The research team, led by Renival Almeida de Carvalho and José Luiz Rodrigues Torres, set out to test a hypothesis that had received surprisingly little attention in the precision agriculture literature: that crop phenology, the sequence of developmental stages a plant passes through, is a major driver of how well satellite data can predict both how much cane a field produces and how much recoverable sugar it contains. To do so, they worked across five commercial sugarcane fields totaling 98.1 hectares, all planted with the widely adopted CTC-04 variety and covering second- and third-ratoon crops on soils ranging from clayey to sandy. The fields sit roughly 800 meters above sea level in a tropical savanna climate classified as Aw under the Köppen–Geiger system, with about 1,500 millimeters of annual rainfall and a mean annual temperature of 21 degrees Celsius.

Field data collection was deliberately grounded in the realities of commercial production rather than experimental manipulation. No fertilizer rates, irrigation regimes, or management treatments were imposed; instead, the natural variability of the five fields, including differences in soil texture, crop age, canopy development, and yield potential, served as the source of variation the models had to capture. Using a geographic information system, the researchers distributed 51 georeferenced sampling points across the fields, each corresponding to a three-square-meter unit consisting of two one-meter rows of stalks spaced 1.5 meters apart. Every stalk within each unit was harvested and weighed with a digital scale accurate to 10 grams, allowing the team to compute total cane yield, expressed in tonnes per hectare, and net cane yield after stripping leaves and apical portions. Ten stalks per point were then sent to the Coruripe Plant laboratory for determination of total recoverable sugar, the industry-standard measure of technological quality used for commercial payment and harvest planning.

On the remote sensing side, the study harnessed two complementary satellite systems. Optical imagery came from the Sentinel-2 Level-2A surface reflectance collection, processed in Google Earth Engine with cloud and cloud-shadow pixels masked using the sensor’s quality assessment layers. From Sentinel-2’s 10-meter bands, the team calculated five vegetation indices: NDVI, EVI, NDRE, GNDVI, and VARI, the last of which uses only visible bands and therefore represents a potential low-cost alternative built on RGB imagery alone. Radar observations came from the ALOS/PALSAR-2 sensor operating in ScanSAR mode at roughly 25-meter resolution, providing HH and HV polarization backscatter and their difference, metrics sensitive to canopy structure, biomass, and water status and, crucially, unaffected by cloud cover. Images were acquired at two contrasting phenological stages: February 2023, during active vegetative development characterized by rapid canopy expansion and biomass accumulation, and the pre-harvest maturation window between May and July 2023, when growth slows and sucrose accumulates in the stalks.

The agronomic data revealed just how heterogeneous commercial sugarcane production can be. Mean total cane yield across the five fields ranged from 82.46 to 130.28 tonnes per hectare, while net cane yield spanned 70.66 to 100.64 tonnes per hectare. At the level of individual sampling points, total cane yield varied from 71.00 to a remarkable 252.50 tonnes per hectare, with coefficients of variation of 29.49 percent for total yield and 28.76 percent for net yield, figures that underscore why conventional field-based assessment struggles to capture within-field variability. Total recoverable sugar, by contrast, was far more stable, averaging 163.63 kilograms per tonne with a coefficient of variation of only 6.60 percent, a difference that would prove important for interpreting the modeling results.

Using multiple linear regression with best-subset predictor selection, variance inflation factor screening for multicollinearity, and leave-one-out cross-validation, the team fitted separate models for each response variable at each phenological stage. The results delivered a clear message about timing. For total cane yield, the best model, based on NDVI acquired during vegetative development, explained 60.3 percent of the observed variation, with a root mean square error of 20.64 tonnes per hectare and a mean absolute percentage error of 18.17 percent. The corresponding pre-harvest NDVI model managed only an R² of 0.401, with the error climbing to 24.99 tonnes per hectare. The same pattern held for net cane yield: the vegetative-stage NDVI model reached an R² of 0.548 with an RMSE of 18.24 tonnes per hectare, whereas the best pre-harvest models fell to R² values between 0.404 and 0.474 with larger errors. In other words, the same satellite, the same index, and the same fields produced meaningfully better predictions simply because the images were captured while the crop was actively growing.

The physiological explanation lies in what the canopy is doing during each stage. During vegetative development, rapid leaf area expansion, high chlorophyll content, and intense photosynthetic activity strengthen the link between spectral reflectance and biomass, allowing indices like NDVI to track the canopy traits most closely tied to final stalk yield. By the pre-harvest stage, the picture becomes muddier: dense canopies push NDVI toward its well-known saturation point, where additional biomass produces diminishing changes in red and near-infrared reflectance, while senescent leaves and accumulated crop residues further decouple canopy reflectance from stalk mass. Interestingly, the RGB-based VARI index, though generally outperformed by the near-infrared indices, still produced significant yield models, suggesting that freely available visible-band imagery could serve as an accessible fallback when multispectral data are unavailable, even if it remains more vulnerable to illumination conditions and soil background effects.

