The vegetation fraction (VF) monitoring in a specific area is a very important parameter for precision agriculture. Until a few years ago, high-cost flights on aeroplanes and satellite imagery were the only option to acquire data to estimate VF remotely. Recently, Unmanned Aerial Vehicles (UAVs) have emerged as a novel and economic tool to supply high-resolution images useful to estimate VF. VF is usually estimated by spectral indices using red-green-blue (RGB) and near-infrared (NIR) bands data. For this study, a UAV equipped with both kinds of sensors (RGB and NIR) was used to obtain high-resolution imagery over a maize field in progressive dates along the mid-season and the senescence development stages. The early-season stage was also monitored using only RGB spectral indices. Flights were performed at 52 m over the terrain, obtaining RGB images of 1.25 cm pixel⁻¹ and multispectral images of 2.10 cm pixel⁻¹. Three spectral indices in the visible region, Excess Green (ExG), Colour Index of Vegetation (CIVE), and Vegetation Index Green (VIg), and three NIR-based vegetation indices, Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Normalized Green (NG), were evaluated for VF estimation. Otsu’s method was applied to automatically determine the threshold value to classify the vegetation coverage. Results show that ExG presents the higher mean accuracy (85.66%) among all the visible indices, with values ranging from 72.54% to 99.53%, having its best performance in the earlier development stage. Nevertheless, GNDVI mean accuracy (97.09%) overcomes all the indices (visible and multispectral), ranging in value from 92.71% to 99.36%. This allowed comparing the accuracy difference gained by using a NIR sensor, with a higher economic cost than required using a simple RGB sensor. The results suggest that ExG can be a very suitable option to monitor VF in the early-season growth stage of the crop, while later stages could require NIR-based indices. Thus, the selection of the index will depend on the objectives of the study and the equipment capacity.

Torrential rainfall can generate landslides, flash floods, and debris flows which might become disasters, causing loss of life and damage to property and infrastructure. To respond opportunely to hydrometeorological hazards, it is necessary to assess, rapidly and accurately, damage to the affected area. This is commonly done through time-consuming reconnaissance visits to obtain detailed field information. This paper proposes a methodology which uses: i) high resolution satellite and RGB images from unmanned aerial vehicles (UAV), ii) digital elevation models (DEM), and iii) object-based image analysis (OBIA) for rapid urban flood damage assessment and estimation of the number of houses washed away, or with a total or partial roof collapse, by comparing pre- and post-event data. The case study was Tropical Storm Earl in 2016 that affected the town of Chicahuaxtla, Puebla, Mexico, due to the overflow of the Zempoloantongo River that cuts through the town causing several loss of life and severe property damage. The results indicate that the three-pronged approach proposed herein is able to discriminate changes before and after the event and improve image classification of washed-away or destroyed houses. The overall accuracy of the proposed automatic classification obtained with UAV data had a value of 97.4%. Structural damage was not assessed in this study.

Nitrogen (N) is an essential nutrient for plant growth and development and is especially important in the production of high quality leafy green vegetables. In this experiment, leaf N concentration, chlorophyll concentration (Chl) and weight above fresh matter (AFM) of romaine lettuce (Lactuca sativa L. var. longifolia) were estimated by correlations between in situ SPAD and atLEAF readings. Lettuce was grown in high tunnels during 42 days and was irrigated at five nitrogen levels: 0, 4, 8, 12 and 16 mEq·L-1 of NO3-, based on the Steiner nutrient solution. The N concentration, Chl concentration and AFM were determined in the laboratory, while SPAD and atLEAF readings were measured in situ weekly. SPAD readings had high, positive and significant linear correlations with N (R2 = 0.90), Chl (R2 = 0.97) and AFM (R2 = 0.98); atLEAF readings had a similar linear correlation with N (R2 = 0.91), Chl (R2 = 0.92) and AFM (R2 = 0.97). Besides, SPAD and atLEAF readings had high, positive, and significant linear correlation (R2 = 0.96). Thus, SPAD and atLEAF meters can be used to non-destructively and accurately estimate the N status of lettuce, in a reliable and quick manner during the crop production cycle. In addition, atLEAF is currently more affordable than SPAD. ********* In press - Online First. Article has been peer reviewed, accepted for publication and published online without pagination. It will receive pagination when the issue will be ready for publishing as a complete number (Volume 47, Issue 3, 2019). The article is searchable and citable by Digital Object Identifier (DOI). DOI link will become active after the article will be included in the complete issue. *********

