Deep learning in environmental remote sensing: Achievements and challenges
Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …
environmental remote sensing research. With an increasing amount of “big data” from earth …
Uniting remote sensing, crop modelling and economics for agricultural risk management
The increasing availability of satellite data at higher spatial, temporal and spectral
resolutions is enabling new applications in agriculture and economic development …
resolutions is enabling new applications in agriculture and economic development …
Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height
Obtaining crop above-ground biomass (AGB) information quickly and accurately is
beneficial to farmland production management and the optimization of planting patterns …
beneficial to farmland production management and the optimization of planting patterns …
Recurrent-based regression of Sentinel time series for continuous vegetation monitoring
Dense time series of optical satellite imagery describing vegetation activity provide essential
information for the efficient and regular monitoring of vegetation. Nevertheless, the temporal …
information for the efficient and regular monitoring of vegetation. Nevertheless, the temporal …
Comparing Sentinel-1 and-2 data and indices for agricultural land use monitoring
AK Holtgrave, N Röder, A Ackermann, S Erasmi… - Remote Sensing, 2020 - mdpi.com
Agricultural vegetation development and harvest date monitoring over large areas requires
frequent remote sensing observations. In regions with persistent cloud coverage during the …
frequent remote sensing observations. In regions with persistent cloud coverage during the …
Improved prediction of rice yield at field and county levels by synergistic use of SAR, optical and meteorological data
Timely and accurate rice yield prediction over large regions is imperative to making informed
decisions on precision crop management and ensuring regional food security. Previous …
decisions on precision crop management and ensuring regional food security. Previous …
Review of synthetic aperture radar with deep learning in agricultural applications
Abstract Synthetic Aperture Radar (SAR) observations, valued for their consistent acquisition
schedule and not being affected by cloud cover and variations between day and night, have …
schedule and not being affected by cloud cover and variations between day and night, have …
Deep learning-based estimation of crop biophysical parameters using multi-source and multi-temporal remote sensing observations
Remote sensing data are considered as one of the primary data sources for precise
agriculture. Several studies have demonstrated the excellent capability of radar and optical …
agriculture. Several studies have demonstrated the excellent capability of radar and optical …
Machine learning model ensemble for predicting sugarcane yield through synergy of optical and SAR remote sensing
Pre-harvest estimate of sugarcane production is required by sugar mill officials for proper
planning about intra or inter-regional trading of sugarcane if expected production is more or …
planning about intra or inter-regional trading of sugarcane if expected production is more or …
A generalized model for mapping sunflower areas using Sentinel-1 SAR data
Existing crop mapping models, rely heavily on reference (calibration) data obtained from
remote sensing observations. However, the transferability of such models in space and time …
remote sensing observations. However, the transferability of such models in space and time …