New Regression Models for Prediction of Grain Yield Anomalies from Satellite-Based Vegetation Health Indices

被引：1

作者：

Menzhulin, Gennady ^{[1
]}

Shamshurina, Natalya ^{[1
]}

Pavlovsky, Artyom ^{[1
]}

Kogan, Felix ^{[2
]}

机构：

[1] Russian Acad Sci, Res Ctr Interdisciplinary Environm Cooperat, St Petersburg 196140, Russia

[2] NOAA, NESDISW, Washington, DC USA

来源：

USE OF SATELLITE AND IN-SITU DATA TO IMPROVE SUSTAINABILITY | 2011年

关键词：

Crop yields anomaly; Vegetation health indices; Precipitation; Temperature; Models; VALIDATION;

D O I：

10.1007/978-90-481-9618-0_12

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In the late 1970s, the first operational weather satellite system had been launched, which showed utility for monitoring land greenness, vigor and vegetation productivity. Currently, 30-year satellite data from the Advanced Very High Resolution Radiometer (AVHRR) are available for monitoring land surface, atmosphere near the ground, natural disasters, and socioeconomic activities. Statistical modeling of agricultural crop yield and production was one of the applications. This paper discusses the topic, how design the new regression models of yield anomaly based on multivariate algorithms and selection of best-fit ensemble of predictors.

引用

页码：105 / +

页数：2

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