ◽  
Alfred Hartemink ◽  
Paul Stoy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Field Scale ◽  
Land Surface Parameters ◽  
Global Surface

<p>Current in situ soil moisture monitoring networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical global surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100m and performed moderately well under cultivated, herbaceous, forest, and shrub soils (R<sup>2</sup> = 0.524, RMSE = 0.07 m<sup>3</sup> m<sup>−3</sup>). It has a relatively good transferability at the regional scale among different continental and regional networks (mean RMSE = 0.08–0.10 m<sup>3</sup> m<sup>−3</sup>). The global model was then applied to map SSM dynamics at 30–100m across a field-scale network (TERENO-Wüstebach) in Germany and an 80-ha irrigated cropland in Wisconsin, USA. Without local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was also affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and high-resolution soil maps as well as assimilation with process-based water flow models.</p>

Quantification of Yield, Water, and Carbon budget at intra-field scale using the AgriCarbon-EO tool

2021 ◽  
Author(s):  
Ahmad Al Bitar ◽  
Taeken Wijmer ◽  
Ludovic Arnaud ◽  
Remy Fieuzal ◽  
Gaetan Pique ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Water Budget ◽  
Carbon Budget ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Transfer Model ◽  
Sensing Data ◽  
The Impact

<p>Achieving the United Nations Sustainable Development Goal 2 that addresses food security and sustainable agriculture requires the promotion of readily transferable and scalable agronomical solutions. The combination of high-resolution remote sensing data, field information, and physical models is identified as a robust way of answering this requirement.  Here, we present the AgriCarbon-EO tool, a decision support system that provides the yield, biomass, water and carbon budget components of agricultural fields at a 10m resolution and at a regional scale. The tool assimilates high resolution optical remote sensing data from Copernicus Sentinel-2 satellites into a  radiative transfer model and a crop model. First, the application of a spatial Bayesian retrieval approach to the PROSAIL radiative transfer model provides Leaf Area Index (LAI) with its associated uncertainty. Second, LAI is assimilated into the SAFYE-CO2 crop model using a temporal Bayesian retrieval that enables the calculation of the yield, biomass, carbon and water budgets components with their associated uncertainties. In addition to remote sensing data, input datasets of crop types, weather and soil data are used to constrain the system. The concise weather data is provided from local weather stations or weather forecasts and is used to force the crop model (SAFYE-CO2) dynamics. The soil data are used in two folds. First to better parametrize the soil emissions in the radiative model retrievals and second to parametrise the water infiltration in the soil module of the crop model. The AgriCarbon-EO tool has been optimized to enable the computation of the yield, carbon, and water budget at high spatial resolution (10m) and large scale (100km2). The model is applied over the South-West of France covered by 3 Sentinel-2 tiles for major crops (wheat, maize,  sunflower). The outputs are validated over experimental plots for biomass, yield, soil moisture, and CO2 fluxes located all in the South-West of France. The experimental sites include the FR-AUR and FR-LAM ICOS sites and 22 cropland fields (biomass sampling). The validation exercise is done for the 2017-2018 and 2019-2020 cultural years. We show the added value of the use of high resolution in driving the crop model to take into account the impact of complex processes that are embedded in the LAI signal like vegetation water stress, disease, and agricultural practices. We show that the system is capable of providing the yield, carbon, and water budget of major crops accurately.  At the regional scale, we give global estimates of the carbon budget, water needs, and yields per crop type. We present the impact of intra-plot heterogeneity in the estimation of yield and the annual carbon and water budget showing the added value for high-resolution intra-plot modeling.</p>

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