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Remote sensing environmental indicators for monitoring spatial and temporal dynamics of weather and vegetation conditions: applications for Brazilian biomes

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Abstract

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm and the radiation use efficiency (RUE) model were coupled to test large-scale remote sensing environmental indicators in Brazilian biomes. MODIS MOD13Q1 reflectance product and gridded weather data for the year 2016 were used to demonstrate the suitability of the algorithm to monitor the dynamics of environmental remote sensing indicators along a year in the Brazilian biomes, Amazon, Caatinga, Cerrado, Pantanal, Atlantic Forest, and Pampa. Significant spatial and temporal variations in precipitation (P), actual evapotranspiration (ET), and biomass production (BIO) yielded differences on water balance (WB = P−ET) and water productivity (WP = ET/BIO). The highest WB and WP differences were detected in the wettest biomes, Amazon, Atlantic Forest, and Pampa, when compared with the driest biome, Caatinga. Rainfall distribution along the year affected the magnitude of the evaporative fraction (ETf), i.e., the ET to reference evapotranspiration (ET0) ratio. However, there was a gap between ETf and WB, which may be related to the time needed for recovering good soil moisture conditions after rainfalls. For some biomes, BIO related most to the levels of absorbed photosynthetically active radiation (Amazon and Atlantic Forest), while for others, BIO followed most the soil moisture levels, depicted by ETf (Caatinga, Cerrado, Pantanal, and Pampa). The large-scale modeling showed suitability for monitoring the water and vegetation conditions, making way to detect anomalies for specific periods along the year by using historical images and weather data, with strong potential to support public policies for management and conservation of natural resources and with possibilities for replication of the methods in other countries.

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Data availability

Raw weather data were from the Brazilian National Meteorological Institution (INMET ˗ https://www.gov.br/agricultura/pt-br/assuntos/inmet), shape files used are available by the Statistical and Geographic Brazilian Institute (IBGE ˗ www.ibge.gov.br), and the MODIS products were downloaded from the EARTHDATA AppEEARS’s site (https://lpdaacsvc.cr.usgs.gov/appeears/). Derived data supporting the findings of this study are available from the corresponding author on request.

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Acknowledgements

To National Meteorological Institute (INMET) for weather data availability.

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Authors and Affiliations

  1. Federal University of Sergipe (UFS), São Cristóvão, SE, Brazil

    Antônio Teixeira

  2. Embrapa Territory, Campinas, SP, Brazil

    Janice Leivas, Celina Takemura & Edlene Garçon

  3. Embrapa Environment, Jaguariuna, SP, Brazil

    Gustavo Bayma

  4. Federal University of Sergipe (UFS), São Cristóvão, SE, Brazil

    Inajá Sousa & Franzone Farias

  5. University of Campinas (UNICAMP), Campinas, SP, Brazil

    Cesar Silva

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  1. Antônio Teixeira
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  2. Janice Leivas
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Contributions

Antônio Teixeira was responsible for running the models, conceptualizations, energy, water, and carbon balance assessments and writing the manuscript, designing of figures, result analyses, software resources, and supervision. Janice Leivas oversaw running of scripts, download and processing MODIS images MODIS images, and formatting of the weather data, methodology, data curation, and editing of the manuscript. Celina Takemura helped on downloading and processing MODIS images, weather data processing, and result analyses. Gustavo Bayma acted on downloading/processing MODIS images and weather data. Edlene Garçon acted on downloading/processing MODIS images and weather data. Inajá Sousa acted on downloading/processing MODIS images and weather data. Franzone Farias acted on processing MODIS images and weather data. Cesar Silva helped with statistical analyzes.

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Correspondence to Antônio Teixeira.

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Teixeira, A., Leivas, J., Takemura, C. et al. Remote sensing environmental indicators for monitoring spatial and temporal dynamics of weather and vegetation conditions: applications for Brazilian biomes. Environ Monit Assess 195, 944 (2023). https://doi.org/10.1007/s10661-023-11560-8

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