Celina Takemura

Remote sensing parameters were used along with weather data to assess the large-scale sugarcane water balance in Northeast Brazil, using a dataset from 2007 to 2024. Rainfall (P) was spatially quantified from interpolated pluviometer data and actual evapotranspiration (ET) was estimated using the SAFER algorithm. Considering the sugarcane crop areas for each year, the mean annual P varied from 8555 million cubic meters in 2009 to 3172 million cubic meters in 2021. Regarding ET, it varied from 5869 million cubic meters in 2007 to 3375 million cubic meters in 2017. The assessments showed time lags of around two months between rainfall water supplies and root zone moisture levels and highlighted the use of evaporative fraction (E_f). Anomalies in P and ET were detected through their standardized indices by comparing the results for the years 2021 to 2024 with the long-term values from 2007 to each of these years, showing that the rainfall water supplies and water fluxes may vary along sugarcane phases and years. It can be concluded that large-scale water balance assessments can potentially serve as the basis for public policies aimed at sustainable crop management and rational expansion in areas with environmental suitability.

MODIS satellite images were used together weather data for large-scale water balance assessments in rainfed sugarcane crops, with a long-term data set from 2007 to 2024 in Northeast Brazil. Precipitation (P) was spatially quantified from interpolated pluviometer data and actual evapotranspiration (ET) estimated by applying the SAFER (Simple Algorithm for Evapotranspiration Retrieving). Considering the sugarcane cropped areas for each year, the mean annual P ranged from 3,172 million cubic meters in 2021 to 8,555 million cubic meters in 2009, while for ET this range was from 3,375 million cubic meters in 2017 to 5,869 million cubic meters in 2007. The assessments showed gaps between rainfall water supplies and the root-zone moisture levels, highlighting the evaporative fraction (Ef), i.e. the ratio of ET to reference evapotranspiration (ET0), as the best root-zone moisture indicator, which showed benefits of supplementary irrigation during the transition from grand growth to maturation stages. It was demonstrated that the assessments of large-scale sugar cane water balance using long-term series of data has potential to subsidize public policies which aim the sustainable crop management as well as for its rational expansion in areas with environmental aptitude under the actual climate and land-use changes scenarios.

Aerial images taken from a camera onboard a drone were used for corn water productivity (WP) assessments in Northeast Brazil, by using a Mapir camera together with weather, biomass and productivity (Pr) data, modelling actual evapotranspiration (ET_a) and biomass production (BIO). The effects of different nitrogen (N) fertilization cover levels were analyzed showing that there were significant differences on WP components from the emergency (E) to physiological maturation (PM) crop stages (CS), however, stabilizing at N at 200 kg ha^− 1. Above this level, farmers should lose money increasing risks of negative environmental impacts with more N leaching to the water table. The evaporative fraction (ET_f), i.e., the ratio of ET_a to reference evapotranspiration (ET₀), taken as a root-zone moisture indicator, presented the best correlation with BIO and Pr, but the fraction of the absorbed photosynthetically active radiation (f_p), representing soil cover by the canopies, is also important through photosynthetic activity. Comparing Pr and the BIO, the highest determination coefficient at V6 (pant with six leaves), confirming that the potential of corn yield is already defined at this CS. The models applied showed potential for monitoring vegetation and water conditions in corn crops, allowing rational fertilization practices while maintained yield at sustainable water use, with the possibility for replication of the methods in other environmental conditions.

O MonitoraOeste é um aplicativo desenvolvido para monitorar e emitir alertas sobre o avanço da ferrugem da soja e da mancha de ramulária do algodão em lavouras do Oeste da Bahia. Os alertas emitidos apontam os locais de ocorrência, trazendo informações atualizadas em tempo real e revelam se as condições climáticas são favoráveis para o desenvolvimento de pragas e doenças, com informações sobre a presença e a dispersão de esporos. No app mobile também estão disponibilizados índices agrometeorológicos, como evapotranspiração, biomassa, NDVI e eficiência no uso da água. Com o MonitoraOeste, os produtores agrícolas e os tomadores de decisão tem acesso a informações complexas, de forma simplificada. A tecnologia auxilia na segurança fitossanitária das lavouras de soja e algodão da região, possibilitando a redução do custo de produção e o impacto ambiental das culturas, considerando as condições climáticas e permitindo um melhor monitoramento das lavouras durante seu desenvolvimento.

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 (ET_f), i.e., the ET to reference evapotranspiration (ET₀) ratio. However, there was a gap between ET_f 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 ET_f (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.

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 the Brazilian biomes. The MODIS MOD13Q1 reflectance product and gridded weather data were used for the year 2016. The analyzed biomes were Amazon, Caatinga, Cerrado, Pantanal, Atlantic Forest, and Pampa. Significant differences on 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 along the year were for the wettest Amazon, Atlantic Forest, and Pampa biomes, when compared with the driest Caatinga biome. Rainfall distribution along the year affected the magnitude of the evaporative fraction (ET_f), i.e, the ratio of ET to reference evapotranspiration (ET₀), however there was a gap between ET_f and WB, what can be related to the time needed for recovering the good soil moisture conditions after the rainy seasons. For some biomes, BIO was more related to the levels of absorbed photosynthetically active radiation (Amazon, Atlantic Forest, and Pampa), while for others BIO followed more the soil moisture levels, represented by ET_f (Caatinga, Cerrado, and Pantanal). The large-scale modelling presented suitability for monitoring environmental indicators, opening the room to detect anomalies for specific periods along the year by using historical images and weather data, with great potential to subsidize public policies regarding the management and conservation of the natural resources and possibility for replication of the methods in other countries.

This paper aimed to support the rational crop expansion in agricultural-growing regions. MODIS satellite images are used together with gridded weather data to model biometeorological parameters at the Fruit Circuit region, state of São Paulo, Southeast Brazil. This region has experienced some cases of drought, while arising rainfall water excess in some periods, demanding large-scale water and energy balance studies to subsidize water resource policies. The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied together with the radiation-use efficiency (RUE) model for biometeorological index assessments. The highest latent heat fluxes (λE), above 8.0 MJ m⁻² d⁻¹, at the end of the year, coincide with the progressive increases on both rainfall and global solar radiation (R_G) levels, and drop to below 5.0 MJ m⁻² d⁻¹ in the middle of the year, during the driest conditions. The same tendencies along the year are verified for sensible heat fluxes (H), for which mean pixel values are above 3.5 MJ m⁻² d⁻¹ at the end of the year. On the one hand, the highest values for water productivity (WP), which is considered the ratio of actual evapotranspiration (ET) to biomass production (BIO), above 4.0 kg m⁻³, are verified in April, period under increasing BIO and low ET rates. On the other hand, the lowest WP values (below 2.0 kg m⁻³) occur between August and October, when BIO is low, and ET is high. Although the area featuring good WP levels under high precipitation (P), with rainfalls generally above ET, supplementary irrigation may benefit agriculture in some periods of the year. The results of the large-scale modeling showed applicability of the models for monitoring water and vegetation dynamics over 16-day timescale and at a 250-m spatial resolution in areas experiencing climate and land-use changes by combining climate data and MODIS images. Application of these tools enables to indicate the best options for expanding the agriculture activities, being of great potential for rational natural resources management, in regions under environmental vulnerability conditions.