Showing papers on "Weather station published in 2011"

Predicting solar generation from weather forecasts using machine learning

Abstract: A key goal of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewables. One challenge with integrating renewables into the grid is that their power generation is intermittent and uncontrollable. Thus, predicting future renewable generation is important, since the grid must dispatch generators to satisfy demand as generation varies. While manually developing sophisticated prediction models may be feasible for large-scale solar farms, developing them for distributed generation at millions of homes throughout the grid is a challenging problem. To address the problem, in this paper, we explore automatically creating site-specific prediction models for solar power generation from National Weather Service (NWS) weather forecasts using machine learning techniques. We compare multiple regression techniques for generating prediction models, including linear least squares and support vector machines using multiple kernel functions. We evaluate the accuracy of each model using historical NWS forecasts and solar intensity readings from a weather station deployment for nearly a year. Our results show that SVM-based prediction models built using seven distinct weather forecast metrics are 27% more accurate for our site than existing forecast-based models.

Integrating indigenous knowledge with conventional science: Enhancing localised climate and weather forecasts in Nessa, Mulanje, Malawi

Abstract: Subsistence rain fed agriculture underpins rural livelihoods in the Sub Saharan Africa. The overdependence on rainfall suggests the need for more reliable climate and weather forecasts to guide farm level decision making. Traditionally, African farmers have used indigenous knowledge (IK) to understand weather and climate patterns and make decisions about crops and farming practices. However, increased rainfall variability in recent years associated with climate change has reduced their confidence in indigenous knowledge, hence reducing their adaptive capacity and increasing their vulnerability to climate change. To address this problem, researchers are advocating the integration of indigenous knowledge into scientific climate forecasts at the local level, where it can be used to enhance the resilience of communities vulnerable to climate change. A study was therefore conducted to establish commonly used IK indicators in weather and climate forecasting and people’s perceptions of climate change and variability in Nessa Village, Southern Malawi. We further compared the people’s perceptions on climate change and variability with empirical evidence from a nearby weather station during 1971–2003 and the major constraints that the people face to fully utilise conventional weather and climate forecasts. Our results show various forms of traditional indicators that have been used to predict weather and climate for generations. These include certain patterns and behaviour of flora and fauna as well as environmental conditions. We further established that the peoples documentation of major climatic events over the years in the area agreed with the empirical evidence from the temperature and rainfall data. Overall, rainfall in the area has reduced since 1971 with increasing temperatures. The people were however of the view that current scientific weather and climate predictions in Malawi were not that useful at village level because they do not incorporate IK.

Elevational species shifts in a warmer climate are overestimated when based on weather station data

Abstract: Strong topographic variation interacting with low stature alpine vegetation creates a multitude of micro-habitats poorly represented by common 2 m above the ground meteorological measurements (weather station data). However, the extent to which the actual habitat temperatures in alpine landscapes deviate from meteorological data at different spatial scales has rarely been quantified. In this study, we assessed thermal surface and soil conditions across topographically rich alpine landscapes by thermal imagery and miniature data loggers from regional (2-km2) to plot (1-m2) scale. The data were used to quantify the effects of spatial sampling resolution on current micro-habitat distributions and habitat loss due to climate warming scenarios. Soil temperatures showed substantial variation among slopes (2–3 K) dependent on slope exposure, within slopes (3–4 K) due to micro-topography and within 1-m2 plots (1 K) as a result of plant cover effects. A reduction of spatial sampling resolution from 1 × 1 m to 100 × 100 m leads to an underestimation of current habitat diversity by 25% and predicts a six-times higher habitat loss in a 2-K warming scenario. Our results demonstrate that weather station data are unable to reflect the complex thermal patterns of aerodynamically decoupled alpine vegetation at the investigated scales. Thus, the use of interpolated weather station data to describe alpine life conditions without considering the micro-topographically induced thermal mosaic might lead to misinterpretation and inaccurate prediction.