Total recoverable sugar told a strikingly different story. Unlike yield, sugar concentration is governed by internal biochemical processes, including carbon assimilation, assimilate translocation, respiration, sink strength, and the partitioning of photoassimilates among plant organs, processes only indirectly visible to a satellite measuring canopy reflectance. Accordingly, TRS models showed moderate coefficients of determination, around 0.550 during vegetative development using NDVI alone and 0.504 during pre-harvest using a combined NDRE plus NDVI model. Yet the prediction errors were remarkably low, with mean absolute percentage errors below 4 percent at both stages, and the pre-harvest model achieving the lowest RMSE at 7.19 kilograms per tonne. The authors emphasize that this contrast between moderate R² values and low errors illustrates why goodness-of-fit and error metrics must be read together: because TRS varies within a narrow range, a model of moderate explanatory power can still deliver operationally accurate estimates. The success of the NDRE and NDVI combination at pre-harvest likely reflects the red-edge band’s resistance to saturation and its sensitivity to chlorophyll status in dense canopies, capturing physiological conditions associated with sucrose accumulation that NDVI alone misses. Notably, RGB-only indices failed to produce significant TRS models, confirming that visible bands alone cannot detect the biochemical processes behind sugar content.

Radar metrics, despite faithfully following the seasonal arc of crop development, delivered weaker predictive relationships than the optical indices, a limitation the authors attribute to SAR backscatter’s entanglement with vegetation architecture, water content, soil moisture, surface roughness, incidence angle, and sensor configuration, as well as its own saturation behavior in high-biomass crops. Still, the team argues that SAR remains essential in tropical sugarcane regions where persistent cloudiness can blind optical sensors precisely during critical windows, and that future monitoring systems will benefit from integrating optical and radar data streams rather than choosing between them. The authors are candid about the study’s limits: validation relied on leave-one-out cross-validation without an independent external dataset, the work covered a single growing season, one farm, and one cultivar, and only interpretable linear models were tested, leaving comparisons with machine-learning approaches and the integration of soil, weather, and management data to future research. Even so, the core conclusion stands as a practical guide for the industry: successful satellite-based sugarcane monitoring demands not just the right sensor and the right index, but the right moment, with vegetative-stage imagery favoring yield forecasts and pre-harvest red-edge combinations favoring sugar quality estimates. As Brazil’s sugar and ethanol sectors push ever deeper into the Cerrado, aligning the satellite calendar with the crop’s internal clock may prove one of the cheapest accuracy upgrades available.

Subject of Research: Influence of crop phenology on the accuracy of satellite-based remote sensing predictions of sugarcane yield and total recoverable sugar in commercial fields of the Brazilian Cerrado.

Article Title: Crop phenology drives remote sensing prediction accuracy of sugarcane yield and quality in the Brazilian Cerrado

Article References: de Carvalho, R. A., Torres, J. L. R., Loss, A., Abreu, D., da Silva Vieira, D. M., & Pereira, D. P. (2026). Crop phenology drives remote sensing prediction accuracy of sugarcane yield and quality in the Brazilian Cerrado. Smart Agricultural Technology, 15, Article 102464. https://doi.org/10.1016/j.atech.2026.102464

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102464

Keywords: sugarcane, remote sensing, crop phenology, NDVI, Sentinel-2, Brazilian Cerrado, yield prediction, total recoverable sugar, precision agriculture, SAR, vegetation indices, Smart Agricultural Technology

The Analytic Hierarchy Process, or AHP, is a structured multi-criteria decision-making technique introduced by Thomas Saaty in which complex problems are decomposed into a hierarchy of criteria, and pairwise comparisons convert expert judgment into numerical weights. In flood hazard mapping, AHP is typically applied by scoring a set of terrain and hydrological factors, weighting each according to its perceived influence on inundation, and combining the weighted layers into a single hazard index. The method is attractive because it is transparent, computationally inexpensive, and does not demand long historical flood records, which makes it a popular choice in developing regions where dense gauge networks are simply unavailable.

The research team, led by Sanaz Moghim together with Alireza Farmahini Farahani and Reza Rajabi, built their hazard maps using eight criteria: distance to river, slope, aspect, curvature, flow accumulation, drainage density, land use and land cover, and elevation. Each criterion was reclassified into classes ranked by relative flood influence, and the AHP weighting scheme assigned the overall importance of each layer. Two alternative maps were then produced. The first relied on a pre-existing stream network, the kind of digitized hydrography that is commonly available in national and international geospatial databases. The second replaced that network with one extracted from the Normalized Difference Water Index, a spectral index computed from optical satellite imagery that highlights surface water by contrasting near-infrared and visible reflectance.

