Cotton Yield Forecasting in Tashkent Province by Using Remote Sensing Techniques

Centre for Geo-Information Thesis Report GIRS-2014-35

Cotton yield forecasting in Tashkent province by using Remote sensing techniques

Djakhangir Atakhanov

24, September, 24, 2014

Cotton yield forecasting in Tashkent province by using Remote sensing techniques

Djakhangir Atakhanov Registration number: 900327020060

Supervisors: Dr. Ir. Jan Clevers Dr. Alim Pulatov

A thesis submitted in partial fulfillment of the degree of Master of Science at Wageningen University and Research Centre, The Netherlands.

September, 2014 Wageningen, the Netherlands

Thesis code number: GRS-80436 Thesis Report: GIRS-2014-35 Wageningen University and Research Centre Laboratory of Geo-Information Science and Remote Sensing iii

Acknowledgements

This study was benefited from constructive discussions and meetings with supervisor Jan Clevers and Alim Pulatov. I would like to thank Alim Pulatov, for help with choosing the Thesis topic, for discussions and expressing his knowledge about area and crop growth in study area.

I would like to appreciate my fellow students who helped me during my thesis research as well. Who helped with analysis and expressed me their experiences and gave me the hints to find a solution and solve problems.

Two coordinators Ewa Wiestma and Alim Pulatov who helped to construct Double degree program and gave me opportunity to be awarded by Erasmus Mundus CASIA 1 project Action 2. I am thankful also for Wageningen University and Tashkent Institute of Irrigation and Melioration for being involved for the scholarship. Moreover, I would like to appreciate all people from both educational institutions for useful experience and knowledge.

MGI department and group, which were involved and supported me during my study and research have to be appreciated as well. Their knowledge and experience in GIS and remote sensing helped me and increased my level of knowledge.

I would like to thank my family and friends from home country for the help with obtaining the data and useful information and for the great support and patience during three years of my study.

I thank all state agencies, educational institutions and centers being involved in process of delivering and providing me valuable, cognitive and important information. In addition, I would like to appreciate the NASA USDA archive for providing images from diverse satellites.

Abstract

Crop yield forecasting is very important in every country around the world. Central Asian countries are enriched with agricultural lands. Uzbekistan’s main sector is agriculture with a large variety of different crops. Nowadays, 46% of all irrigated land is utilized for cotton. Cotton is considered as most important crop in Uzbekistan. Production of cotton plays a dominant role in the economy of Uzbekistan. Remote sensing assists to create information at a large spatial scale. Therefore, it was used for prediction of cotton yield in Tashkent province, which has 15 administrative districts and 12 of them are located in the agricultural zone. The prediction was made for Tashkent province (oblast) level and for the administrative district level. Classification of cotton fields using MODIS (MOD13Q1) was applied by using a decision rule with thresholds on the Normalized Difference Vegetation Index (NDVI) for day of the year 113, 161 and 225. NDVI data was acquired as 16-day maximum value product in order to get information about biomass condition. Different NDVI-based indicators were studied and analyzed in terms of choosing the best option for that region. A regression model between NDVI indicators and yield was applied using an overall temporal trend component and yearly deviations from this trend. The temperature and precipitation were studied in order to find a relationship among yield, NDVI and weather conditions. The correlations between these factors were low and it is assumed that at least weekly observations of cotton are required for establishing a better correlation between NDVI and weather conditions. Research has been successfully done and yield for 2013 was predicted at the province level as well as at administrative districts level. Province level and administrative district level showed difference in most valuable indicators, relationships between weather conditions and yield and correlation between weather conditions and NDVI. As indicators maximum value of NDVI and seasonal sum of NDVI (iNDVI=NDVI integral) were used in this research. Results have shown that MODIS 250 m spatial resolution is not the most suitable satellite sensor for Tashkent region and its administrative districts. In addition, there are many varieties of fruits, vegetables and other crops that make it very difficult to classify and make an error assessment of the classification and delineation of the cotton, as the cotton growing calendar is similar to the one of other crops in the region. The results obtained at the province level showed a decrease of yield in 2013 with 0.2 c\ha. The model was validated by the leave-one-out cross-validation (LOOCV) procedure and the calculated RMSE for the model at the Tashkent province level expressed a low value (RMSE=0.034), which means that error was low. The best indicator was identified for the province level and for each administrative district level separately, subsequently the forecasting was done for all research levels. Finally, the correlation between the ground truth data (historical data) and NDVI forecasting was found to be R2=0.55, which means that there was moderate correlation. Results obtained from this research indicate that alternative satellites and indicators have to be assessed and analyzed. The harvesting of the cotton and its time planning can be improved, as well as agricultural management, in order to get better outcomes.

