scholarly journals Soil Moisture Depletion Modelling Using a TDR Multi-Sensor System, GIS, Soil Analyzes, Precision Agriculture and Remote Sensing on Maize for Improved Irrigation-Fertilization Decisions

2021 ◽  
Vol 9 (1) ◽  
pp. 36
Author(s):  
Agathos Filintas
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Precision Agriculture ◽  
Maize Yield ◽  
Drainage Water ◽  
Substantial Improvement ◽  
Gis Mapping ◽  
Water Losses ◽  
Geostatistical Models ◽  
Sentinel 2

The effects of three drip irrigation (IR1: Farmer’s, IR2:Full (100%ETc), IR3:Deficit (80%ETc) irrigation), and two fertilization (Ft1, Ft2) treatments were studied on maize yield and biomass by applying new agro-technologies (TDR—sensors for soil moisture (SM) measurements, Precision Agriculture, Remote Sensing—NDVI (Sentinel-2 satellite sensor), soil-hydraulic analyses and Geostatistical models, SM-rootzone modelling-2D-GIS mapping). A daily soil moisture depletion (SMDp) model was developed. The two-way-ANOVA statistical analysis results revealed that irrigation (IR3 = best) and fertilization treatments (Ft1 = best) significantly affect yield and biomass. Deficit irrigation and proper fertilization based on new agro-technologies for improved management decisions can result in substantial improvement on yield (+116.10%) and biomass (+119.71%) with less net water use (−7.49%) and reduced drainage water losses (−41.02%).

Rainfed and Supplemental Irrigation Modelling 2D GIS Moisture Rootzone Mapping on Yield and Seed Oil of Cotton (Gossypium hirsutum) Using Precision Agriculture and Remote Sensing

2021 ◽  
Vol 9 (1) ◽  
pp. 37
Author(s):  
Agathos Filintas ◽  
Aikaterini Nteskou ◽  
Persefoni Katsoulidi ◽  
Asimina Paraskebioti ◽  
Marina Parasidou
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Oil Content ◽  
Seed Oil ◽  
Substantial Improvement ◽  
Balance Model ◽  
Gis Mapping ◽  
Growth Stages ◽  
Supplemental Irrigation ◽  
Geostatistical Models

The effects of two irrigation (IR1: rainfed; IR2: rainfed + supplemental drip irrigation), and two fertilization (Ft1, Ft2) treatments were studied on cotton yield and seed oil by applying a number of new agro-technologies such as: TDR sensors; soil moisture (SM); precision agriculture; remote-sensing NDVI (Sentinel-2 satellite sensor); soil-hydraulic analyses; geostatistical models; SM-rootzone, and modelling 2D GIS mapping. A daily soil-water-crop-atmosphere (SWCA) balance model was developed. The two-way ANOVA statistical analysis results revealed that irrigation (IR2 = best) and fertilization treatments (Ft1 = best) significantly affected yield and oil content. Supplemental irrigation, if applied during critical growth stages, could result in substantial improvement on yield (+234.12%) and oil content (+126.44%).

scholarly journals USABILITY, STRENGTH AND PRACTICALITY OF THE UPCOMING HYSPIRI IN DETECTING MAIZE GRAY LEAFY SPOT IN RELATION TO SENTINEL-2 MSI, VENμS AND LANDSAT 8 OLI SPECTRAL BAND-SETTINGS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1015-1022
Author(s):  
M. Sibanda ◽  
O. Mutanga ◽  
T. Dube ◽  
J. Odindi ◽  
P. L. Mafongoya
Keyword(s):  
Disease Progression ◽  
Precision Agriculture ◽  
Spectral Band ◽  
Maize Yield ◽  
Gray Leaf Spot ◽  
Added Value ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
To Come ◽  
Sentinel 2

Abstract. Considering the high maize yield loses that are caused by diseases incidences as well as incomprehensive monitoring initiatives in the crop farming sector of agriculture, there is a need to come up with spatially explicit, cheap, fast and consistent approaches for monitoring as well as forecasting food crop diseases, such as maize gray leaf spot. This study, therefore, we sought to investigate the usability, strength and practicality of the forthcoming HyspIRI in detecting disease progression of Maize Gray leafy spot infections in relation to the Sentinel-2 MSI, Landsat 8 OLI spectral configurations. Maize Gray leafy spot disease progression that were discriminated based on partial least squares –discriminant analysis (PLS-DA) algorithm were (i) healthy, (ii) intermediate and (ii) severely infected maize crops. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93 and 0.89 exhibited by Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor sensors, respectively. Further, the results showed that the visible section the red-edge and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray leafy spot infections. These findings underscore the added value and potential scientific breakthroughs likely to be brought about by the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop disease epidemics to ensure food security.

scholarly journals Rainfed cultivation with supplemental irrigation modelling on seed yield and oil of Coriandrum sativum L. using precision agriculture and GIS moisture mapping

