Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe
Închide
Conţinutul numărului revistei
Articolul precedent
Articolul urmator
233 0
SM ISO690:2012
POTOPOVA, Vera; TRNKA, Miroslav; HAMOUZ, Pavel ; SOUKUP, Josef; CASTRAVEŢ, Tudor. Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe. In: Agricultural Water Management. 2020, nr. 30(236), p. 0. ISSN 0378-3774.
10.1016/j.agwat.2020.106168
EXPORT metadate:
Google Scholar
Crossref
CERIF

DataCite
Dublin Core
Agricultural Water Management
Numărul 30(236) / 2020 / ISSN 0378-3774

Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe


DOI: 10.1016/j.agwat.2020.106168
Pag. 0-0

Potopova Vera1, Trnka Miroslav23, Hamouz Pavel 1, Soukup Josef1, Castraveţ Tudor4
 
1 Czech University of Life Sciences in Prague,
2 Global Change Research Institute of the Czech Academy of Sciences,
3 Institute of Agrosystems and Bioclimatology, Mendel University in Brno,
4 Moldova Tiraspol State University
 
Disponibil în IBN: 8 mai 2020


Rezumat

Meteorological and agricultural information coupled with remote sensing observations has been used to assess the effectiveness of satellite-derived indices in yield estimations. The estimate yield models generated by both the regression (MLR) and Bayesian network (BBN) algorithms and their levels of predictive skill were assessed. The enhanced vegetation index (EVI2), soil water index (SWI), standardized precipitation evaporation index (SPEI) have been considered predictors for three rainfed crops (maize, sunflower and grapevine) grown in 37 districts in the Republic of Moldova (RM). We used the weekly EVI2, which was collected by MODIS instruments aboard the Terra satellite with a 250m × 250m spatial resolution and aggregated for each district during the 2000–2018 period. We also used the weekly SWI, which was collected from the ASCAT instruments with a 12 km x 12 km spatial resolution and aggregated for each district at the topsoil (0–40 cm; SWI-12) and the root-zone layer (0–100 cm; SWI-14) during 2000–2018. The multiscalar SPEI during 1951–2018 farming years proved to be a significant addition to the remote sensing indices and led to the development of a model that improved the yield assessment. The study also summarized (i) the optimal time window of satellite-derived SWIi and EVI2i for yield estimation, and (ii) the capability of remotely sensed indices for representing the spatio–temporal variations of agricultural droughts. We developed statistical soil-vegetation-atmosphere models to explore drought-related yield losses. The skill scores of the sunflower MLR and BBN models were higher than those for the maize and grape models and were able to estimate yields with reasonable accuracy and predictive power. The accurate estimation of maize, sunflower and grapevine yields was observed two months before the harvest (RMSE of ∼1.2 tha-1). Despite the fact that summer crops (maize, sunflower) are able to develop a root system that uses the entire root zone depth, however, the SWI-12 had the stronger correlation with crop yield, then SWI-14. This explains much better the fit between yields of the crops and SWI-12, which represents soil moisture anomaly in the key rooting layer of soil. In any case, all summer crops showed negative correlations with each of the remote sensing soil moisture indices in the early and middle of the growing season, with SWI-12 performing better than SWI-14. Based on the crop-specific soil moisture model, we found that topsoil moisture declines in the most drought-susceptible crop growth stages, which indicates that RM is a good candidate for studying drought persists as main driver of rainfed yield losses in the south-eastern Europe. 

Cuvinte-cheie
Bayesian neural network, EVI2 MODIS, Remotely sensed drought indices, SPEI climate-based drought index, Statistical modelling of risk, SWI-12 and S WI-14 ASCAT