Evaluation of Satellite-Based, Modeled-Derived Daily Solar Radiation Data for the Continental United States

Abstract: Published in Agron. J. 103:1242–1251 (2011) Posted online 6 Jun 2011 doi:10.2134/agronj2011.0038 Copyright © 2011 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. M agricultural and natural resource management efforts involve spatial scales above the field and farm levels. Applications range from monitoring regional water use, to identifying promising zones for production of new crops, to targeting of specific cultivars or crop traits, to determining the potential impact of climate change and potential options for adaptation. Spatial assessments often consider climatic variation and increasingly, long-term records of daily weather data are required to examine climatic risks or trends related to climate change. Such analyses, however, are usually constrained by the availability and quality of the observed long-term meteorological data. Weather station data may not be available from the regions of interest, and individual stations may lack data for long time intervals. Weather data per se may show local variation due to positioning of the station and the instrument, instrument calibration drift, change in instrumentation, and other factors (Davey and Pielke, 2005; Younes et al., 2005). Solar radiation data have long been recognized as especially problematic (Durrenberger and Brazel, 1976; Stoffel et al., 2000). Radiation must be correctly integrated at low sun elevation angles and over all wavelengths. Radiometers using thermopiles are expensive, while lower-cost silicon pyranometers are less accurate. Both types of sensors require electronic circuitry to integrate readings over time and are sensitive to ambient temperatures. Sensor calibration is difficult because accurate reference values (besides 0) cannot be produced through simple techniques; thus sensors are usually cross-calibrated against radiometers whose calibrations are traceable to standards such as those maintained by the National Institute of Standards and Technology. The NASA/POWER project at the NASA Langley Research Center provides daily data for surface solar radiation and other weather variables on a 1° × 1° geographic coordinate grid for the ABSTRACT

The influence of the weather on affective experience: An experience sampling study.

Abstract: This study examined the relationship between affective experiences and weather variables using an experience-sampling method. The moderating effects of personality and age on the relationship were also investigated. Two age groups of participants (students and elderly people) recorded their moods when signalled during 14 consecutive days on 7 randomly determined occasions per day. Hourly weather data (temperature, relative humidity, barometric pressure, and luminance) for the same period were obtained from the local weather station. Previously participants had completed the Estonian versions of the Revised NEO Personality Inventory (Kallasmaa, Allik, Realo, & McCrae, 2000) and the Positive and Negative Affect Schedule (Allik & Realo, 1997). Multilevel random coefficient modeling analyses showed that momentary ratings of positive and negative affect were weakly related to temperature, positive affect was also related to sunlight. However, momentary ratings of fatigue showed a distinct tendency for greater incidence of sleepiness in the cold and dark. Age group was one of the most important moderators of the weather-emotion models. The influence of weather on emotions interacted with being outdoors. Personality traits also explained a small portion of variance in the influence of weather on affective states.

Flexible insurance for heterogeneous farmers: Results from a small-scale pilot in Ethiopia

Abstract: We analyze the effectiveness of a new approach in providing weather index–based insurance products to low-income populations. The approach is based on the concept of providing multiple weather securities that pay a fixed amount if the event written on the security (that monthly rainfall at a nearby weather station falls below a stated cutoff) comes true. A theoretical model is developed to outline the conditions in which weather securities could outperform crop-specific weather index–based insurance policies. Data collected during both an experimental game and real purchases of such insurance policies among farmers in southern Ethiopia suggest that the securities are well understood and can fit heterogeneous farmer needs. This paper documents (1) heterogeneity of rainfall risk among farmers, (2) the understanding of securities and transmission of information about weather securities among members of endogenously formed risk-sharing groups, and (3) the nature of purchasing decisions and manner in which they are made.

A Zigbee based smart sensing platform for monitoring environmental parameters

Abstract: The ability to monitor environmental conditions is crucial to research in fields ranging from climate variability to agriculture and zoology. Being able to document baseline and changing environmental parameters over time is increasingly essential important and researchers are relying more and more on unattended weather stations for this propose. A Zigbee Based Smart Sensing Platform for Monitoring Environmental Parameters has been designed and developed. The smart weather station consists of SiLab C8051F020 microcontroller based measuring units which collect the value of the temperature, relative humidity, pressure and sunlight. These units send their data wireless to a central station, which collects the data, stores and displays them into a database. The facility of adding a few more sensors and a few more stations has been provided.

Analysis of the Urban Thermal Fingerprint of the City of Trento in the Alps

Abstract: The temperature contrasts typically marking urban heat island (UHI) effects in the city of Trento, Italy, located in an Alpine valley and inhabited in its inner urban area by a population of about 56 000, are investigated. Time series of air temperature data, collected at an urban weather station, in the city center, and at five extraurban stations are compared. The latter are representative of rural and suburban areas, both on the valley floor and on the valley sidewalls. It is found that the extraurban weather stations, being affected by different local-scale climatic conditions, display different temperature contrasts with the urban site. However, the diurnal cycle of the UHI is characterized by similar patterns of behavior at all of the extraurban weather stations: the UHI intensity is stronger at night, whereas during the central hours of the day an “urban cool island” is likely to occur. The diurnal maximum UHI intensity turns out to be typically of order 3°C, but under particularly favorabl...