2021 ◽  
Author(s):  
Agathos Filintas ◽  
Eleni Wogiatzi ◽  
Nikolaos Gougoulias
Keyword(s):  
Soil Moisture ◽  
Essential Oil ◽  
Seed Yield ◽  
Precision Agriculture ◽  
Oil Content ◽  
Root Zone ◽  
Coriandrum Sativum ◽  
Gis Mapping ◽  
Growth Stages ◽  
Supplemental Irrigation

Abstract The aim of the present study was to determine the effects of rainfed and supplemental irrigation, and sowing period (SP) treatments on Coriander (Coriandrum sativum L.) yield, essential oil content and umbel heights by applying new agro-technologies (TDR-sensors for soil moisture (SM), GIS, Precision Agriculture, soil-hydraulic analyses and Geostatistical models) for yield and SM root zone geospatial modelling and two-dimensional GIS mapping. Results of laboratory analysis indicated a suitable soil for coriander's growth and revealed that field's soil was characterized Sandy Clay Loam(SCL) with mean values: Soil Organic Matter(SOM) = 1.70%, bulk specific gravity = 1.42 g·cm−3, Plant Available Water = 0.129 cm·cm−1, pH = 7.10 and cation-exchange capacity(CEC) = 19.3 cmol·kg−1. The two-way ANOVA statistical analysis (P = 0.05) results revealed that the irrigation treatments (IR1:rainfed, IR2:rainfed plus supplemental irrigation[best]), and the SP treatments (SP1:October's last week, SP2:November's first week[best]) significantly affects Coriander's seed yield and essential oil content, but the SP have no significant effect on plant's umbel height (P = 0.873). Supplemental irrigation, using a limited amount of water, if applied during the critical crop growth stages, can result in substantial improvement on seed yield (+284.934%), essential oil content (+125.396%) and plant's umbel height (+117.929%). HIGHLIGHT Geostatistical modelling on yield and oil of Coriander (Coriandrum sativum L.), GIS, Precision Agriculture, Rainfed cultivation with supplemental irrigation, Soil and hydraulic analyses, TDR-soil moisture mapping.

Sentinel-2 exposed soil composite for soil organic carbon prediction: the ‘greening-up’ method for detecting suitable images

2021 ◽  
Author(s):  
Klara Dvorakova ◽  
Bas van Wesemael
Keyword(s):  
Remote Sensing ◽  
Surface Roughness ◽  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Satisfactory Result ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Sentinel 2

<p>Pilot studies have demonstrated the potential for remote sensing techniques for soil organic carbon  (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface such as photosynthetic active and nonphotosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, many studies have focused on stabilizing the soil reflectance by building image composites that are generated using a set of criteria. These composites tend to minimize and cancel out the disturbing effects. Here we aim to develop a robust method that allows selecting Sentinel-2 (S-2) pixels that are not affected by the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Loam Belt of Belgium from January 2019 to December 2020 (in total 38 images). We then built four exposed soil composites based on four sets of criteria: (1) NDVI < 0.25, (2) NDVI < 0.25 & Normalized Burn Ratio (NBR2) < 0.07, (3) the ‘greening-up’ period of a crop and (4) the ‘greening-up’ period of a crop & NBR2 < 0.07. The ‘greening-up’ period was selected based on the NDVI timeline, where ‘greening-up’ is considered as the last date of acquisition where the soil is bare (NDVI < 0.25) before the crop develops (NDVI > 0.6).,We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the four above described composites. We obtained a non-satisfactory result for composites (1) to (3): R² = 0.22, RMSE = 3.46 g C kg<sup>-1</sup> and RPD 1.12 for (1), R² = 0.19, RMSE = 3.43 g C kg<sup>-1</sup> and RPD 1.10 for (2) and R² = 0.15, RMSE = 2.74 g C kg<sup>-1</sup> and RPD 1.06 for (3). We, however, obtained a satisfactory result for composite (4): R² = 0.54, RMSE = 2.09 g C kg<sup>-1</sup> and RPD 1.68. Hence, the ‘greening-up’ method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be the surface coverage, which for a two-year period reached 47% of croplands, compared to 89% exposure if only the NDVI threshold is applied.</p>

scholarly journals Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

Agronomy ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 641 ◽  
Author(s):  
Joel Segarra ◽  
Maria Luisa Buchaillot ◽  
Jose Luis Araus ◽  
Shawn C. Kefauver
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Satellite Image ◽  
European Space Agency ◽  
Stress Monitoring ◽  
Biotic Stresses ◽  
Space Agency ◽  
Wide Range ◽  
Technical Features ◽  
Sentinel 2

The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

scholarly journals Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation

2019 ◽  
Vol 11 (13) ◽  
pp. 1618 ◽  
Author(s):  
Wen Zhuo ◽  
Jianxi Huang ◽  
Li Li ◽  
Xiaodong Zhang ◽  
Hongyuan Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Winter Wheat ◽  
Cloud Model ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Yield Estimation ◽  
Sensing Data ◽  
Winter Wheat Yield ◽  
Sentinel 2

Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Sign in / Sign up

Export Citation Format

Share Document