Precipitation and its distribution greatly influence the evolution of ecosystems and the development of society. The objective was to analyse trends in extreme precipitation indices at 25 weather stations of Aguascalientes State. Eleven extreme precipitation indices were obtained. The time series of these indices were analysed with the non‐parametric Mann–Kendall test. The number of days above 50 mm, consecutive wet days, and extremely wet days did not have significant trends at any of the weather stations. Each of the indices maximum 1 day precipitation amount, maximum 5 day precipitation amount and number of very heavy precipitation days showed a significant positive trend at 12% of the weather stations; both the number of heavy precipitation days and the number of very wet days had significant positive trends at 8% of the weather stations; the simple daily intensity index, consecutive dry days and annual total wet‐day precipitation showed significant positive trends at 20%, 36% and 4% of weather stations, respectively. The intensity index was the only one that showed a significant negative trend, which happened at 4% of the weather stations. In a small part of Aguascalientes, the precipitation intensity, the number of rainy days and the accumulated total increased in short periods. Also, in a reduced region, daily precipitation intensity decreased; and in another very small area the number of dry days increased. For mitigating the effects of these phenomena, it is suggested that water be used more efficiently for sustained agricultural production systems and ecosystem management. The results of the present study will be of great importance in future economic planning in the Aguascalientes State.

Remote sensing-based crop monitoring has evolved unprecedentedly to supply multispectral imagery with high spatial-temporal resolution for the assessment of crop evapotranspiration (ETc). Several methodologies have shown a high correlation between the Vegetation Indices (VIs) and the crop coefficient (Kc). This work analyzes the estimation of the crop coefficient (Kc) as a spectral function of the product of two variables: VIs and green vegetation cover fraction (f_v). Multispectral images from experimental maize plots were classified to separate pixels into three classes (vegetation, shade and soil) using the OBIA (Object Based Image Analysis) approach. Only vegetation pixels were used to estimate the VIs and f_v variables. The spectral Kc_fv:VI models were compared with Kc based on Cumulative Growing Degree Days (CGDD) (Kc-c_GDD). The maximum average values of Normalized Difference Vegetation Index (NDVI), WDRVI, amd EVI2 indices during the growing season were 0.77, 0.21, and 1.63, respectively. The results showed that the spectral Kc_fv:VI model showed a strong linear correlation with Kc-c_GDD (R² > 0.80). The model precision increases with plant densities, and the Kc_fv:NDVI with 80,000 plants/ha had the best fitting performance (R² = 0.94 and RMSE = 0.055). The results indicate that the use of spectral models to estimate Kc based on high spatial and temporal resolution UAV-images, using only green pixels to compute VI and f_v crop variables, offers a powerful and simple tool for ETc assessment to support irrigation scheduling in agricultural areas.

The objective of this research was to analyze the temporal patterns of monthly and annual precipitation at 36 weather stations of Aguascalientes, Mexico. The precipitation trend was determined by the Mann–Kendall method and the rate of change with the Theil–Sen estimator. In total, 468 time series were analyzed, 432 out of them were monthly, and 36 were annual. Out of the total monthly precipitation time series, 42 series showed a statistically significant trend (p ≤ 0.05), from which 8/34 showed a statistically significant negative/positive trend. The statistically significant negative trends of monthly precipitation occurred in January, April, October, and December. These trends denoted more significant irrigation water use, higher water extractions from the aquifers in autumn–winter, more significant drought occurrence, low forest productivity, higher wildfire risk, and greater frost risk. The statistically significant positive trends occurred in May, June, July, August, and September; to a certain extent, these would contribute to the hydrology, agriculture, and ecosystem but also could provoke problems due to water excess. In some months, the annual precipitation variability and El Niño-Southern Oscillation (ENSO) were statistically correlated, so it could be established that in Aguascalientes, this phenomenon is one of the causes of the yearly precipitation variation. Out of the total annual precipitation time series, only nine series were statistically significant positive; eight out of them originated by the augments of monthly precipitation. Thirteen weather stations showed statistically significant trends in the total precipitation of the growing season (May, June, July, August, and September); these stations are located in regions of irrigated agriculture. The precipitation decrease in dry months can be mitigated using shorter cycle varieties with lower water consumption, irrigation methods with high efficiency, and repairing irrigation infrastructure. The precipitation increase in humid months can be used to store water and use it during the dry season, and its adverse effects can be palliated with the use of varieties resistant to root diseases and lodging. The results of this work will be beneficial in the management of agriculture, hydrology, and water resources of Aguascalientes and in neighboring arid regions affected by climate change.