"11558","8","archive","3170",,,"disk0/00/01/15/58","2020-08-19 16:38:09","2020-08-19 16:38:09","2020-08-19 16:38:09","article",,,"show",,,,"","","","","","","","","","",,,,"Gumma","M K","","","","","","","Gumma","M K","","","","",,,,,"","",,,,,"","","ICRISAT (Patancheru)","India","Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information","pub","S100","CRPS2","crp1.13","public",,,"Cropping pattern, Sentinel-2, Matching technique,
Small-plot agriculture, Semiarid–conditions",,"The authors are grateful to Divya Kashyap, Arindom Baidya, Kaushal Garg, Giaime Origgi, Loris Copa,
Venkataradha Akuraju, Bhavani, Michael Anthony, Jameeruddin and Prashant Patil for their suggestions
and recommendations. We also thank Jameeruddin A and Ismail Mohammed for their support during
ground data collection.","Accurate monitoring of croplands helps in making decisions (for
insurance claims, crop management and contingency plans) at
the macro-level, especially in drylands where variability in cropping
is very high owing to erratic weather conditions. Dryland
cereals and grain legumes are key to ensuring the food and nutritional
security of a large number of vulnerable populations living
in the drylands. Reliable information on area cultivated to such
crops forms part of the national accounting of food production
and supply in many Asian countries, many of which are employing
remote sensing tools to improve the accuracy of assessments
of cultivated areas. This paper assesses the capabilities and limitations
of mapping cultivated areas in the Rabi (winter) season and
corresponding cropping patterns in three districts characterized
by small-plot agriculture. The study used Sentinel-2 Normalized
Difference Vegetation Index (NDVI) 15-day time-series at 10m
resolution by employing a Spectral Matching Technique (SMT)
approach. The use of SMT is based on the well-studied relationship
between temporal NDVI signatures and crop phenology. The
rabi season in India, dominated by non-rainy days, is best suited
for the application of this method, as persistent cloud cover will
hamper the availability of images necessary to generate clearly
differentiating temporal signatures. Our study showed that the
temporal signatures of wheat, chickpea and mustard are easily
distinguishable, enabling an overall accuracy of 84%, with wheat
and mustard achieving 86% and 94% accuracies, respectively. The
most significant misclassifications were in irrigated areas for mustard
and wheat, in small-plot mustard fields covered by trees and
in fragmented chickpea areas. A comparison of district-wise
national crop statistics and those obtained from this study
revealed a correlation of 96%.","2020-08","published",,"Geocarto International (TSI)",,,"Taylor and Francis",,"1-17",,,,,,"doi:10.1080/10106049.2020.1805029",,,,,"TRUE",,"1010-6049",,,,,,"","https://doi.org/10.1080/10106049.2020.1805029","https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=10.1080%2F10106049.2020.1805029&btnG=","pub",,"Swiss Agency for Development and Cooperation (SDC),","RIICE III",,,,,,"",,,,,,,"",,,,,"",,,,,"","",,,,,"","",,,,,
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"11558",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"Dixit","S","","",,,,,"Dixit","S","","",,,,,,,,,,,,,,,"International Reinsurance and Insurance Consultancy and Broking Services Pvt. Ltd, Mumbai, India","Tanzania",,,,"CRPS4",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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