Modeling and Syndromic Surveillance for Estimating Weather-Induced Heat-Related Illness

Abstract: This paper compares syndromic surveillance and predictive weather-based models for estimating emergency department (ED) visits for Heat-Related Illness (HRI). A retrospective time-series analysis of weather station observations and ICD-coded HRI ED visits to ten hospitals in south eastern Ontario, Canada, was performed from April 2003 to December 2008 using hospital data from the National Ambulatory Care Reporting System (NACRS) database, ED patient chief complaint data collected by a syndromic surveillance system, and weather data from Environment Canada. Poisson regression and Fast Orthogonal Search (FOS), a nonlinear time series modeling technique, were used to construct models for the expected number of HRI ED visits using weather predictor variables (temperature, humidity, and wind speed). Estimates of HRI visits from regression models using both weather variables and visit counts captured by syndromic surveillance as predictors were slightly more highly correlated with NACRS HRI ED visits than either regression models using only weather predictors or syndromic surveillance counts.

Modelling surface-air-temperature variation over complex terrain around Abisko, Swedish Lapland: Uncertainties of measurements and models at different scales

Abstract: P>Many ecological, physical and geographical processes affected by climate in the natural environment are scale-dependent: determining surface-air-temperature distribution at a scale of tens to hundreds of metres can facilitate such research, which is currently hampered by the relative dearth of meteorological stations and complex surface temperature characteristics, particularly in mountain areas. Here we discuss both the couplings and mismatch of present climatological data at different scales, ranging from similar to 50 m to 100 km, and provide a novel model of the surface-air-temperature distribution in topographically heterogeneous regions. First, a comparison of the large-scale weather station measurements and gridded climate reanalysis (ERA-40) data is used to define regional climatology in the Swedish sub-Arctic and obtain the mesoscale temperature lapse rates. Second, combined with temperature measurements obtained from transects set among complex terrain, key microclimatic characteristics of the temperature distribution are identified, showing few temperature inversions when the wind speed exceeds 3 m s-1, while temperature inversions prevail during calm nights. Besides wind, there is a pronounced winter temperature stratification around the large Lake Tornetrask, and variations in topography are found to have a strong influence in shaping the microscale temperature pattern through their effect on solar radiation during summer. A monthly 50-m scale temperature-distribution (topoclimate) model is built based on the above findings, and model validation is conducted using further fieldwork measurements from different seasons. We present results of surface-air-temperature distribution for the Abisko region, and discuss how these results help reconcile the scale mismatch mentioned above. (Less)

Point process modeling of wildfire hazard in Los Angeles County, California

Abstract: The Burning Index (BI) produced daily by the United States government's National Fire Danger Rating System is commonly used in forecasting the hazard of wildfire activity in the United States. However, recent evaluations have shown the BI to be less effective at predicting wildfires in Los Angeles County, compared to simple point process models incorporating similar meteorological information. Here, we explore the forecasting power of a suite of more complex point process models that use seasonal wildfire trends, daily and lagged weather variables, and historical spatial burn patterns as covariates, and that interpolate the records from different weather stations. Results are compared with models using only the BI. The performance of each model is compared by Akaike Information Criterion (AIC), as well as by the power in predicting wildfires in the historical data set and residual analysis. We find that multiplicative models that directly use weather variables offer substantial improvement in fit compared to models using only the BI, and, in particular, models where a distinct spatial bandwidth parameter is estimated for each weather station appear to offer substantially improved fit.

Development of a WSN integrated weather station node for an irrigation alert program under Tunisian conditions

Abstract: Recent advances in sensor and wireless radio frequency (RF) technologies offer vast opportunities for development and application of sensor systems for agriculture. It is the concept of precision agriculture, where Wireless Sensor Networks (WSN) is playing an important part in the handling and managing of water resources for irrigation, in understanding the changes in the crops to assess the optimum point for harvesting, in estimating fertilizers requirements and to predict crop performance more accurately. The field micro-climate monitoring is important for crop water requirements determination. Traditional weather stations are expensive, on demand data collection and not flexible for new sensors addition or WSN integration. This paper aims at the design of a custom low-cost Weather Station hardware and software node (WS-node) for irrigation scheduling in developing countries context. These sensors data are transmitted to the DSS (Decision Support System) in a Wireless Sensor Networks (WSN) infrastructure, taking into account all particularities of such environments constraints. Cost factors, field conditions, farmer knowledge, devices availability are the most important ones.