Table of contents Acknowledgements ...... iv Abstract ...... v Table of contents ...... vi Abbreviations ...... vii 1. Introduction ...... 1 1.1. Background (Literature review) ...... 1 1.2. Yield prediction ...... 2 1.3. MODIS data...... 3 1.4. Problem Definition ...... 4 1.5. Research Questions ...... 4 1.6. Research objectives ...... 4 2. Methods and Materials ...... 5 2.1. Study Area ...... 5 2.2. Cotton ...... 6 2.3. Materials description ...... 7 2.3.1. Methodology ...... 7 2.3.2. MOD13Q1 Production ...... 9 2.3.3. NDVI Metrics description ...... 9 2.4. NDVI Analysis ...... 10 2.5. Classification ...... 11 2.6. Summary of methodology (Flow chart) ...... 14 3. Results ...... 15 3.1. Tashkent Province (Oblast level) ...... 15 3.1.1. Classification ...... 15 3.1.2. Validation of the cotton raster layers ...... 16 3.2. Yield analysis ...... 18 3.3. NDVI Metrics ...... 19 3.4. Results for administrative districts (District level) ...... 23 3.5. Validation of forecasting model ...... 27 4. Discussion ...... 28 5. Conclusions and Recommendations ...... 30 6. Reference list ...... 32

Abbreviations

AVHRR Advanced Very High Resolution Radiometer, LR-sensor on-board of the NOAA- satellites

B Blue

CGMS Crop Growth Monitoring System, the combination of an agrometeorological crop growth simulation model WOFOST, a database, and a yield prediction routine.

CropSyst Cropping System simulation model

DEM Digital Elevation Model

DOY Day of the Year

EOS Earth Observation System

EVI Enhanced Vegetation Index fAPAR Fraction of Absorbed Photosynthetically Active Radiation fPAR Fraction of Photosynthetically Active Radiation

GDP Gross Domestic Product

GIS Geographical Information System, software for storage of geographical data, mostly in vector format iNDVI NDVI integrated time series

LAI Leaf Area Index

LOOCV Leave-one-out cross-validation

MODIS Moderate Resolution Imaging Spectroradiometer

NASA National Aeronautics and Space Administration

NDVI Normalized Difference Vegetation Index. RS-indicator for amount of standing vegetation

NIR Near infrared range of the spectrum, roughly from 780 nm to 1300nm

NOAA Series of near-polar satellites monitored by the US National Oceanographic and Atmospheric Administration

NPP Net primary productivity

R Red vii

Rell.eff. Relative efficiency

RMSE Root mean square error

PAR Photosynthetically Active Radiation

RS Remote Sensing: earth observation with imaging sensors on-board of space/airborne platforms

SPOT Système Pour l’Observation de la Terre

USDA United States Department of Agriculture

UzHydromet Centre of Hydrometeorological Service at Cabinet of Ministers of the Rebublic of Uzbekistan

WOFOST World Food Studies crop growth model, simulation model

VI Vegetation Index

viii

1. Introduction

1.1. Background (Literature review) Assessment and forecasting of crop yields are important for each country. Central Asian countries are very rich of agricultural lands covered by various crops. Uzbekistan has the largest sector of agriculture among five Central Asian countries. The economy of Uzbekistan mostly depends on agriculture (Abdullaev et al., 2007). Agricultural accountings of Uzbekistan economy cover about 30% of Gross Domestic Product (GDP), 40% of employment and 60% of foreign exchange earnings (Abdullaev et al., 2007). There is 45 million hectares of land, where 60% of the land is used for agricultural purposes and 12% of this area are irrigated (FAO, 2003). Cotton is the key crop in agricultural production of Uzbekistan. Other major cotton-producing countries are USA, China, India, Pakistan, Uzbekistan, Turkey and Australia (Reddy et al., 2000). Nowadays, cotton is considered as the main crop on Uzbekistan agricultural lands and its production plays a dominant role in Uzbekistan. Among all irrigated lands in this area, 46% is covered by cotton (Zhou et al. 2007). Uzbekistan was considered as an important cotton- growing region even in Russian Imperial times. The cotton lands were enhancing during the Soviet Union, particularly after 1950. Main external influence to cotton yield is generally caused by rainfall, temperature, incoming light, and nutrition. According to Muminov (1973), Central Asia is an area with inadequate moisture and dry type of climate, therefore soil moisture is an important factor for cotton yield evaluation in Central Asia. Water mainly is taken from two main rivers, which are Amu Darya and Syr Darya. Amu Darya and Syr Darya are two main tributaries of the Aral Sea (Abdullaev et al., 2007). The estimation and forecasting of cotton crop yield is of importance for better targeting of water resources, proper land-use planning, time management, proper use of labor forces, enhancement of production and establishment of classification maps at a regional scale (Ruecker et al., 2007). Remote sensing is one of the technologies which gives an unbiased vision of large areas, provides spatial information and is widely used in assessment and forecasting of crop yields at a regional scale (Doraiswamy et al., 2004). Satellite images allows the accumulation of valuable information for the determination of relationships with ground truth data by using spectral characteristics of the fields with expected harvest (Terekhov et al., 2007). The normalized difference vegetation index (NDVI) is an indicator which uses visible and near- infrared bands of the electromagnetic spectrum and can be useful for forecasting of crop yields. A vegetation index such as the NDVI is adopted to analyze the remotely sensed measurements and to assess whether the observed target contains live green vegetation or not. Large size fields make it possible to use satellite information of medium resolution to study the spectral characteristics of crops (Terekhov et al., 2007). Forecasting of the crop yield can be based on empirical data using vegetation indexes, obtained from remote observation of the fields that take place every year during the earing – flowering time (late July-early August). According to Terekhov and Kauazov (2007), this period allows observation of the fields due to the amount of green biomass and close relationship between the amount of plant and productivity. Moreover, it can be a basis for forecasting model of crop yields. Aim of this research is to forecast the cotton yield in Tashkent province by using remote sensing techniques. The current methods of crop yield prediction in Uzbekistan are outdated. To renew 1

