Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data
Accurate and timely monitoring of flooded crop areas is crucial for disaster rescue and loss
assessment. However, most flooded crop monitoring methods based on synthetic aperture …
assessment. However, most flooded crop monitoring methods based on synthetic aperture …
[HTML][HTML] County-level soybean yield prediction using deep CNN-LSTM model
Yield prediction is of great significance for yield mapping, crop market planning, crop
insurance, and harvest management. Remote sensing is becoming increasingly important in …
insurance, and harvest management. Remote sensing is becoming increasingly important in …
Enhancing FAIR data services in agricultural disaster: A review
The agriculture sector is highly vulnerable to natural disasters and climate change, leading
to severe impacts on food security, economic stability, and rural livelihoods. The use of …
to severe impacts on food security, economic stability, and rural livelihoods. The use of …
A framework for multi-sensor satellite data to evaluate crop production losses: the case study of 2022 Pakistan floods
In August 2022, one of the most severe floods in the history of Pakistan was triggered due to
the exceptionally high monsoon rainfall. It has affected~ 33 million people across the …
the exceptionally high monsoon rainfall. It has affected~ 33 million people across the …
DeepYield: A combined convolutional neural network with long short-term memory for crop yield forecasting
Crop yield forecasting is of great importance to crop market planning, crop insurance,
harvest management, and optimal nutrient management. Commonly used approaches for …
harvest management, and optimal nutrient management. Commonly used approaches for …
A systematic review on case studies of remote-sensing-based flood crop loss assessment
This article reviews case studies which have used remote sensing data for different aspects
of flood crop loss assessment. The review systematically finds a total of 62 empirical case …
of flood crop loss assessment. The review systematically finds a total of 62 empirical case …
[HTML][HTML] Cotton yield estimation model based on machine learning using time series UAV remote sensing data
Crop yield prediction is of great practical significance for farmers to make reasonable
decisions, such as decisions on crop insurance, storage demand, cash flow budget …
decisions, such as decisions on crop insurance, storage demand, cash flow budget …
Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive
Field-level monitoring of crop types in the United States via the Cropland Data Layer (CDL)
has played an important role in improving production forecasts and enabling large-scale …
has played an important role in improving production forecasts and enabling large-scale …
Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm
Abstract Space-based crop identification and acreage estimation have played a significant
role in agricultural studies in recent years, due to the development of Remote Sensing …
role in agricultural studies in recent years, due to the development of Remote Sensing …
An unsupervised domain adaptation deep learning method for spatial and temporal transferable crop type mapping using Sentinel-2 imagery
Accurate crop type mapping is essential for crop growth monitoring and yield estimation.
Recently, various machine learning methods have been increasingly used for crop type …
Recently, various machine learning methods have been increasingly used for crop type …