Farming systems monitoring using machine learning and trend analysis methods based on fitted NDVI time series data in a semi-arid region of Morocco-Reference-Cited by-同舟云学术

Farming systems monitoring using machine learning and trend analysis methods based on fitted NDVI time series data in a semi-arid region of Morocco

Published:2019-10-21 Issue: Volume: Page:
ISSN:
Container-title:Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI
language:
Short-container-title:

Author:

Lebrini Youssef,Benabdelouahab Tarik,Boudhar Abdelghani,Htitiou Abdelaziz,Hadria Rachid,Lionboui Hayat

Publisher

SPIE

Reference33 articles.

1. Identifying Droughts Affecting Agriculture in Africa Based on Remote Sensing Time Series between 2000–2016: Rainfall Anomalies and Vegetation Condition in the Context of ENSO

2. Identifying Agricultural Systems Using SVM Classification Approach Based on Phenological Metrics in a Semi-arid Region of Morocco

3. Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions

4. Mapping Crop Cycles in China Using MODIS-EVI Time Series

5. Analysis of Farms Performance Using Different Sources of Irrigation Water: A Case Study in a Semi-Arid Area;Lionboui;International Journal of Agricultural Management and Development,2016

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Dynamic Changes and Influencing Factors of Vegetation in the “Green Heart” Zone of the Chang-Zhu-Tan Urban Agglomeration during the Past 21 Years;International Journal of Environmental Research and Public Health;2023-03-03

2. Impact of massive development projects on ecosystem services in Mediterranean rural landscapes;Remote Sensing Applications: Society and Environment;2023-01

3. Mapping and Characterization of Phenological Changes over Various Farming Systems in an Arid and Semi-Arid Region Using Multitemporal Moderate Spatial Resolution Data;Remote Sensing;2021-02-06

4. Remote monitoring of agricultural systems using NDVI time series and machine learning methods: a tool for an adaptive agricultural policy;Arabian Journal of Geosciences;2020-08

5. A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: a machine learning approach;Geocarto International;2020-05-20