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.

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.

Biomass production (BIO) and its anomalies were modeled using MODIS satellite images and gridded weather data to test an environmental monitoring system in the biomes Atlantic Forest (AF) and Caatinga (CT) within SEALBA, an agricultural growing region bordered by the states of Sergipe (SE), Alagoas (AL), and Bahia (BA), Northeast Brazil. Spatial and temporal variations on BIO between these biomes were strongly identified, with the annual long-term averages (2007–2023) for AF and CT of 78 ± 22 and 58 ± 17 kg ha−1 d−1, respectively. BIO anomalies were detected through its standardized indexes—STD (BIOSTD), comparing the results for the years from 2020 to 2023 with the long-term rates from 2007 to each of these years. The highest negative BIOSTD values were in 2023, but concentrated in CT, indicating periods with the lowest vegetation growth, regarding the long-term conditions from 2007 to 2023. The largest positive BIOSTD values were for the AF biome in 2022, evidencing the highest vegetative vigor in comparison with the long-term period 2007–2022. The proposed BIO monitoring system is important for environmental policies as they picture suitable periods and places for agricultural and forestry explorations, allowing sustainable managements under climate and land-use changes conditions, with possibilities for replication of the methods in other environmental conditions.