Coffee demand continues to rise, while producing countries face increasing challenges and yield losses due to climate change. In response, farmers are adopting agricultural practices capable of boosting productivity. However, these practices increase intercrop variability, making coffee mapping more challenging. In this study, a novel approach is proposed to identify coffee cultivation considering four phenological stages: planting (PL), producing (PR), skeleton pruning (SK), and renovation with stumping (ST). A hierarchical classification framework was designed to isolate coffee pixels and identify their respective stages in one of Brazil’s most important coffee-producing regions. A dense time series of multispectral bands, spectral indices, and texture metrics derived from Harmonized Landsat Sentinel-2 (HLS) imagery, with an average revisit time of ~3 days, was employed. This data was combined with an ensemble learning approach based on decision-tree algorithms, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The results achieved unprecedented sensitivity and specificity for coffee plantation detection with RF, consistently exceeding 95%. The classification of coffee phenological stages showed balanced accuracies of 77% (ST) and from 93% to 95% for the other classes. These findings are promising and provide a scalable framework to monitor climate-resilient coffee management practices.

Mapping banana and peach palm in heterogeneous landscapes remains challenging due to spatial heterogeneity, spectral similarities between crops and native vegetation, and persistent cloud cover. This study focused on the municipality of Jacupiranga, located within the Ribeira Valley region and surrounded by the Atlantic Forest, which is home to one of Brazil’s largest remaining continuous forest areas. More than 99% of Jacupiranga’s agricultural output in the 21st century came from bananas (Musa spp.) and peach palms (Bactris gasipaes), underscoring the importance of perennial crops to the local economy and traditional communities. Using a time series of vegetation indices from Sentinel-2 imagery combined with field and remote data, we used a hierarchical classification method to map where these two crops are cultivated. The Random Forest classifier fed with 10 m resolution images enabled the detection of intricate agricultural mosaics that are typical of family farming systems and improved class separability between perennial and non-perennial crops and banana and peach palm. These results show how combining geographic information systems, data analysis, and remote sensing can improve digital agriculture, rural management, and sustainable agricultural development in socio-environmentally important areas.

Apple orchards are increasingly adopting anti-hail nets to mitigate climate risks; however, these structures alter canopy reflectance and pose challenges for remote sensing. This study presents an operational framework to map apple orchards under different netting conditions in Vacaria, Brazil. Multisensor surface reflectance data from Sentinel-2 and Harmonized Landsat and Sentinel-2 were used to generate dense spectral index time series combined with field observations. Five spectral indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and Bare Soil Index (BSI), were evaluated individually and in combination within a hierarchical classification framework. Random Forest (RF) and one-dimensional convolutional neural networks (1DCNN) were applied as complementary machine learning approaches. RF showed more stable performance across hierarchical levels, while indices contributed differently depending on scale: BSI and NDVI were more effective at broader levels, whereas EVI and SAVI were critical for discriminating net colors. To our knowledge, this is the first study applying multisensor time series and machine learning to map anti-hail net systems in apple orchards.

The monitoring of perennial and annual crops requires different analytical approaches due to their contrasting phenological dynamics and management practices. This study investigates the temporal behavior of the Normalized Difference Vegetation Index (NDVI) derived from Harmonized Landsat and Sentinel-2 (HLS) imagery to characterize apple, grape, soybean, and maize crops in Vacaria, Southern Brazil, between January 2024 and April 2025. NDVI time series were extracted from cloud-free HLS observations and analyzed using raw, interpolated, and Savitzky–Golay, smoothed data, supported by field reference points collected with the AgroTag application. Distinct NDVI temporal patterns were observed, with apple and grape showing higher stability and soybean and maize exhibiting stronger seasonal variability. Descriptive statistics derived from 112 observation dates confirmed these differences, highlighting the ability of HLS-based NDVI time series to capture crop-specific phenological patterns at the municipal scale. Complementary analysis using the SATVeg platform demonstrated consistency in long-term vegetation trends while evidencing scale limitations of coarse-resolution data for small perennial plots. Overall, the findings demonstrate that the NDVI enables robust monitoring of mixed agricultural landscapes, with complementary spatial resolutions and analytical tools enhancing crop-specific phenological analysis.

Hail events represent a major source of economic risk in apple production, leading to significant losses in yield and marketable output. Although anti-hail net systems are increasingly adopted as a mitigation strategy, evidence on their economic viability remains limited, especially in emerging regions. This study examines the economic dimensions of anti-hail net adoption in apple orchards in Vacaria, southern Brazil, by integrating remote sensing data, official loss records, and producer interviews. Multitemporal Sentinel-2 imagery was used to map the expansion of net-covered areas between 2016 and 2026. Economic loss data from eight recorded hail events, obtained from official government sources, were analyzed, along with apple area and production data. Exploratory interviews provided insights into installation costs, production losses, and perceived benefits. Results indicate a clear expansion of anti-hail nets associated with substantial hail losses. Producers reported pre-adoption losses of 30–90%, installation costs of Brazilian Real (BRL) 40,000–55,000 per hectare, and estimated payback periods of 3–4 years. These findings highlight the value of integrating remote sensing, official disaster records, and producer knowledge to support exploratory climate adaptation and economic assessments.