Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model

被引:32
|
作者
Chahbi, Aicha [1 ,2 ]
Zribi, Mehrez [1 ]
Lili-Chabaane, Zohra [2 ]
Duchemin, Benoit [1 ]
Shabou, Marouen [1 ,2 ]
Mougenot, Bernard [1 ]
Boulet, Gilles [1 ]
机构
[1] UPS, CNRS, IRD, CESBIO,CNES, Toulouse, France
[2] Carthage Univ, LRSTE, INAT, Tunis, Tunisia
关键词
SATELLITE MEASUREMENTS; MOISTURE ESTIMATION; CROP PRODUCTION; WHEAT; NDVI; INDEX; REFLECTANCE; ALGORITHM; RADIATION; REGION;
D O I
10.1080/01431161.2013.875629
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
In semi-arid areas, a strongly variable climate represents a major risk for food safety. An operational grain yield forecasting system, which could help decision-makers to make early assessments and plan annual imports, is thus needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. In this context, the aim of the present study is to analyse the characteristics of two types of irrigated and non-irrigated cereals: barley and wheat. Through the use of a rich database, acquired over a period of two years for more than 30 test fields, and from 20 optical satellite SPOT/HRV images, two research approaches are considered. First, statistical analysis is used to characterize the vegetation's dynamics and grain yield, based on remotely sensed (satellite) normalized difference vegetation index (NDVI) measurements. A relationship is established between the NDVI and LAI (leaf area index). Different robust relationships (exponential or linear) are established between the satellite NDVI index acquired from SPOT/HRV images, just before the time of maximum growth (April), and grain and straw, for barley and wheat vegetation covers. Following validation of the proposed empirical approaches, yield maps are produced for the studied site. The second approach is based on the application of a Simple Algorithm for Yield Estimation (SAFY) growth model, developed to simulate the dynamics of the LAI and the grain yield. An inter-comparison between ground yield measurements and SAFY model simulations reveals that yields are underestimated by this model. Finally, the combination of multi-temporal satellite measurements with the SAFY model estimations is also proposed for the purposes of yield mapping. Although the results produced by the SAFY model are found to be reasonably well correlated with those determined by satellite measurements (NDVI), the grain yields are nevertheless underestimated. © 2014 © 2014 Taylor & Francis.
引用
收藏
页码:1004 / 1028
页数:25
相关论文
共 50 条
  • [31] Climate variability and its effect on normalized difference vegetation index (NDVI) using remote sensing in semi-arid area
    Dorsaf Fayech
    Jamila Tarhouni
    Modeling Earth Systems and Environment, 2021, 7 : 1667 - 1682
  • [32] Mapping waterholes and testing for aridity using a remote sensing water index in a southern African semi-arid wildlife area
    Dzinotizei, Zorodzai
    Murwira, Amon
    Zengeya, Fadzai M.
    Guerrini, Laure
    GEOCARTO INTERNATIONAL, 2018, 33 (11) : 1268 - 1280
  • [33] Remote sensing of land cover change dynamics in mountainous catchments and semi-arid environments: a review
    Yono, Anothando
    Mokua, Retang Anna
    Dube, Timothy
    GEOCARTO INTERNATIONAL, 2025, 40 (01)
  • [34] Spatiotemporal monitoring of surface soil moisture using optical remote sensing data: a case study in a semi-arid area
    Khellouk, Rida
    Barakat, Ahmed
    Boudhar, Abdelghani
    Hadria, Rachid
    Lionboui, Hayat
    El Jazouli, Aafaf
    Rais, Jamila
    El Baghdadi, Mohamed
    Benabdelouahab, Tarik
    JOURNAL OF SPATIAL SCIENCE, 2020, 65 (03) : 481 - 499
  • [35] Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region
    Jalilvand, Ehsan
    Tajrishy, Masoud
    Hashemi, Sedigheh Alsadat Ghazi Zadeh
    Brocca, Luca
    REMOTE SENSING OF ENVIRONMENT, 2019, 231
  • [36] Irrigation demand forecasting using remote sensing and meteorological data in semi-arid regions
    Ullah, Kaleem
    Hafeez, Mohsin
    GRACE, REMOTE SENSING AND GROUND-BASED METHODS IN MULTI-SCALE HYDROLOGY, 2011, 343 : 157 - 162
  • [37] Derivation of biomass information for semi-arid areas using remote-sensing data
    Eisfelder, Christina
    Kuenzer, Claudia
    Dech, Stefan
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2012, 33 (09) : 2937 - 2984
  • [38] Using Remote Sensing to Quantify Vegetation Change and Ecological Resilience in a Semi-Arid System
    Cui, Xia
    Gibbes, Cerian
    Southworth, Jane
    Waylen, Peter
    LAND, 2013, 2 (02) : 108 - 130
  • [39] Estimating Daily Reference Evapotranspiration in a Semi-Arid Region Using Remote Sensing Data
    Najmaddin, Peshawa M.
    Whelan, Mick J.
    Balzter, Heiko
    REMOTE SENSING, 2017, 9 (08)
  • [40] Estimation of Vegetation Coverage in Semi-arid Sandy Land Based on Multivariate Statistical Modeling Using Remote Sensing Data
    Wei Chen
    Tetsuro Sakai
    Kazuyuki Moriya
    Lina Koyama
    Chunxiang Cao
    Environmental Modeling & Assessment, 2013, 18 : 547 - 558