The scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent’s hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented ecosystems that are highly threatened by climate change and human activity. Maps of LULC in drylands are, thus, essential in order to investigate their vulnerability to both natural and anthropogenic impacts. This paper comprehensively reviewed existing mapping initiatives of South America’s drylands to discuss the main knowledge gaps, as well as central methodological trends and challenges, for advancing our understanding of LULC dynamics in these fragile ecosystems. Our review centered on five essential aspects of remote-sensing-based LULC mapping: scale, datasets, classification techniques, number of classes (legends), and validation protocols. The results indicated that the Landsat sensor dataset was the most frequently used, followed by AVHRR and MODIS, and no studies used recently available high-resolution satellite sensors. Machine learning algorithms emerged as a broadly employed methodology for land cover classification in South America. Still, such advancement in classification methods did not yet reflect in the upsurge of detailed mapping of dryland vegetation types and functional groups. Among the 23 mapping initiatives, the number of LULC classes in their respective legends varied from 6 to 39, with 1 to 14 classes representing drylands. Validation protocols included fieldwork and automatic processes with sampling strategies ranging from solely random to stratified approaches. Finally, we discussed the opportunities and challenges for advancing research on desertification, climate change, fire mapping, and the resilience of dryland populations. By and large, multi-level studies for dryland vegetation mapping are still lacking.

Leaf phenology is key to ecosystem functioning by regulating carbon, water, and energy fluxes and influencing vegetation productivity. Yet, detecting land surface phenology (LSP) in drylands using remote sensing remains particularly challenging due to sparse and heterogeneous vegetation cover, high spatiotemporal variability, and complex spectral signals. Unlike the Northern Hemisphere, these challenges are further compounded in the Southern Hemisphere (SH), where several regions experience year-round moderate temperatures. When combined with irregular rainfall, this leads to highly variable vegetation activity throughout the year. However, LSP dynamics in the SH remain poorly understood. This study presents a review of remote sensing-based phenology research in drylands, integrating (i) a synthesis of global methodological advances and (ii) a systematic analysis of peer-reviewed studies published from 2015 through April 2025 focused on SH drylands. This review reveals a research landscape still dominated by conventional vegetation indices (e.g., NDVI) and moderate-spatial-resolution sensors (e.g., MODIS), though a gradual shift toward higher-resolution sensors such as PlanetScope and Sentinel-2 has emerged since 2020. Despite the widespread use of start- and end-of-season metrics, their accuracy varies greatly, especially in heterogeneous landscapes. Yet, advanced products such as solar-induced chlorophyll fluorescence or the fraction of absorbed photosynthetically active radiation were rarely employed. Gaps remain in the representation of hyperarid zones, grass- and shrub-dominated landscapes, and large regions of Africa and South America. Our findings highlight the need for multi-sensor approaches and expanded field validation to improve phenological assessments in dryland environments. The accurate differentiation of vegetation responses in LSP is essential not only for refining phenological metrics but also for enabling more realistic assessments of ecosystem functioning in the context of climate change and its impact on vegetation dynamics.

The Caatinga biome represents around 10% of the Brazilian territory, and it has an estimated population of 28 million inhabitants. Its tree-shrub vegetation, adapted to semi-arid conditions, plays a fundamental role in maintaining the hydrological balance, feeding the energy matrix, and generating revenues for Brazil. Despite its importance, the Caatinga is one of the most neglected biomes by the scientific community. Therefore, this review article aims to present elements that contribute to updating the state-of-the-art on the use of optical Earth observation data in the conservation of Caatinga vegetation, based on the identification of mapping initiatives at different scales that consider the biome. To this end, this study carried out a systematic bibliographic review in which the main focus was the characterization of orbital sensors, image classification techniques, land-use and land-cover classes, validation strategies, and the time interval defined by each mapping initiative. This detailed overview allowed us to assess the degree of usability and reliability of the existing products. Therefore, this study looks to open up possibilities to fill current scientific gaps that need further investigation regarding the role of optical Earth observation data in mapping the vegetation of the Caatinga and subsidizing resources for new initiatives, restoration actions, and hence the improvement of public policies favoring the conservation and sustainable use of the Caatinga’s resources.

The Cerrado is the second largest Brazilian biome, being recognized as the most biodiverse savanna in the world. Beginning in 1970, the land use and land cover dynamics in the biome have been characterized by extensive agricultural activities, resulting in historically higher deforestation rates than the Amazon biome. This scenario reinforces the need to investigate the methodology initiatives of the Cerrado’s vegetation cover, aiming to identify the gaps and challenges that still exist for the advancement of scientific knowledge in the Remote Sensing (RS) field. To this end, this review article identified 15 initiatives that mapped the biome’s vegetation at different scales, periods, and legend levels. The first was the Radam/RadamBrasil project, in the 1970s. However, it was not until the 2000s that Cerrado began to have more visibility, with the emergence of larger and more targeted initiatives for the biome (PROBIO, Conservation International). Recently, new initiatives (MapBiomas, PRODES) have stood out for incorporating different methodologies that have followed the computational evolution of the Remote Sensing techniques. The research carried out in this article identified that the discrimination of different land cover types in the biome is still one of the main challenges to be overcome, especially regarding the non-forest phytophysiognomies as well as the land-use classes that are spectrally similar, such as pasture. This study aims to aggregate details of the main initiatives for mapping the Cerrado vegetation, as well as their methodologies, challenges encountered, such as the difficulty in discriminating its vegetation types, and further discussions and future prospects in the field of RS.

O objetivo desse trabalho foi analisar a influência da sazonalidade e da supressão vegetal no aumento de temperatura em grandes metrópoles, tais como São Paulo, Rio de Janeiro, Belo Horizonte e Brasília. Correlacionar esses parâmetros implica num melhor conhecimento dos impactos associados à expansão dessas cidades, bem como avaliar aspectos ambientais relacionados ao planejamento urbano das mesmas. Por meio dos dados do sensor OLI do Landsat 8 foram obtidos dados do índice CO2flux, o qual detecta o sequestro de carbono. Posteriormente utilizou-se dados do sensor TIRS do mesmo satélite, os quais foram calibrados para obter a temperatura de brilho. Os dois parâmetros foram correlacionados utilizando-se o Coeficiente de Kendall. De modo geral, os resultados tiveram ampla variação do CO2flux e temperatura ao se comparar áreas urbanizadas e não urbanizadas, sendo que a relação estabelecida entre os parâmetros é inversa. As cidades do Rio de Janeiro e São Paulo, respectivamente, apresentaram maior registro de temperatura e menor variação do sequestro de carbono em função da sazonalidade. A diferença sazonal entre os parâmetros para a cidade de Belo Horizonte foi mínima. Já para Brasília, a sazonalidade demonstrou ter uma forte influência, especialmente no comportamento do sequestro de carbono. Ao final, a correlação dos índices comprovou que a relação inversa entre o CO2flux e a temperatura ocorre devido a variação sazonal do balanço de radiação solar recebido e à capacidade fotossintética florestal.