Embedded weather station with remote wireless control

Abstract: Weather monitoring is of great importance in many domains such as: agriculture, military, entertainment etc. There are several solutions for monitoring the weather. The classical solution consists in static weather stations. Another solution is based on wireless sensor networks (WSNs). The third solution uses low dimensions weather stations. This paper presents a weather station made of temperature, humidity, pressure and luminosity sensors embedded in a microcontroller based board. The station is controlled through the SMS service of mobile phones.

Weather daily variation in winter and its effect on behavior and affective states in day-care children.

Abstract: This study aimed to analyze the impact of winter weather conditions on young children’s behavior and affective states by examining a group of 61 children attending day-care centers in Florence (Italy). Participants were 33 males, 28 females and their 11 teachers. The mean age of the children at the beginning of the observation period was 24.1 months. The day-care teachers observed the children’s behavioral and emotional states during the morning before their sleeping time and filled in a questionnaire for each baby five times over a winter period of 3 weeks. Air temperature, relative humidity, air pressure and solar radiation data were collected every 15 min from a weather station located in the city center of Florence. At the same time, air temperature and relative humidity data were collected in the classroom and in the garden of each day-care center. We used multilevel linear models to evaluate the extent to which children’s emotional and behavioral states could be predicted by weather conditions, controlling for child characteristics (gender and age). The data showed that relative humidity and solar radiation were the main predictors of the children’s emotional and behavioral states. The outdoor humidity had a significant positive effect on frustration, sadness and aggression; solar radiation had a significant negative effect only on sadness, suggesting that a sunny winter day makes children more cheerful. The results are discussed in term of implications for parents and teachers to improve children’s ecological environment.

Gridded meteorological data as a resource for mechanistic macroecology in coastal environments.

Abstract: Gridded weather data were evaluated as sources of forcing variables for biophysical models of intertidal animal body temperature with model results obtained using local weather station data serving as the baseline of comparison. The objective of the study was to determine which gridded data are sufficient to capture observed patterns of thermal stress. Three coastal sites in western North America were included in this analysis: Boiler Bay, Oregon; Bodega Bay, California; and Pacific Grove, California. The gridded data with the highest spatial resolution, the 32-km North American Regional Reanalysis (NARR) and the 38-km Climate Forecasting System Reanalysis (CFSR), predicted daily maximum intertidal animal temperature most similarly to the local weather station data. Time step size was important for variables that change rapidly throughout the day, such as solar radiation. There were site-based differences in the ability of the model to predict daily maximum intertidal animal temperature, with the gridded ...

Alternative climate data sources for distributed hydrological modelling on a daily time step.

Abstract: Two major criteria in choosing climate data for use in hydrological modelling are the period of record of the data set and the proximity of the collection platform(s) to the basin under study. Conventional data sets are derived from weather stations; however, in many cases there are no weather stations sufficiently close to a basin to be representative of climate conditions in that basin. In addition, it is often the case either that the period of record for the weather station(s) does not cover the period of the proposed simulation or that there are gaps in the data. Therefore, the objectives of this study are to investigate alternative climate data sources for use in hydrological modelling and to develop a protocol for creating hydrological data sets that are spatially and temporally harmonized. The methods we used for constructing daily, spatially distributed, climatic data sets of precipitation, maximum and minimum temperature, wind speed, solar radiation, potential evapotranspiration, and relative humidity are described. The model used in this study was the Soil and Water Assessment Tool implemented on the Mimbres River Basin located in southwestern New Mexico, USA, for the period 2003–2006. Our hydrological simulations showed that two events in January and February 2005 were missed, while an event in August 2006 was well simulated. We have also investigated the usefulness of several other precipitation data sets and compared the simulation results. Copyright © 2010 John Wiley & Sons, Ltd.