The choice of river network matters because distance to river is one of the strongest controls on flood hazard in the AHP framework. Pixels close to a stream channel receive the highest hazard scores, and the scores decay with distance. If the underlying stream network is incomplete, generalized, or outdated, every downstream calculation inherits those errors. In flat, marshy lowlands such as those of Dasht-e Azadegan and Hoveyzeh, where subtle topographic differences and seasonal wetlands complicate conventional hydrographic mapping, a satellite-derived view of where water actually accumulates could plausibly represent flood dynamics better than a legacy database layer.

To find out whether this is true in practice, the researchers needed an independent benchmark, and they found it in the 2019 flood that inundated large parts of Khuzestan Province. The extent of that flood was mapped from Sentinel-1 synthetic aperture radar, or SAR, observations. SAR is uniquely valuable for flood mapping because its microwave signal penetrates cloud cover and can be acquired day or night, and because smooth open water reflects the radar energy away from the sensor, appearing dark in the imagery in sharp contrast to the rougher surrounding land. This made it possible to build an objective record of where floodwater actually stood, against which the modeled hazard maps could be judged.

The validation employed receiver operating characteristic analysis, a statistical technique that evaluates how well a continuous hazard index separates flooded from non-flooded locations. The area under the ROC curve, or AUC, ranges from 0.5, equivalent to random guessing, to 1.0, indicating perfect discrimination. The results were clear. The NDWI-derived hazard map achieved an AUC of 0.88, indicating strong agreement with the observed 2019 inundation, while the map built on the pre-existing stream network reached an AUC of 0.81. In practical terms, the satellite-derived river network pushed the model’s discriminatory power noticeably higher, suggesting that even a modest change in one input layer can cascade into a substantially more trustworthy hazard product.

Beyond the headline comparison, the team conducted a sensitivity analysis to determine which of the eight criteria actually drove the classification. Flow accumulation and slope emerged as the two features with the strongest effect on hazard classification, a finding consistent with the physical intuition that water converges in low-lying areas with gentle gradients. By contrast, aspect and curvature had minimal influence on the final hazard pattern. This kind of sensitivity information is valuable for practitioners because it indicates where investing in higher-quality data pays off and where simpler or coarser inputs are unlikely to compromise the result, an important consideration in data-limited settings where every dataset must be weighed against acquisition and processing costs.

The implications extend well beyond two counties in southwestern Iran. Rentschler and colleagues estimated in a 2022 Nature Communications analysis that flood exposure and poverty overlap extensively across 188 countries, and global assessments of future river flood risk have repeatedly identified data-poor regions as those where hazard information is weakest precisely where vulnerability is highest. The Iranian study offers a template for such settings: freely available Sentinel imagery, a transparent AHP weighting procedure, and validation against openly accessible SAR flood observations together produce a defensible hazard map without requiring expensive field campaigns or proprietary models. The data used in the study are publicly available from sources including the U.S. Geological Survey, the Copernicus Data Space Ecosystem, and Esri, underscoring the reproducibility of the approach.

There are, of course, caveats worth noting. The study validates against a single flood event, and the NDWI is sensitive to turbid water, aquatic vegetation, and cloud cover, which is precisely why SAR served as the reference rather than another optical product. AHP weights also retain a subjective element inherited from expert pairwise comparisons, although sensitivity analysis partially mitigates this by revealing which weights matter most. Future work could extend the validation to multiple flood events, test alternative water indices, or compare the enhanced AHP framework against machine learning classifiers that have shown strong performance in flood susceptibility studies. Nevertheless, the quantitative gain from 0.81 to 0.88 AUC provides concrete evidence that satellite-derived inputs can strengthen a decades-old decision-support method.

For flood managers and policymakers in Khuzestan and analogous lowland regions worldwide, the message is direct: hazard maps need not wait for perfect ground data. By letting satellites describe where water flows and pools, and by validating openly against observed floods, planners gain a more reliable basis for zoning, early warning, and infrastructure investment. As climate change intensifies the hydrological cycle and extreme rainfall events grow more frequent, the ability to update flood hazard information rapidly and cheaply from orbit may prove one of the most consequential tools in the disaster-risk-reduction toolkit, and this study shows exactly how such a workflow performs under real-world scrutiny.

Subject of Research: AHP-based flood hazard mapping enhanced by satellite remote sensing and validated against SAR-observed flood extent in Iran

Article Title: AHP-based Flood Hazard Mapping Enhanced by Remote Sensing

Article References: Moghim, S., Farmahini Farahani, A., & Rajabi, R. (2026). AHP-based Flood Hazard Mapping Enhanced by Remote Sensing. Water Resources Management, 40(11), Article 521. https://doi.org/10.1007/s11269-026-04879-7

Image Credits: AI Generated

DOI: 10.1007/s11269-026-04879-7

Keywords: flood hazard mapping, AHP, remote sensing, NDWI, Sentinel-1, SAR, ROC-AUC, Khuzestan Province, flow accumulation, drainage density, multi-criteria decision analysis, flood risk assessment