Large-Scale Spatial Modeling of Crop Coefficient and Biomass Production in Agroecosystems in Southeast Brazil

Nenhuma Miniatura disponível

Data

2018-12-01

Orientador

Coorientador

Pós-graduação

Curso de graduação

Título da Revista

ISSN da Revista

Título de Volume

Editor

Mdpi

Tipo

Artigo

Direito de acesso

Acesso abertoAcesso Aberto

Resumo

Sentinel-2 images at 10-m resolution were used for modeling crop coefficients and biomass production with the application of the so-called SAFER (Simple Algorithm for Evapotranspiration Retrieving) and Monteith model for biomass production in an area nearby the city of aguas de Santa Barbara, in the central-western part of SAo Paulo State, Brazil, which presents a vast agricultural landscape mosaic, to analyze the effects of the end of the recent ENSO's (El Nino-Southern Oscillation) most active period (2016/2017) and its posteriori effects on vegetation (until early 2018). Surface albedo, temperature, net radiation, and NDVI (Normalized Difference Vegetation Index) from the main land uses were extracted to process microclimatic comparisons. Crop coefficient (dimensionless) and biomass production (kgha(-1)day(-1)) ranges for the period studied were 0.92-1.35 and 22-104 kgha(-1)day(-1) (in the area occupied by sugarcane crop), 0.56-0.94 and 15-73 kgha(-1)day(-1) (pasture), 1.17-1.56 and 25-210 kgha(-1)day(-1) (silviculture), and 1.05-1.36 and 30-134 kgha(-1)day(-1) (forest). According to the spatial and temporal consistencies, and after comparison with previous point and large-scale studies with similar climatic and thermal conditions, the SAFER and Monteith modelsshowed the ability to quantify and differentiate the large-scale crop coefficients and biomass production of different land uses in the southeast Brazil region. The SAFER algorithm with Sentinel-2 images obtained crop coefficients that indicated plant growth stages and local thermohydrological conditions at a 10-m resolution. The results are important for land use, crop yield and reforestation planning, and for water management plans for actual and future water demand scenarios, and this methodology is useful for monitoring rural and water parameters, and for precision agriculture applications.

Descrição

Idioma

Inglês

Como citar

Horticulturae. Basel: Mdpi, v. 4, n. 4, 20 p., 2018.

Itens relacionados

Coleções