Search Results (464)

The quality of Crocus sativus L. flowers, beyond their stigmas, is influenced by the presence of bioactive metabolites also in their floral bio-residues. Given the effect of climatic and soil variables on these bioactive compounds, the aim of this research was to develop an agroecological zoning of saffron crop areas within the Protected Designation of Origin (PDO) La Mancha region (Castilla-La Mancha, Spain) by integrating the floral metabolite content with climatic and soil variables. To achieve this, a total of 173 samples were collected during the 2022 and 2023 harvests and analyzed via RP-HPLC-DAD to determine crocins, picrocrocin, kaempferols, and anthocyanins. Two new indices, Cropi (crocins + picrocrocin) and Kaeman (kaempferols + anthocyanins), were defined to classify flowers into four quality categories (A–D). High-quality classifications (A and B) were consistently associated with plots grouped in the meteorological stations of Ontur, El Sanchón, and Bolaños, indicating favorable edaphoclimatic conditions and climatic parameters, such as moderate temperatures and reduced humidity, for metabolite biosynthesis. In contrast, plots included in the meteorological stations of Tarazona and Pedernoso were mostly assigned to lower categories (C and D). Spatial analysis using thematic maps revealed that areas with an intermediate carbonate content, less calcareous soils, and higher organic matter levels were linked to higher flower quality. These findings highlight the influence of soil characteristics and climate, with distinct seasonal contrasts, that positively influence metabolite synthesis and flower quality. Full article

Accurate land cover classification is vital for informed water resource management, especially in irrigation-dependent regions facing increased climate variability. Using fused multi-sensor remote sensing imagery from Landsat 8 and Sentinel-2, this study assesses the effectiveness of three machine learning classifiers: Random Forest (RF), Gradient Tree Boosting (GTB), and Naive Bayes (NB) in creating land cover maps for the Tisza-Körös Valley Irrigation System (TIKEVIR) in Hungary. Water bodies, built-up areas, forests, grasslands, and major crops were among the important land cover categories that were classified for the two agricultural seasons (2018 and 2022). RF performed consistently in 2022 and reached its best accuracy in 2018 (OA = 0.87, KC = 0.83, PI = 0.94). While NB’s performance in 2022 remained less consistent, GTB’s performance increased. The findings show that RF works effectively for generating accurate land cover data, providing useful information for regional monitoring, and assisting in water and environmental management decision-making. Full article

Water scarcity, exacerbated by climate change, population growth, and competing sectoral demands, poses a major threat to agricultural sustainability, particularly in irrigated regions such as the Nile Delta in Egypt. Addressing this challenge requires innovative approaches to evaluate irrigation performance despite the limitations in ground data availability. Traditional assessment methods are often costly, labor-intensive, and reliant on field data, limiting their scalability, especially in data-scarce regions. This paper addresses this gap by presenting a comprehensive and scalable framework that employs publicly accessible satellite data to map crop types and subsequently assess irrigation performance without the need for ground truthing. The framework consists of two parts: First, crop mapping, which was conducted seasonally between 2015 and 2020 for the four primary crops in the Nile Delta (rice, maize, wheat, and clover). The WaPOR v2 Land Cover Classification layer was used as a substitute for ground truth data to label the Landsat-8 images for training the random forest algorithm. The crop maps generated at 30 m resolution had moderate to high accuracy, with overall accuracy ranging from 0.77 to 0.80 in summer and 0.87–0.95 in winter. The estimated crop areas aligned well with national agricultural statistics. Second, based on the mapped crops, three irrigation performance indicators—adequacy, reliability, and equity—were calculated and compared with their established standards. The results reveal a good level of equity, with values consistently below 10%, and a relatively reliable water supply, as indicated by the reliability indicator (0.02–0.08). Average summer adequacy ranged from 0.4 to 0.63, indicating insufficient supply, whereas winter values (1.3 to 1.7) reflected a surplus. A noticeable improvement gradient was observed for all indicators toward the north of the delta, while areas located in the delta’s new lands consistently displayed unfavorable conditions in all indicators. This approach facilitates the identification of regions where agricultural performance falls short of its potential, thereby offering valuable insights into where and how irrigation systems can be strategically improved to enhance overall performance sustainably. Full article

