Annual field-scale maps of tall and short crops at the global scale using GEDI and Sentinel-2

• URL: http://arxiv.org/abs/2212.09681v1
• Date: Mon, 19 Dec 2022 18:09:34 GMT
• Title: Annual field-scale maps of tall and short crops at the global scale using GEDI and Sentinel-2
• Authors: Stefania Di Tommaso, Sherrie Wang, Vivek Vajipey, Noel Gorelick, Rob Strey, David B. Lobell
• Abstract summary: We develop wall-to-wall maps of short vs tall crops on a global scale at 10 m resolution for 2019-2021. GEDI returns can reliably be classified into tall and short crops after removing shots with extreme view angles or topographic slope. GEDI-S2 performed nearly as well as models trained on thousands of local reference training points.
• Score: 11.379287122235954
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Crop type maps are critical for tracking agricultural land use and estimating crop production. Remote sensing has proven an efficient and reliable tool for creating these maps in regions with abundant ground labels for model training, yet these labels remain difficult to obtain in many regions and years. NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, originally designed for forest monitoring, has shown promise for distinguishing tall and short crops. In the current study, we leverage GEDI to develop wall-to-wall maps of short vs tall crops on a global scale at 10 m resolution for 2019-2021. Specifically, we show that (1) GEDI returns can reliably be classified into tall and short crops after removing shots with extreme view angles or topographic slope, (2) the frequency of tall crops over time can be used to identify months when tall crops are at their peak height, and (3) GEDI shots in these months can then be used to train random forest models that use Sentinel-2 time series to accurately predict short vs. tall crops. Independent reference data from around the world are then used to evaluate these GEDI-S2 maps. We find that GEDI-S2 performed nearly as well as models trained on thousands of local reference training points, with accuracies of at least 87% and often above 90% throughout the Americas, Europe, and East Asia. Systematic underestimation of tall crop area was observed in regions where crops frequently exhibit low biomass, namely Africa and South Asia, and further work is needed in these systems. Although the GEDI-S2 approach only differentiates tall from short crops, in many landscapes this distinction goes a long way toward mapping the main individual crop types. The combination of GEDI and Sentinel-2 thus presents a very promising path towards global crop mapping with minimal reliance on ground data.

Related papers

• Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning [1.6889377382676625]
  We release a large dataset of individual shrub delineations on freely available satellite imagery. We use an instance segmentation model to map all junipers over the treeline for an entire biosphere reserve. Our model achieved an F1-score in shrub delineation of 87.87% on the PI data and 76.86% on the FW data.
  arXiv  Detail & Related papers  (2024-01-31T16:44:20Z)
• Automating global landslide detection with heterogeneous ensemble deep-learning classification [44.99833362998488]
  Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. Deep learning models have recently been applied for landside mapping using medium- to high-resolution satellite images as input.
  arXiv  Detail & Related papers  (2023-09-12T10:56:16Z)
• 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)
• Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights [50.52704854147297]
  We present a new vision transformer (ViT) model optimized with a classification (discrete) and a continuous loss function. This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
  arXiv  Detail & Related papers  (2023-04-22T22:39:03Z)
• Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery [101.18253437732933]
  We present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery. We solve real-world deep learning problems in the very specific context of agave crop segmentation. With the resulting accurate models, agave production forecasting can be made available for large regions.
  arXiv  Detail & Related papers  (2023-03-21T03:15:29Z)
• Towards Global Crop Maps with Transfer Learning [0.0]
  Deep learning models require large amounts of annotated data that are scarce and hard-to-acquire. In this work, we have developed and trained a deep learning model for paddy rice detection in South Korea using Sentinel-1 VH-1 time-series. Our approach shows excellent performance when transferring in different areas for the same crop type and rather promising results when transferring in a different area and crop type.
  arXiv  Detail & Related papers  (2022-11-09T09:17:42Z)
• Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan Region [0.28675177318965034]
  We develop an ML pipeline that relies on Sentinel-2 satellite images time series. We generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps.
  arXiv  Detail & Related papers  (2022-03-28T12:08:06Z)
• Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops [0.0]
  We explore the use of NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, combined with Sentinel-2 optical data, for crop type mapping. GEDI energy profiles are capable of reliably distinguishing maize, a crop typically above 2m in height, from crops like rice and soybean that are shorter. GEDI is able to distinguish maize from other crops within each region with accuracies higher than 84%, and able to transfer across regions with accuracies higher than 82%.
  arXiv  Detail & Related papers  (2021-09-10T16:55:50Z)
• 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)
• Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study [82.02173199363571]
  We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images, accounting for more than 200 billions samples (pixels) We present tools for data representativeness analysis in terms of appearance similarity and a thorough study of MiniFrance data, demonstrating that it is suitable for learning and generalizes well in a semi-supervised setting.
  arXiv  Detail & Related papers  (2020-10-15T15:36:58Z)

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.