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The forgotten land use class: Mapping of fallow fields across the Sahel using Sentinel-2
کلاس استفاده از اراضی فراموش شده: نقشه برداری از مزارع مزرعه در ساحل با استفاده از سنتینل-2-2020 Remote sensing-derived cropland products have depicted the location and extent of agricultural lands with an
ever increasing accuracy. However, limited attention has been devoted to distinguishing between actively
cropped fields and fallowed fields within agricultural lands, and in particular so in grass fallow systems of semiarid
areas. In the Sahel, one of the largest dryland regions worldwide, crop-fallow rotation practices are widely
used for soil fertility regeneration. Yet, little is known about the extent of fallow fields since fallow is not
explicitly differentiated within the cropland class in any existing remote sensing-based land use/cover maps,
regardless of the spatial scale. With a 10 m spatial resolution and a 5-day revisit frequency, Sentinel-2 satellite
imagery made it possible to disentangle agricultural land into cropped and fallow fields, facilitated by Google
Earth Engine (GEE) for big data handling. Here we produce the first Sahelian fallow field map at a 10 m resolution
for the baseline year 2017, accomplished by designing a remote sensing driven protocol for generating
reference data for mapping over large areas. Based on the 2015 Copernicus Dynamic Land Cover map at 100 m
resolution, the extent of fallow fields in the cropland class is estimated to be 63% (403,617 km2) for the Sahel in
2017. Similar results are obtained for five contemporary cropland products, with fallow fields occupying
57–62% of the cropland area. Yet, it is noted that the total estimated area coverage depends on the quality of the
different cropland products. The share of cropped fields within the Copernicus cropland area is found to be
higher in the arid regions (200–300 mm rainfall) as compared to the semi-arid regions (300–600 mm rainfall).
The woody cover fraction within cropped and fallow fields is found to have a reversed pattern between arid
(higher woody cover in cropped fields) and semi-arid (higher woody cover in fallow fields) regions. The method
developed, using cloud-based Earth Observation (EO) data and computation on the GEE platform, is expected to
be reproducible for mapping the extent of fallow fields across global croplands. Future applications based on
multi-year time series is expected to improve our understanding of crop-fallow rotation dynamics in grass fallow
systems being key in teasing apart how cropland intensification and expansion affect environmental variables,
such as soil fertility, crop yields and local livelihoods in low-income regions such as the Sahel. The mapping
result can be visualized via a web viewer (https://buwuyou.users.earthengine.app/view/fallowinsahel). Keywords: Fallow fields | Cropland | Satellite image time series | Land use/cover mapping | Sentinel-2 | Drylands | Sahel |
مقاله انگلیسی |
2 |
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
DuPLO: نقطه دید DUal معماری یادگیری عمیق برای classificati سری زمانی-2019 Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example
is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10 m) with high
temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the
use of SITS has been proved to be beneficial in the context of Land Use/Land Cover (LULC) map generation,
unfortunately, most of machine learning approaches commonly leveraged in remote sensing field fail to take
advantage of spatio-temporal dependencies present in such data.
Recently, new generation deep learning methods allowed to significantly advance research in this field. These
approaches have generally focused on a single type of neural network, i.e., Convolutional Neural Networks
(CNNs) or Recurrent Neural Networks (RNNs), which model different but complementary information: spatial
autocorrelation (CNNs) and temporal dependencies (RNNs). In this work, we propose the first deep learning
architecture for the analysis of SITS data, namely DuPLO (DUal view Point deep Learning architecture for time
series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity.
Our hypothesis is that, since CNNs and RNNs capture different aspects of the data, a combination
of both models would produce a more diverse and complete representation of the information for the underlying
land cover classification task. Experiments carried out on two study sites characterized by different land cover
characteristics (i.e., the Gard site in Mainland France and Reunion Island, a overseas department of France in the
Indian Ocean), demonstrate the significance of our proposal Keywords: Satellite image time series | Deep learning | Land cover classification | Sentinel-2 |
مقاله انگلیسی |
3 |
Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture
ترکیب سری زمانی ماهواره ای Sentinel-1 و Sentinel-2 برای نقشه برداری از پوشش زمین از طریق یک معماری یادگیری عمیق چند منبعی-2019 The huge amount of data currently produced by modern Earth Observation (EO) missions has allowed for the
design of advanced machine learning techniques able to support complex Land Use/Land Cover (LULC) mapping
tasks. The Copernicus programme developed by the European Space Agency provides, with missions such as
Sentinel-1 (S1) and Sentinel-2 (S2), radar and optical (multi-spectral) imagery, respectively, at 10m spatial
resolution with revisit time around 5 days. Such high temporal resolution allows to collect Satellite Image Time
Series (SITS) that support a plethora of Earth surface monitoring tasks. How to effectively combine the complementary
information provided by such sensors remains an open problem in the remote sensing field. In this
work, we propose a deep learning architecture to combine information coming from S1 and S2 time series,
namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies
in both types of SITS. The proposed architecture is devised to boost the land cover classification task by
leveraging two levels of complementarity, i.e., the interplay between radar and optical SITS as well as the
synergy between spatial and temporal dependencies. Experiments carried out on two study sites characterized by
different land cover characteristics (i.e., the Koumbia site in Burkina Faso and Reunion Island, a overseas department
of France in the Indian Ocean), demonstrate the significance of our proposal. Keywords: Satellite Image Time Series | Deep learning | Land cover classification | Sentinel-2 | Sentinel-1 | Data fusion |
مقاله انگلیسی |