Integrated modelling of crop production and nitrate leaching with the Daisy model

An integrated modelling strategy was designed and applied to the Soil-Vegetation-Atmosphere Transfer model Daisy for simulation of crop production and nitrate leaching under pedo-climatic and agronomic environment different than that of model original parameterisation. The points of significance and caution in the strategy are: • Model preparation should include field data in detail due to the high complexity of the soil and the crop processes simulated with process-based model, and should reflect the study objectives. Inclusion of interactions between parameters in a sensitivity analysis results in better account for impacts on outputs of measured variables. • Model evaluation on several independent data sets increases robustness, at least on coarser time scales such as month or year. It produces a valuable platform for adaptation of the model to new crops or for the improvement of the existing parameters set. On daily time scale, validation for highly dynamic variables such as soil water transport remains challenging. • Model application is demonstrated with relevance for scientists and regional managers. The integrated modelling strategy is applicable for other process-based models similar to Daisy. It is envisaged that the strategy establishes model capability as a useful research/decision-making, and it increases knowledge transferability, reproducibility and traceability

Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors

The two-source energy balance model estimates canopy transpiration (Tr) and soil evaporation (E) traditionally from satellite partitions of remotely sensed land surface temperature (LST) and the Priestley-Taylor equation (TSEB-PT) at seasonal time with limited accuracy. The high spatial–temporal resolution spectral data collected by unmanned aerial vehicles (UAVs) provide valuable opportunity to estimate Tr and E precisely, improve the understanding of the seasonal and the diurnal cycle of evapotranspiration (ET), and timely detect agricultural drought. The UAV data vary in spatial resolution and the uncertainty imposed on the TSEB-PT outcome has thus far not being considered. To address these challenges and prospects, a new energy flux modelling framework based on TSEB-PT for high spatial resolution thermal and multispectral UAV data is proposed in this paper. Diurnal variations of LST in agricultural fields were recorded with a thermal infrared camera installed on an UAV during drought in 2018 and 2019. Observing potato as a test crop, LST, plant biophysical parameters derived from corresponding UAV multispectral data, and meteorological forcing variables were employed as input variables to TSEB-PT. All analyses were conducted at different pixelation of the UAV data to quantify the effect of spatial resolution on the performance. The 1 m spatial resolution produced the highest correlation between Tr modelled by TSEB-PT and measured by sap flow sensors (R2 = 0.80), which was comparable to the 0.06, 0.1, 0.5 and 2 m pixel sizes (R2 = 0.76–0.78) and markedly higher than the lower resolutions of 2 to 24 m (R2 = 0.30–0.72). Modelled Tr was highly and significantly correlated with measured leaf water potential (R2 = 0.81) and stomatal conductance (R2 = 0.74). The computed irrigation requirements (IRs) reflected the field irrigation treatments, ET and conventional irrigation practices in the area with high accuracy. It was also found that using a net primary production model with explicit representation of temperature influences made it possible to distinguish effects of drought vis-a-vis heat stress on crop productivity and water use efficiency. The results showed excellent model performance for retrieving Tr and ET dynamics under drought stress and proved that the proposed remote sensing based TSEB-PT framework at UAV scale is a promising tool for the investigation of plant drought stress and water demand; this is particularly relevant for local and regional irrigations scheduling.info:eu-repo/semantics/publishedVersio

Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors

The two-source energy balance model estimates canopy transpiration (Tr) and soil evaporation (E) traditionally from satellite partitions of remotely sensed land surface temperature (LST) and the Priestley-Taylor equation (TSEB-PT) at seasonal time with limited accuracy. The high spatial-temporal resolution spectral data collected by unmanned aerial vehicles (UAVs) provide valuable opportunity to estimate Tr and E precisely, improve the understanding of the seasonal and the diurnal cycle of evapotranspiration (ET), and timely detect agricultural drought. The UAV data vary in spatial resolution and the uncertainty imposed on the TSEB-PT outcome has thus far not being considered. To address these challenges and prospects, a new energy flux modelling framework based on TSEB-PT for high spatial resolution thermal and multispectral UAV data is proposed in this paper. Diurnal variations of LST in agricultural fields were recorded with a thermal infrared camera installed on an UAV during drought in 2018 and 2019. Observing potato as a test crop, LST, plant biophysical parameters derived from corresponding UAV multispectral data, and meteorological forcing variables were employed as input variables to TSEB-PT. All analyses were conducted at different pixelation of the UAV data to quantify the effect of spatial resolution on the performance. The 1 m spatial resolution produced the highest correlation between Tr modelled by TSEB-PT and measured by sap flow sensors (R2 = 0.80), which was comparable to the 0.06, 0.1, 0.5 and 2 m pixel sizes (R2 = 0.76-0.78) and markedly higher than the lower resolutions of 2 to 24 m (R2 = 0.30-0.72). Modelled Tr was highly and significantly correlated with measured leaf water potential (R2 = 0.81) and stomatal conductance (R2 = 0.74). The computed irrigation requirements (IRs) reflected the field irrigation treatments, ET and conventional irrigation practices in the area with high accuracy. It was also found that using a net primary production model with explicit representation of temperature influences made it possible to distinguish effects of drought vis-a-vis heat stress on crop productivity and water use efficiency. The results showed excellent model performance for retrieving Tr and ET dynamics under drought stress and proved that the proposed remote sensing based TSEB-PT framework at UAV scale is a promising tool for the investigation of plant drought stress and water demand; this is particularly relevant for local and regional irrigations scheduling

Heavy metal soil contamination detection using combined geochemistry and field spectroradiometry in the United Kingdom

Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field- and lab-based spectra from contaminated soils by using ASD FieldSpec® 3, where the spectrum varies between 350 and 2500 nm; (ii) build field- and lab-based spectral libraries; (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer; (iv) identify the specific spectral regions associated to the modelling of HMSC; and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field- and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations

Spatiotemporal Winter Wheat Water Status Assessment Improvement Using a Water Deficit Index Derived from an Unmanned Aerial System in the North China Plain

Agricultural droughts cause a great reduction in winter wheat productivity; therefore, timely and precise irrigation recommendations are needed to alleviate the impact. This study aims to assess drought stress in winter wheat with the use of an unmanned aerial system (UAS) with multispectral and thermal sensors. High-resolution Water Deficit Index (WDI) maps were derived to assess crop drought stress and evaluate winter wheat actual evapotranspiration rate (ETa). However, the estimation of WDI needs to be improved by using more appropriate vegetation indices as a proximate of the fraction of vegetation cover. The experiments involved six irrigation levels of winter wheat in the harvest years 2019 and 2020 at Luancheng, North China Plain on seasonal and diurnal timescales. Additionally, WDI derived from several vegetation indices (VIs) were compared: near-infrared-, red edge-, and RGB-based. The WDIs derived from different VIs were highly correlated with each other and had similar performances. The WDI had a consistently high correlation to stomatal conductance during the whole season (R2 between 0.63–0.99) and the correlation was the highest in the middle of the growing season. On the contrary, the correlation between WDI and leaf water potential increased as the season progressed with R2 up to 0.99. Additionally, WDI and ETa had a strong connection to soil water status with R2 up to 0.93 to the fraction of transpirable soil water and 0.94 to the soil water change at 2 m depth at the hourly rate. The results indicated that WDI derived from multispectral and thermal sensors was a reliable factor in assessing the water status of the crop for irrigation scheduling

A Framework for the Heterogeneity and Ecosystem Services of Farmland Landscapes: An Integrative Review

It is essential for the sustainable development of farmland landscapes to balance ecosystem service trade-offs and improve resource use efficiency during crop production. Thus, an integrative and concept-centric qualitative approach was applied by combining the patch–corridor–matrix model of landscape ecology and the crop layout theory of farming systems into a theoretical framework. The thesis concludes that a farmland landscape comprises three compositions: the crop (the main crop and the service crop), the non-crop, and the non-vegetation, leading to heterogeneous composition and configuration. The main crop, typically displayed as large patches with a high distribution ratio, provides most of the provisioning services, while the service crop performs many regulation services. The non-crop and non-vegetation compositions often appear as strips that can connect different patches as corridors and support the provisioning services of crops. Non-crop compositions mainly focus on support and regulation services, while non-vegetation compositions support farming operations. Further research is needed in several respects, including the ecological impact and ecosystem service trade-offs of the composition and configuration heterogeneity, and strategies for the adoption of cropping systems and agronomic measures at the landscape scale, which are essential to the evaluation, improvement, and redesign of farmland landscapes