Derivation of Drought Severity-Duration-Frequency Curves Using Drought Frequency Analysis

Abstract: In this study, frequency analysis using drought index had implemented for the derivation of drought severity-duration-frequency (SDF) curves to enable quantitative evaluations of past historical droughts having been occurred in Korean Peninsular. Seoul, Daejeon, Daegu, Gwangju, and Busan weather stations were selected and precipitation data during 1974~2010 (37 years) was used for the calculation of Standardized Precipitation Index (SPI) and frequency analysis. Based on the results of goodness of fit test on the probability distribution, Generalized Extreme Value (GEV) was selected as most suitable probability distribution for the drought frequency analysis using SPI. This study can suggest return periods for historical major drought events by using newrly derived SDF curves for each stations. In case of 1994~1995 droughts which had focused on southern part of Korea. SDF curves of Gwangju weather station showed 50~100 years of return period and Busan station showed 100~200 years of return period. Besides, in case of 1988~1989 droughts, SDF of Seoul weather station were appeared as having return periods of 300 years.

ReSET-Raster: Surface Energy Balance Model for Calculating Evapotranspiration Using a Raster Approach

Abstract: Surface energy balance models developed for mapping evapotranspiration at high resolution use weather station data. Previous models such as SEBAL or METRIC use wind and reference evapotranspiration from a single weather station. A third model (ReSET) uses wind run from multiple weather stations but uses a single weather station for the internal calibration. Because these models solve the energy balance equation for areas that usually have significant spatial variability, it is more appropriate to have all inputs and calculations in a raster format. This paper presents a full rasterized model, which uses all inputs and performs all calculations in a raster format (ReSET-Raster). The ReSET-Raster model provides the flexibility to use several weather stations as data sources to generate interpolated weather parameters in a raster format. It explicitly takes into account the spatial variation of weather parameters between weather stations, which can be significant. This paper presents examples showing that evapotranspiration calculated with the raster approach can vary as much as 17% compared with evapotranspiration calculated with point values.

Point process modeling of wildfire hazard in Los Angeles County, California

Abstract: The Burning Index (BI) produced daily by the United States government’s National Fire Danger Rating System is commonly used in forecasting the hazard of wildfire activity in the United States. However, recent evaluations have shown the BI to be less effective at predicting wildfires in Los Angeles County, compared to simple point process models incorporating similar meteorological information. Here, we explore the forecasting power of a suite of more complex point process models that use seasonal wildfire trends, daily and lagged weather variables, and historical spatial burn patterns as covariates, and that interpolate the records from different weather stations. Results are compared with models using only the BI. The performance of each model is compared by Akaike Information Criterion (AIC), as well as by the power in predicting wildfires in the historical data set and residual analysis. We find that multiplicative models that directly use weather variables offer substantial improvement in fit compared to models using only the BI, and, in particular, models where a distinct spatial bandwidth parameter is estimated for each weather station appear to offer substantially improved fit.

Present weather-sensor tests for measuring drifting snow

Abstract: In Antarctica, blowing snow accounts for a major component of the surface mass balance near the coast. Measurements of precipitation and blowing snow are scarce, and therefore collected data would allow testing of numerical models of mass flux over this region. A present weather station (PWS), Biral VPF730, was set up on the coast at Cap Prud'homme station, 5 km from Dumont d'Urville (DDU), principally to quantify precipitation. Since we expected to be able to determine blowing-snow fluxes from the PWS data, we tested this device first on our experimental site, the Lac Blanc pass. An empirical calibration was made with a snow particle counter. Although the physics of the phenomenon was not well captured, the flux outputs were better than those from FlowCapts. The first data from Antarctica were reanalyzed. The new calibration seems to be accurate for estimating the high blowingsnow flux with an interrogation of the precipitation effects.

Weather conditions and daily television use in the Netherlands, 1996–2005

Abstract: This study examines the impact of daily atmospheric weather conditions on daily television use in the Netherlands for the period 1996–2005. The effects of the weather parameters are considered in the context of mood and mood management theory. It is proposed that inclement and uncomfortable weather conditions are associated with lower human mood, and that watching entertainment and avoiding informational programs may serve to repair such mood. We consequently hypothesize that people spend more time watching television if inclement and uncomfortable weather conditions (low temperatures, little sunshine, much precipitation, high wind velocity, less daylight) coincide with more airtime for entertainment programs, but that they view less if the same weather conditions coincide with more airtime devoted to information fare. We put this interaction thesis to a test using a time series analysis of daily television viewing data of the Dutch audience obtained from telemeters (T = 3,653), merged with meteorological weather station statistics and program broadcast figures, whilst controlling for a wide array of recurrent and one-time societal events. The results provide substantial support for the proposed interaction of program airtime and the weather parameters temperature and sunshine on aggregate television viewing time. Implications of the findings are discussed.

