Mapping Optimum Nitrogen Crop Uptake

2017 ◽  
Vol 8 (2) ◽  
pp. 322-327
Author(s):  
J. Villodre ◽  
I. Campos ◽  
H. Lopez-Corcoles ◽  
J. Gonzalez-Piqueras ◽  
L. González ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Nitrogen Concentration ◽  
Field Measurements ◽  
Growing Season ◽  
Crop Growth Model ◽  
Crop Uptake ◽  
Use Efficiency ◽  
Critical Nitrogen Concentration ◽  
Nitrogen Treatments ◽  
Crop Biomass

This work proposes a methodology that uses remote sensing (RS) images to obtain optimum nitrogen crop uptake (Nuptake) maps, for the all pixels in the image included in the field during the entire growing season. The Nuptake was determined from relationship between critical nitrogen concentration (Nc) and biomass where biomass was estimated by a crop growth model based on the water use efficiency. The paper proposes the use of this methodology in commercial wheat farm. The results are discussed with respect to field measurements of crop biomass and N concentration on different dates and in zones with different nitrogen treatments from 8 commercial wheat farms in Albacete, Spain during 2015 and 2016.

scholarly journals LINKING PHOTOSYNTHETIC LIGHT USE EFFICIENCY AND OPTICAL VEGETATION ACTIVE INDICATORS: IMPLICATIONS FOR GROSS PRIMARY PRODUCTION ESTIMATION BY REMOTE SENSING

2020 ◽  
Vol V-3-2020 ◽  
pp. 571-578
Author(s):  
S. Wang ◽  
Z. Li ◽  
Y. Zhang ◽  
D. Yang ◽  
C. Ni
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Seasonal Changes ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Canopy Structure ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Light Use Efficiency ◽  
Use Efficiency

Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.

scholarly journals Aerial Biomass and Nitrogen Estimation of Emerged and Floating Macrophytes using Optical Remote Sensing: A Non-Destructive Method

2021 ◽  
Author(s):  
Jessica Cristina Carvalho Medeiros ◽  
Maurício Perine ◽  
Marcelo Pompêo ◽  
Marisa Bitencourt
Keyword(s):  
Remote Sensing ◽  
Aquatic Plants ◽  
Spectral Response ◽  
Nitrogen Concentration ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Pistia Stratiotes ◽  
Optical Remote Sensing ◽  
Eichhornia Azurea ◽  
Sentinel 2

Abstract Freshwater resources faces threats with aquatic plants invasion, considered biological pollution with deep effects on water quality and nutrients cycling due to their rapid growth. Orbital remote sensing has been an effective instrument of monitoring large water bodies. Thus, the aim of this study was to analyze the relation between reflectance and field measurements (biomass and nitrogen concentration) of aquatic plants to develop estimation equations and to test vegetation indices to use in orbital remote sensing. The most common tropical infesting species (Salvinia auriculata, Pistia stratiotes, Eichhornia crassipes and Eichhornia azurea) were collected during a year, measured their spectral response to simulate satellite bands, and the biomass and nitrogen concentration measurements. The bands intervals of Sentinel-2 satellite were choosing to the simulation due to their narrow bands and the RedEdge new band. The obtained field data were correlated with the reflectance obtained from spectroradiometry of each species and the equations showed R² = 0.64 to estimate biomass and R² = 0.60 to estimate nitrogen using the entire spectrum. Several indices described in the literature were tested with different Sentinel-2 bands but with no significant results. The NDVI index showed a separation among species using RedEdge band and can be used to identify the species, but not to estimate their biomass.

scholarly journals Remote Sensing of Ecosystem Water Use Efficiency: A Review of Direct and Indirect Estimation Methods

2021 ◽  
Vol 13 (12) ◽  
pp. 2393
Author(s):  
Wanyuan Cai ◽  
Sana Ullah ◽  
Lei Yan ◽  
Yi Lin
Keyword(s):  
Remote Sensing ◽  
Water Use Efficiency ◽  
Analytical Model ◽  
Water Use ◽  
Estimation Methods ◽  
Retrieval Algorithm ◽  
Canopy Conductance ◽  
Coupling Mechanism ◽  
Indirect Methods ◽  
Use Efficiency