Urbanization is causing a decrease in agricultural land. This leads to changes in cropping patterns, irrigation water availability, and water allowance. Therefore, change in cropping pattern, irrigation water availability, and water allowance were investigated in the Multan region of Pakistan using remote sensing and GIS techniques. The multi-temporal Landsat images with 30 m resolution were acquired for both Rabi (winter) and Kharif (summer) seasons for the years of 1988, 1999 and 2020. The image processing tasks including layer stacking, sub-setting, land use/land cover (LULC) classification, and accuracy assessment were performed using ERDAS Imagine (2015) software. The LULC maps showed a considerable shift of orchard area to urban settlements and other crops. About 82% of orchard areas have shifted to urban settlements and other crops from 1988 to 2020. The LULC maps for Kharif season indicated that cropped areas for cotton have decreased by 42.5% and the cropped areas for rice have increased by 718% in the last 32 years (1988–2020). During the rabi season, the cropped areas for wheat (Triticum aestivum L.) have increased by 27% from 1988 to 2020. The irrigation water availability and water allowance have increased up to 125 and 110% due to decrease in agricultural land, respectively. The overall average accuracies were found as 87 and 89% for Rabi and Kharif crops, respectively. The LULC mapping technique may be used to develop a decision support system for evaluating the changes in cropping pattern and their impacts on net water availability and water allowances. Full article

Unlike traditional yield mapping, which is conducted using costly yield sensors mounted on combine harvesters to collect post-harvest data, yield potential prediction using remote sensing data is considered a low-cost alternative. In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. Combinations of seven vegetation indices from Sentinel-2 imagery and seven phenology metrics were evaluated for the prediction of maize and soybean yield potential. Ground truth yield data were provided by the Quantile Loss Domain Adversarial Neural Network (QDANN) database, with 1000 samples randomly selected per year from 2019 to 2022 for Iowa and Illinois. Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). Across all evaluations, RF was found to outperform the other models in both cross-validation and final model accuracy metrics. Vegetation index values at peak of season (POS) and phenological timing, expressed as the day of year (DOY) of phenological events, were identified as the most influential covariates for predicting yield potential in particular years for both maize and soybean. Full article

Intergovernmental Panel on Climate Change (IPCC) guidelines have been standardized and widely used to calculate methane (CH₄) emissions from paddy fields. The emission factor (EF) is a key parameter in these guidelines, and it is different for each location globally and regionally. However, limited studies have been conducted to measure locally specific EFs (EFlocal) through on-site assessments and modeling their spatial distribution effectively. This study aims to investigate the potential of multisensor satellite data to develop a spatial model of CH₄ emission estimation on rice paddy fields under different water management practices, i.e., continuous flooding (CF) and alternate wetting and drying (AWD) in Subang, West Java, Indonesia. The model employed the national EF (EFnational) and EFlocal using the IPCC guidelines. In this study, we employed the multisensor satellite data to derive the key parameters for estimating CH₄ emission, i.e., rice cultivation area, rice age, and EF. Optical high-resolution images were used to delineate the rice cultivation area, Sentinel-1 SAR imagery was used for identifying transplanting and harvesting dates for rice age estimation, and ALOS-2/PALSAR-2 was used to map the water regime for determining the scaling factor of the EF. The closed-chamber method has been used to measure the daily CH₄ flux rate on the local sites. The results revealed spatial variability in CH₄ emissions, ranging from 1–5 kg/crop/season to 20–30 kg/crop/season, depending on the water regime. Fields under CF exhibited higher CH₄ emissions than those under AWD, underscoring the critical role of water management in mitigating CH₄ emissions. This study demonstrates the feasibility of combining remote sensing data with the IPCC model to spatially estimate CH₄ emissions, providing a robust framework for sustainable rice cultivation and greenhouse gas (GHG) mitigation strategies. Full article

Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies—such as high spatiotemporal resolution optical, radar, and thermal infrared sensors—has opened new avenues for efficient soil moisture retrieval. However, the accuracy of soil moisture retrieval decreases significantly when the soil is covered by vegetation. This study proposes a multi-modal remote sensing collaborative retrieval framework that integrates UAV-based multispectral imagery, Sentinel-1 radar data, and in situ ground sampling. By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. The results demonstrate that the retrieval performance of the model was significantly improved across different soil depths (0–5 cm, 5–10 cm, 10–15 cm, 15–20 cm). After vegetation suppression, the coefficient of determination (R²) exceeded 0.8 for all soil layers, while the mean absolute error (MAE) decreased by 35.1% to 49.8%. This research innovatively integrates optical–radar–thermal multi-source data and a physically driven vegetation suppression strategy to achieve high-accuracy, meter-scale dynamic mapping of soil moisture in vegetated areas. The proposed method provides a reliable technical foundation for precision irrigation and drought early warning. Full article

