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An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series
2015
Remote Sensing
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Within the framework of GEO Global Agricultural Monitoring (GEOGLAM) [29, 30] , the Joint Experiment of Crop Assessment and Monitoring (JECAM) [31] initiative develops a convergence of standards for monitoring ...
This research introduces a generic methodology for mapping annual cropland along the season at high spatial resolution with the use of globally available baseline land cover and no need for field data. ...
These performances seem compatible with the expected use of a cropland mask in an operational agricultural monitoring system, i.e., masking out the non-cropland area to specifically monitor the crop growing ...
doi:10.3390/rs71013208
fatcat:qaqeaurvmnbvdj6ca3qrdwlr3i
The potential of satellite-observed crop phenology to enhance yield gap assessments in smallholder landscapes
2015
Frontiers in Environmental Science
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Similarly, the spatial coverage of remote sensing-derived phenology offers potential for integration with ancillary spatial datasets to identify causes of yield gaps. ...
However, the information (e.g., location and causes of cropland underperformance) required to support measures to close yield gaps in smallholder landscapes are often not available. ...
PA is grateful to the University of Utrecht for supporting him with The Belle van Zuylen Chair. ...
doi:10.3389/fenvs.2015.00056
fatcat:kfbw6w5bdzefro4kp3fllxtpuy
Analysis of Water Resources in Horqin Sandy Land Using Multisource Data from 2003 to 2010
2016
Sustainability
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The result shows that the groundwater depletion rate in Horqin Sandy Land is 13.5˘1.9 mm¨year´1 in 2003-2010, which is consistent with the results of monitoring well stations. ...
Bare soil and built-up land have increased by 76.6% and 82.2%, respectively, while cropland, vegetation, and water bodies have decreased by 14.1%, 74.5%, and 82.6%, respectively. ...
Level Yearbook for providing in situ monitoring well observations, and the Geospatial Data Cloud for providing Landsat TM imageries. ...
doi:10.3390/su8040374
fatcat:xxkcm5tipbci3njqmjlrnccuze
Mapping Irrigated Areas of Northeast China in Comparison to Natural Vegetation
2019
Remote Sensing
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Second, the difference in LSWI between irrigation cropland and forest is larger in arid regions than in humid regions. ...
First, the canopy moisture of irrigated cropland, indicated by a satellite-based land surface water index (LSWI), is higher than that of the adjacent forest. ...
Acknowledgments: We thank Haicheng Zhang, Xiaoyuan Wang and Wenfang Xu for their help of data processing.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs11070825
fatcat:hhtcam5kgbeqvcyh5gond3hyjy
Bayesian spatial extreme value analysis to assess the changing risk of concurrent high temperatures across large portions of European cropland
2012
Environmetrics
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There is strong evidence that extremely high temperatures are detrimental to the yield and quality of many economically and socially critical crops. ...
We wish to assess the risk of the catastrophic scenario in which large areas of croplands experience extreme heat stress during the same growing season. ...
This study regressed global yields on maximum and minimum temperatures extracted from a gridded data product (Mitchell and Jones, 2005) . ...
doi:10.1002/env.2178
fatcat:iah7ufj2izb5njtafr2xk2e6im
The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World
2021
Remote Sensing
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We also discuss the future challenges associated with maintaining food security in ASA regions and explore some recent advances in RS that can be used to monitor cropland and forecast crop production and ...
yield. ...
process
regression (GPR), and random forest (RF)
R 2 = 0.75
[119]
6
Iran, Boshruyeh city
Barley
Sentinel-2/10 m
Gaussian process regression algorithm,
decision tree, K-nearest neighbour regression ...
doi:10.3390/rs13173382
fatcat:gva463nkdjbark4odzswvqg7l4
Cropland soil organic matter content change in Northeast China, 1985-2005
2015
Open Geosciences
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The datasets presented here can be used not only as baselines for the calibration of process-based carbon budget models, but also to identify regional soil quality hotspots and to guide spatial-explicit ...
Our results showed that SOM content decreased in 39% of all the cropland in NEC, while increase in SOM content was only detected on 16% of the cropland. ...