Water use efficiency (WUE) is a key index for understanding the ecosystem of carbon–water coupling. The undistinguishable carbon–water coupling mechanism and uncertainties of indirect methods by remote sensing products and process models render challenges for WUE remote sensing. In this paper, current progress in direct and indirect methods of WUE estimation by remote sensing is reviewed. Indirect methods based on gross primary production (GPP)/evapotranspiration (ET) from ground observation, processed models and remote sensing are the main ways to estimate WUE in which carbon and water cycles are independent processes. Various empirical models based on meteorological variables and remote sensed vegetation indices to estimate WUE proved the ability of remotely sensed data for WUE estimating. The analytical model provides a mechanistic opportunity for WUE estimation on an ecosystem scale, while the hypothesis has yet to be validated and applied for the shorter time scales. An optimized response of canopy conductance to atmospheric vapor pressure deficit (VPD) in an analytical model inverted from the conductance model has been also challenged. Partitioning transpiration (T) and evaporation (E) is a more complex phenomenon than that stated in the analytic model and needs a more precise remote sensing retrieval algorithm as well as ground validation, which is an opportunity for remote sensing to extrapolate WUE estimation from sites to a regional scale. Although studies on controlling the mechanism of environmental factors have provided an opportunity to improve WUE remote sensing, the mismatch in the spatial and temporal resolution of meteorological products and remote sensing data, as well as the uncertainty of meteorological reanalysis data, add further challenges. Therefore, improving the remote sensing-based methods of GPP and ET, developing high-quality meteorological forcing datasets and building mechanistic remote sensing models directly acting on carbon–water cycle coupling are possible ways to improve WUE remote sensing. Improvement in direct WUE remote sensing methods or remote sensing-driven ecosystem analysis methods can promote a better understanding of the global ecosystem carbon–water coupling mechanisms and vegetation functions–climate feedbacks to serve for the future global carbon neutrality.

scholarly journals Drone-Based Remote Sensing for Research on Wind Erosion in Drylands: Possible Applications

2021 ◽  
Vol 13 (2) ◽  
pp. 283
Author(s):  
Junzhe Zhang ◽  
Wei Guo ◽  
Bo Zhou ◽  
Gregory S. Okin
Keyword(s):  
Remote Sensing ◽  
Wind Erosion ◽  
Field Measurements ◽  
Spatially Explicit ◽  
Surface Model ◽  
High Resolution Imagery ◽  
Soil Elevation ◽  
3D Photogrammetry ◽  
Using Data ◽  
Sample Set

With rapid innovations in drone, camera, and 3D photogrammetry, drone-based remote sensing can accurately and efficiently provide ultra-high resolution imagery and digital surface model (DSM) at a landscape scale. Several studies have been conducted using drone-based remote sensing to quantitatively assess the impacts of wind erosion on the vegetation communities and landforms in drylands. In this study, first, five difficulties in conducting wind erosion research through data collection from fieldwork are summarized: insufficient samples, spatial displacement with auxiliary datasets, missing volumetric information, a unidirectional view, and spatially inexplicit input. Then, five possible applications—to provide a reliable and valid sample set, to mitigate the spatial offset, to monitor soil elevation change, to evaluate the directional property of land cover, and to make spatially explicit input for ecological models—of drone-based remote sensing products are suggested. To sum up, drone-based remote sensing has become a useful method to research wind erosion in drylands, and can solve the issues caused by using data collected from fieldwork. For wind erosion research in drylands, we suggest that a drone-based remote sensing product should be used as a complement to field measurements.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

EFFECT OF CULTIVATION AND NITROGEN ON FRUIT QUALITY, YIELD AND COLOR OF McINTOSH APPLES GROWN IN IRRIGATED GRASS SOD COVER CROP

1969 ◽  
Vol 49 (2) ◽  
pp. 149-154 ◽  
Author(s):  
J. L. Mason
Keyword(s):  
Nitrogen Fertilization ◽  
Cover Crop ◽  
Nitrogen Concentration ◽  
Titratable Acidity ◽  
Fruit Color ◽  
Soluble Solids ◽  
Apple Trees ◽  
Pressure Test ◽  
Nitrogen Treatments ◽  
Plot Design

Nitrogen treatments from 0.15 to 0.90 kg of nitrogen and cultivation treatments from zero to three shallow rotovations were applied in a split-plot design to 30-year-old McIntosh apple trees growing in irrigated grass sod.Fruit quality was very largely unaffected by the treatments. Pressure test after harvest was reduced from 6.61 to 6.44 kg (P = 0.10) as nitrogen increased. Number of rots increased from 2.7 to 3.9 per 60-fruit sample with increasing nitrogen. Titratable acidity and soluble solids after harvest and pressure test, titratable acidity, soluble solids, stem-cavity browning and core flush in tests after storage were all unchanged. In addition, none of these tests were affected by cultivation except pressure test, which decreased with more cultivation (P = 0.10).Yield was not changed by either the nitrogen or the cultivation treatments, and terminal length increased only slightly with more cultivation. However, nitrogen concentration in the leaf was increased from 1.90 to 1.98% by the nitrogen treatments and from 1.83 to 1.98% by increasing cultivation. Extra Fancy grade was reduced and C grade increased by increasing nitrogen (P = 0.10), but cultivation had no effect.The conclusion is drawn that grass sod can very largely eliminate the effect of widely different nitrogen fertilization levels on McIntosh apple, and that moderate cultivation changes this effect only slightly. In many mature orchards of high initial fertility, nitrogen fertilizer may be required in only small amounts or even not at all for optimum fruit color.

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