Early-season crop mapping plays a critical role in yield estimation, agricultural management, and policy-making. However, most existing methods assign a uniform earliest identification time across provincial or broader extents, overlooking spatial heterogeneity in crop phenology and environmental conditions. This often results in delayed detection or reduced mapping accuracy. To address this issue, we proposed a zonal-based early-season mapping framework for winter wheat by integrating phenological and environmental factors. Aggregation zones across Shandong Province were delineated using Principal Component Analysis (PCA) based on factors such as start of season, end of season, temperature, slope, and others. On this basis, early-season winter wheat identification was conducted for each zone individually. Training samples were generated using the Time-Weighted Dynamic Time Warping (TWDTW) method. Time-series datasets derived from Sentinel-1/2 imagery (2021–2022) were processed on the Google Earth Engine (GEE) platform, followed by feature selection and classification using the Random Forest (RF) algorithm. Results indicated that Shandong Province was divided into four zones (A–D), with Zone D (southwestern Shandong) achieving the earliest mapping by early December with an overall accuracy (OA) of 97.0%. Other zones reached optimal timing between late December and late January, all with OA above 95%. The zonal strategy improved OA by 3.6% compared to the non-zonal approach, demonstrated a high correlation with official municipal-level statistics (R² = 0.97), and surpassed the ChinaWheat10 and ChinaWheatMap10 datasets in terms of crop differentiation and boundary delineation. Historical validation using 2017–2018 data from Liaocheng City, a prefecture-level city in Shandong Province, achieved an OA of 0.98 and an F₁ score of 0.96, further confirming the temporal robustness of the proposed approach. This zonal strategy significantly enhances the accuracy and timeliness of early-season winter wheat mapping at a large scale. Full article

Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management. Full article

Yield maps represent crop production output and are essential for evaluating within-field spatial variability. Managing this yield variability is critical for precision and digital agriculture to facilitate optimized crop yield and reduced environmental impact. This work evaluated the spatial and temporal variability in corn and soybean yield data from three conventionally managed agricultural fields, with nine, three, and four seasons’ data. The data variability was measured through standard deviation (SD) and coefficient of variation (CV%). After separately normalizing each year of the yield data set, the temporal variability (TSD and TCV%) was calculated by grid cell for each field across years. A new index is proposed in this paper, the yield performance index (YPI, the ratio of mean normalized yield (${\overline{Y}}_N$) to the TSD), as an index with a lower value for lower yield and higher temporal variability. Two, three, and four zones were delineated using only YPI. These zones were valuable for identifying areas needing particular attention, with consistently (i) high yields and low variability or (ii) low yields and high variability. Full article

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides consistent long-term satellite observations that are valuable for rice mapping and production estimation through phenology extraction. This study evaluates the effectiveness of three MODIS products, MOD09GQ (1-day), MOD09Q1 (8-day), and MOD13Q1 (16-day), for mapping rice phenology in An Giang Province, a key rice-producing region in Vietnam’s climate-sensitive Mekong River Delta (MRD). The analysis focuses on rice cropping seasons from 2019 to 2021, using time series of the Normalized Difference Vegetation Index (NDVI) to capture temporal and spatial variations in rice growth dynamics. To address data gaps due to persistent cloud cover and sensor-related noises, smoothing techniques, including the Double Logistic Function (DLF) and Savitzky–Golay Filtering (SGF), were applied. Thirteen phenological parameters were extracted and used as inputs to an unsupervised K-Means clustering algorithm, enabling the classification of distinct rice growth patterns. The results show that DLF-processed MOD09GQ data most accurately reconstructed NDVI time series and captured short-term phenological transitions, outperforming coarser-resolution products. The resulting phenology maps could be used to correlate the influence of anthropogenic factors, such as the widespread adoption of short-duration rice varieties and shifts in water management practices. This study provides a robust framework for phenology-based rice mapping to support food security, sustainable agricultural planning, and climate resilience in the MRD. Full article

Flood risk mapping is spreading in the Global South due to the availability of high-resolution/high-frequency satellite imagery, volunteered geographic information, and hydraulic models. However, these maps are increasingly generated without the participation of exposed communities, contrary to the Sendai Framework for Disaster Risk Reduction 2015–2030 priorities. As a result, the understanding of risk is limited. This study aims to map flood risk with citizen science complemented by hydrology, geomatics, and spatial planning. The Niger River floods of 2024–2025 on a 113 km² area upstream of Niamey are investigated. The novelty of the work is the integration of local and technical knowledge in the micro-mapping of risk in a large area. We consider risk the product of a hazard and damage in monetary terms. Focus groups in flooded municipalities, interviews with irrigation perimeter managers, and statistical river flow and rainfall analysis identified the hazard. The flood plain was extracted from Sentinel-2 images using MNDWI and validated with ground control points. Six classes of assets were identified by visual photo interpretation of very high-resolution satellite imagery. Damage was ascertained through interviews with a sample of farmers. The floods of 2024–2025 may occur again in the next 12–19 years. Farmers cannot crop safer sites, raising significant environmental justice issues. Damage depends on the strength of the levees, the crop, and the season. From January to February, horticulture is at a higher risk. Flooding does not bring benefits. Risk maps highlight hot spots, are validated, and can be linked to observed flood levels. Full article

