4.7 Article

Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods

Related references

Note: Only part of the references are listed.
Article Agronomy

Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models

Junliang Fan et al.

Summary: Accurate estimation of plant transpiration (T) is crucial for agricultural production, and this study investigated the use of machine learning models to estimate daily T of summer maize. Incorporating soil water content and leaf area index variables improved model performance, with the deep neural network (DNN) model slightly outperforming others.

AGRICULTURAL WATER MANAGEMENT (2021)

Article Agronomy

Evapotranspiration partitioning and water productivity of rainfed maize under contrasting mulching conditions in Northwest China

Jing Zheng et al.

Summary: Soil mulching can improve crop yield and water productivity by promoting plant transpiration and suppressing soil evaporation.

AGRICULTURAL WATER MANAGEMENT (2021)

Review Agriculture, Multidisciplinary

Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications

Zheng Zhou et al.

Summary: The paper reviews the application of infrared thermal imaging in assessing crop water stress, focusing on different technological aspects and challenges, particularly emphasizing imaging techniques for canopy segmentation. The future trend points towards the potential application of deep learning methods.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Article Engineering, Civil

Upscaling evapotranspiration from the instantaneous to the daily time scale: Assessing six methods including an optimized coefficient based on worldwide eddy covariance flux network

Lei Jiang et al.

Summary: Remote sensing plays a crucial role in mapping evapotranspiration at the regional scale. This study compared six upscaling methods for estimating daily ET from instantaneous ET under different ecosystems and meteorological conditions, finding that the MEF method and the sine method performed best in most ecosystems, with optimal instantaneous times varying by ecosystem.

JOURNAL OF HYDROLOGY (2021)

Article Agronomy

The mean value of gaussian distribution of excess green index: A new crop water stress indicator

Liyuan Zhang et al.

Summary: The study introduced a new crop water stress indicator, MGDEXG, and conducted experiments in a maize field in northern Colorado under varying deficit irrigation conditions. The results showed that MGDEXG was sensitive to maize water status and had significant correlations with water stress references such as CWSI.

AGRICULTURAL WATER MANAGEMENT (2021)

Article Engineering, Civil

Detecting and mapping irrigated areas in a Mediterranean environment by using remote sensing soil moisture and a land surface model

Jacopo Dari et al.

Summary: This study investigates the capability of remotely sensed soil moisture products to detect irrigation signals in an intensively irrigated area in North East Spain, proposing a method to map actually irrigated areas using the K-means clustering algorithm. The data sets used in this study include SMOS, SMAP, Sentinel-1, and ASCAT, with downscaled versions obtained by the DISPATCH algorithm. L-band passive microwave downscaling products, particularly SMAP at 1 km, show the best performance in detecting irrigation signals in the study area.

JOURNAL OF HYDROLOGY (2021)

Article Agronomy

Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices

Guomin Shao et al.

Summary: This study aimed to explore the potential of leaf area index (LAI) and multispectral vegetation indices (VIs) obtained by an unmanned aerial vehicle (UAV) for estimating the crop coefficient (Kc) value for maize on a field scale. The results showed that the RFR algorithm effectively estimated maize Kc values based on ground-based LAI and UAV-based VIs, which could provide a potential solution for the distribution of water use and precision irrigation on a field scale.

AGRICULTURAL WATER MANAGEMENT (2021)

Article Agriculture, Multidisciplinary

Temporal convolution-network-based models for modeling maize evapotranspiration under mulched drip irrigation

Zhijun Chen et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Article Environmental Sciences

Soybean yield prediction from UAV using multimodal data fusion and deep learning

Maitiniyazi Maimaitijiang et al.

REMOTE SENSING OF ENVIRONMENT (2020)

Article Remote Sensing

Mapping vegetation biophysical and biochemical properties using unmanned aerial vehicles-acquired imagery

Bing Lu et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2018)

Article Agriculture, Multidisciplinary

A refined method for rapidly determining the relationship between canopy NDVI and the pasture evapotranspiration coefficient

Muhammad Shahinur Alam et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Article Meteorology & Atmospheric Sciences

Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

Xianming Dou et al.

ADVANCES IN METEOROLOGY (2018)

Article Agriculture, Multidisciplinary

Evaluation of artificial intelligence models for actual crop evapotranspiration modeling in mulched and non-mulched maize croplands

Dahua Tang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Article Geography, Physical

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

Maitiniyazi Maimaitijiang et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2017)

Article Agronomy

Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients

Emily G. Kullberg et al.

AGRICULTURAL WATER MANAGEMENT (2017)

Article Agronomy

Comparison of canopy temperature-based water stress indices for maize

Kendall C. DeJonge et al.

AGRICULTURAL WATER MANAGEMENT (2015)

Article Environmental Sciences

Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index

Baburao Kamble et al.

REMOTE SENSING (2013)

Article Agronomy

Canopy Cover and Leaf Area Index Relationships for Wheat, Triticale, and Corn

David C. Nielsen et al.

AGRONOMY JOURNAL (2012)

Article Geochemistry & Geophysics

Improved Biomass Estimation Using the Texture Parameters of Two High-Resolution Optical Sensors

Janet E. Nichol et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2011)

Article Environmental Sciences

A VARI-based relative greenness from MODIS data for computing the fire potential index

P. Schneider et al.

REMOTE SENSING OF ENVIRONMENT (2008)

Review Computer Science, Artificial Intelligence

Image registration methods:: a survey

B Zitová et al.

IMAGE AND VISION COMPUTING (2003)

Article Agronomy

Field test of a soil water balance simulation model

B Panigrahi et al.

AGRICULTURAL WATER MANAGEMENT (2003)

Article Environmental Sciences

Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance

CST Daughtry et al.

REMOTE SENSING OF ENVIRONMENT (2000)