On the other hand, surface temperature in China has risen 0.8 • C in the twentieth century, being consistent with the general global trend [41] . ...
doi:10.1515/geo-2015-0034
fatcat:nb4mjc7fr5fhtisylm5v4ohj2y
From tropical shelters to temperate defaunation: The relationship between agricultural transition stage and the distribution of threatened mammals
2018
Global Ecology and Biogeography
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Here, we evaluate how the extent, intensity, and history of croplands relate to the 34 global distribution of threatened mammals. ...
Data were analysed with a grain size of ~110 x 110 km at 49 both global and biogeographic-realm scales. 50 Results 51 Agricultural extent and intensity were the most relevant indicator types, with specific ...
Globally, croplands tended to co-occur with 258 built-up areas and heavily fertilized areas (Spearman's ρ=0.89 and ρ=0.74, respectively) and were 259 moderately disagreeing with non-used portions (ρ=-0.57 ...
doi:10.1111/geb.12725
fatcat:tdomvboaqbca3bhzqqk6bcgrqy
A Gaussian Kernel-based Spatiotemporal Fusion Model for Agricultural Remote Sensing Monitoring
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Time series normalized difference vegetation index (NDVI) is the primary data for agricultural remote sensing monitoring. ...
The experimental results show that GKSFM outperformed the comparative models in different proportions of cropland/non-cropland and different crop phenology. ...
[7] and yield prediction [8] [9] [10] [11] [12] . ...
doi:10.1109/jstars.2021.3066055
fatcat:kooy6scex5dd7eeqex7lh5vyey
Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m
2016
Remote Sensing
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The fully-automated method offers the first cropland map at 100-m using the PROBA-V sensor with an overall accuracy of 84% and an F-score for the cropland class of 74%. ...
Further improvements are expected with the upcoming enhanced cloud screening of the PROBA-V sensor. ...
., meaning no human action during the whole processing from images to cropland map. ...
doi:10.3390/rs8030232
fatcat:eh4lkqqk3jgiti6rjaod2ijxrm
Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI
2021
Remote Sensing
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Seasonal climate is the main driver of crop growth and yield in broadacre grain cropping systems. ...
For cropland, the correlation is improved by 58%, the MAE by 36% and the RMSE by 76%. ...
Areas with high RMSE but not necessarily high MAE have greater NDVI variability, such as lower-yielding cropland. ...
doi:10.3390/rs13112128
fatcat:xr5o6ibevrhzxa67xd5vxx423e
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
[article]
2021
arXiv
pre-print
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Furthermore, processing satellite and ground survey data requires domain knowledge that many in the machine learning community lack. ...
toward the United Nations Sustainable Development Goals (SDGs) has been hindered by a lack of data on key environmental and socioeconomic indicators, which historically have come from ground surveys with ...
Wang and Jiaxuan You for their help in making the crop yield dataset; and Han Lin Aung and Burak Uzkent for permission to release the field delineation dataset. ...
arXiv:2111.04724v1
fatcat:55nfc47fvveilj5bmhitzz5gbq
National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
2021
Remote Sensing
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Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. ...
The cropland classification product for the nominal year 2019–2020 showed an overall accuracy of 97.86% with a Kappa of 0.95. ...
Thus, a reliable cropland monitoring system requires satellite datasets with both high spatial and temporal resolutions [4] . ...
doi:10.3390/rs13214378
fatcat:xihlx4zyxndb3kthcaomdorvdi
Pixelating crop production: Consequences of methodological choices
2019
PLoS ONE
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harvested area, production quantity and yield). ...
The results are also somewhat sensitive to the use of a simple spatial allocation method based solely on cropland proportions versus a cross-entropy allocation method, as well as the set of crops or crop ...
However, highly detailed maps of cropland, crop acreage and crop performance are only available from a handful of national agricultural monitoring programs [2] [3] . ...
doi:10.1371/journal.pone.0212281
pmid:30779813
pmcid:PMC6380596
fatcat:2wqdpjiz7feq5bycirwyclgggq
Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain
2013
Geocarto International
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We also thank Dr Per Jönsson, Malmo University, and Dr Lars Eklundh, Lund University, for their help with the TIMESAT software application. This research is ...
Therefore, monitoring phenological changes in croplands could improve our understanding of their biological responses to a warmer climate. ...
The parameters in Equation (2) are determined in an iterative process with predefined convergence criteria. ...
doi:10.1080/10106049.2012.760004
fatcat:gy5oqaz3uvaptbelo6ockmgoiy
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