Research into alternative phosphorus (P) fertilizer sources that may be able to supplement P resources is necessary. Struvite (MgNH₄PO₄ · 6H₂O) can be made by removing excess nutrients from waste sources and may reduce greenhouse gas (GHG) emissions from cropping systems. This study sought to quantify GHG [i.e., methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂)] fluxes, season-long emissions, and net GHG emissions from chemically precipitated struvite (CPST) and synthetic and real-wastewater-derived electrochemically precipitated struvite (ECST) compared to monoammonium phosphate (MAP) and an unamended control (UC) from flood-irrigated rice (Oryza sativa) grown in P-deficient, silt loam soil in a greenhouse. Gas samples were collected weekly over a 140-day period in 2022. Methane and CO₂ emissions differed (p < 0.05) among P fertilizer sources, while N₂O emissions were similar among all treatments. Methane, CO₂, and N₂O emissions from MAP-fertilized rice were the greatest (98.7, 20,960, and 0.44 kg ha⁻¹ season⁻¹, respectively), but they were similar to those of CH₄ and CO₂ for CPST and those of N₂O for all other P fertilizer sources. Season-long CH₄, CO₂, and N₂O emissions and net GHG emissions did not differ between ECST materials. This study’s results emphasized the potential that wastewater-recovered struvite has to reduce GHG emissions in rice production systems. Full article

Remotely piloted aircraft (RPA) are essential in precision coffee farming due to their capability for continuous monitoring, rapid data acquisition, operational flexibility at various altitudes and resolutions, and adaptability to diverse terrain conditions. This study evaluated the soil water conditions in a coffee plantation using remotely piloted aircraft to obtain multispectral images and vegetation indices. Fifteen vegetation indices were chosen to evaluate the vigor, water stress, and health of the crop. Soil samples were collected to measure gravimetric and volumetric moisture at depths of 0–10 cm and 10–20 cm. Data were collected at thirty georeferenced sampling points within a 1.2 ha area using GNSS RTK during the dry season (August 2020) and the rainy season (January 2021). The highest correlation (51.57%) was observed between the green spectral band and the 0–10 cm volumetric moisture in the dry season. Geostatistical analysis was applied to map the spatial variability of soil moisture, and the correlation between vegetation indices and soil moisture was evaluated. The results revealed a strong spatial dependence of soil moisture and significant correlations between vegetation indices and soil moisture, highlighting the effectiveness of RPA and geostatistics in assessing water conditions in coffee plantations. In addition to soil moisture, vegetation indices provided information about plant vigor, water stress, and general crop health. Full article

Phosphorus (P) is essential for crop growth, but its complex behavior in tropical soils necessitates alternative management strategies, such as foliar supplementation. Foliar-applied nutrients act as biostimulants, enhancing stress tolerance and plant productivity. This study assessed the physiological responses of soybean to foliar application of soluble monoammonium phosphate (MAP; at a rate of 5 kg ha⁻¹ each application) at different phenological stages (two during vegetative stages V₄ and V₆ and two during reproductive stages R₁ and R₃ or all four stages) across two growing seasons in tropical field conditions. Key parameters analyzed included leaf nutrient content, photosynthetic pigments, Rubisco activity, carbohydrate content, gas exchange (photosynthetic rate, stomatal conductance, transpiration, water use efficiency, and carboxylation efficiency), oxidative stress markers, and productivity indicators (100-grain weight and grain yield). MAP application improved all parameters, particularly at R₁ and R₃. Total chlorophyll increased by 29.2% at R₁ and 30.0% when applied at all four stages, while the net photosynthetic rate rose by 15.8% and 18.4%, respectively. Water use efficiency improved by 20.0% at R₁ and all four stages, while oxidative stress indicators, such as H₂O₂ levels, decreased. Rubisco activity increased most at R₃ (46.0%) and all four stages (59.9%). Grain yield was highest with MAP spread at all four stages (12.3% increase), though a single application at R₁ still boosted yield by 7.4%, compared to the control treatment. Full article