How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?

• URL: http://arxiv.org/abs/2307.02575v2
• Date: Sun, 2 Jun 2024 11:42:03 GMT
• Title: How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?
• Authors: Hannah Kerner, Catherine Nakalembe, Adam Yang, Ivan Zvonkov, Ryan McWeeny, Gabriel Tseng, Inbal Becker-Reshef,
• Abstract summary: EO-based monitoring systems require accurate cropland maps to provide information about croplands. There is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries.
• Score: 15.186674512627876
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa, where there is high food insecurity and sparse agricultural statistics. EO-based monitoring systems require accurate cropland maps to provide information about croplands, but there is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries. This study provides a quantitative evaluation and intercomparison of 11 publicly available land cover maps to assess their suitability for cropland classification and EO-based agriculture monitoring in Africa using statistically rigorous reference datasets from 8 countries. We hope the results of this study will help users determine the most suitable map for their needs and encourage future work to focus on resolving inconsistencies between maps and improving accuracy in low-accuracy regions.

Related papers

• A Machine Learning Approach for Crop Yield and Disease Prediction Integrating Soil Nutrition and Weather Factors [0.0]
  The development of an intelligent agricultural decision-supporting system for crop selection and disease forecasting in Bangladesh is the main objective of this work. The recommended approach uses a variety of datasets on the production of crops, soil conditions, agro-meteorological regions, crop disease, and meteorological factors.
  arXiv  Detail & Related papers  (2024-03-28T09:57:50Z)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
  We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
  arXiv  Detail & Related papers  (2022-01-26T14:58:51Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations [58.17039841385472]
  We take advantage of all parallel developments in mechanistic modeling and satellite data availability for advanced monitoring of crop productivity. Our model successfully estimates gross primary productivity across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.
  arXiv  Detail & Related papers  (2020-12-07T16:23:13Z)
• AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk [3.8902094267855163]
  This paper develops an end-to-end cranberry health monitoring system to enable and support real time cranberry over-heating assessment. Drone-based field data and ground-based sky data collection systems are used to collect video imagery at multiple time points for use in crop health analysis. The sun irradiance prediction error was found to be 8.41-20.36% MAPE in the 5-20 minutes time horizon.
  arXiv  Detail & Related papers  (2020-11-08T20:03:20Z)
• Learning from Data to Optimize Control in Precision Farming [77.34726150561087]
  Special issue presents the latest development in statistical inference, machine learning and optimum control for precision farming. Satellite positioning and navigation followed by Internet-of-Things generate vast information that can be used to optimize farming processes in real-time.
  arXiv  Detail & Related papers  (2020-07-07T12:44:17Z)
• Rapid Response Crop Maps in Data Sparse Regions [4.215938932388722]
  High-resolution cropland maps are not readily available for most countries, especially in regions dominated by smallholder farming. A major challenge for developing crop maps is that many regions do not have readily accessible ground truth data on croplands. We present a method for rapid mapping of croplands in regions where little to no ground data is available.
  arXiv  Detail & Related papers  (2020-06-23T17:19:26Z)
• UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture [0.8399688944263843]
  This paper presents a novel satellite imagery refinement framework based on a deep learning technique. It exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. A vineyard in Serralunga d'Alba (Northern Italy) was chosen as a case study for validation purposes.
  arXiv  Detail & Related papers  (2020-04-29T18:34:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.