Simplified SEBAL method for estimating vast areal evapotranspiration with MODIS data

Abstract: The SEBAL (surface energy balance algorithm for land) model provides an efficient tool for estimating the spatial distribution of evapotranspiration, and performs a simple adjustment procedure to calculate sensible heat flux using the wind speed data set from only one weather station. This paper proposes a simplified method to modify the traditional SEBAL model for calculating the 24-hour evapotranspiration ( ET daily ) in the Haihe Basin with data from 34 weather stations. We interpolated the wind speeds using the inverse distance weighting method to establish a wind field and then used it to calculate the friction velocity directly. This process also simplifies the iterative computation process of sensible heat flux. To validate the feasibility of this simplified method, we compared the results with those obtained with an appropriate but more complex method proposed by Tasumi, which separates a vast area into several sub-areas based on the weather conditions, and runs the SEBAL model separately in each sub-area. The results show good agreement between the evapotranspiration generated by the two methods, with a coefficient of determination ( r 2 ) of 0.966, which indicates the feasibility of estimating evapotranspiration over a large region with the simplified method.

Why Analyze Mental Models of Local Climate Change? A Case from Southern Mozambique

Abstract: People construct mental models of local climate change based on their observations and experiences of past climate events and changes. These mental models offer critical insight into locally important factors that trigger responses to new climate conditions and can be used to ground-truth regional climate models. In this paper, the authors explore mental models of changes to local climate patterns and climate-associated environmental changes over the past 45 years (1963–2008) in two rural communities in Matutuine District, Mozambique. Interview results are compared to data from a regional weather station. Residents discuss temperature increases, short-term and long-term precipitation changes, and altered seasonal timing. Measurable climate change in this region includes increasing temperatures and more erratic rainfall leading to drought and altered season timing. The climate-associated environmental changes residents observed draw attention to links between local livelihood practices and climate,...

Integrating neural network models with computational fluid dynamics (CFD) for site-specific wind condition

Abstract: Most building energy simulations tend to neglect microclimates in building and system design, concentrating instead on building and system efficiency. Energy simulations utilize various outdoor variables from weather data, typically from the average weather record of the nearest weather station that is located in an open field, near airports and parks. The weather data may not accurately represent the physical microclimate of the site, and may therefore reduce the accuracy of simulation results. For this reason, this paper investigates utilizing computational fluid dynamics (CFD) with neural network (NN) model to predict site-specific wind parameters for energy simulation. The CFD simulation is used to find selected samples of site-specific wind conditions. Findings from CFD simulation are used as training data for NN. A trained NN predicts site-specific hourly wind conditions for a typical year. The outcome of the site-specific wind condition from the neural network is used as wind condition input for the energy simulation. The results of energy simulation using typical weather station data and site-specific weather data are compared in this paper, in order to find the possibility of using site-specific weather condition by NN with CFD to yield more realistic and robust ES results.

Normalized difference vegetation index relationships with rainfall patterns and yield in small plantings of rain-fed sugarcane

Abstract: Cane growth in rain-fed sugarcane production, with an abrupt end to rainfall months before harvest, could differ from what is known in better-studied systems. Therefore, we evaluated the relationship between the normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectrometer (MODIS) obtained for 2,854 sugarcane farmers’ fields and rainfall patterns in northeastern Thailand. Temporal changes of NDVI were related to rainfall patterns. The regional monthly average NDVI and the regional monthly average rainfall, calculated by averaging weather station data representing four individual provinces in the region were linearly related (r 2 = 0.867, p<0.001) during the rainy season. Similarly, the average monthly MODIS NDVI for farmers’ fields situated within a five km radius of the weather stations representing sugarcane management zones, was significantly related to monthly rainfall for both individual weather stations and average weather station data. Neither average rainfall nor average MODIS NDVI was related to the average sugarcane yield of the farmers’ fields situated within the five km radius of the nine weather stations. On a larger scale, MODIS NDVI had a positive correlation (r = 0.565) with yield when averaged across all nine management zones, but only for the rainy-season planting. Commercial pre-harvest yield prediction would likely need to be made between the end of the rainy season (mid-October) and mid-January. Our results showed that NDVI is a confounded measurement during this evaluation period which is associated with the differences in both plant biomass and cane maturity. Once the rainy season ends, NDVI declines while stalk weight increases. Therefore, NDVI-based yield predictions may be difficult even with higher quality imagery.