methods of prediction in Uzbekistan and in order to improve the time management and amount of needed labor forces during the cotton harvest, accurate remote sensing prediction is needed. Precise forecasting can be helpful not only for decreasing redundancy of time planning and labor forces during cotton picking season, but also for good, accurate and statistically straightforward prediction.

1.2. Yield prediction Optical remote sensing techniques work sufficiently with agricultural systems, since remote sensing provides information about actual status of plants at different stages of growth through the reflectance and spectral signatures of a crop. This technique helps to identify crop species of interest and also diseases, weed infestations, density, and other values of agricultural variables (Soria-Ruiz et al., 2004). These variables can be used as an input for crop growth models as yield indicators (Clevers et al., 1993). The status of a crop can be obtained by using different indicators or vegetation indices like NDVI (Tucker, 1979). There are different purposes for yield prediction in agricultural management. Among the main goals are to categorize and forecast the amount of crop yield and to guarantee resources for the population, obtained by agricultural and environmental services. Crop yield prediction for large regions and for the period before the harvest time is rather an important and big problem for many countries (Soria-Ruiz et al., 2004). Moreover, there are a lot of different models with various issues associated with them (Soria-Ruiz et al., 2004), such as models which build and rely on computer simulations and include weather-related variables. Other models are generally statistical approaches which require several regression equations. In addition, there is no single model, which can be suitable for all regions and environments. All crop growth prediction and estimation models are limited to different conditions due to the reason that they were used and simulated in specific research contexts and sometimes for areas as small as one field. Moreover, they have to be used in combination with other inputs provided by various resources such as geostatistical and geo-information systems (Soria-Ruiz et al., 2004). Previous empirical studies were done to establish the statistical relationship between crop yield and climate stresses. These studies had as purpose crop yield estimation and prediction (Liang et al., 2012). The studies showed that the relationships are very complex and depend on farming practices, climate and soil characteristics during the growing period. According to Liang et al. (2012), weather conditions and soil characteristics have a strong influence on cotton yield. Different vegetation indexes were used, checked and developed in order to apply in one of the existing models. Spectral vegetation indexes (VI) were used in order to measure the green biomass at any given time. Satellite data such as NOAA-AVHRR (National Oceanic and Atmospheric Administration – Advanced Very High Resolution Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) have been used for different models run at daily, monthly and yearly time step (Rucker et al. 2007a). Linear relationships were applied between the fractions of photosynthetically active radiation (fPAR) and biomass of cotton. To estimate the fPAR, NDVI was derived from AVHRR and the Monteith model was applied (Bastiaansen, 2003). Moreover, different satellites and sensors have been extensively used to monitor the condition of the crop and forecast crop yield and production in many different countries. Studies were done to predict the yield by using remote sensing techniques. 2

Several studies by using a crop growth model instead of using the regression models have been applied worldwide. One of the studies was done in order to predict yields using meteorological information. The crop growth monitoring system was created for Europe and this study led to the objective of another research, which was to monitor agricultural conditions over the whole of the European Union and neighboring countries, and because of the importance of wheat in Uzbekistan, the model was adapted there in order to make a quantitative within season yield forecast at regional and national scale for specific crops by using the Crop Growth Monitor System (CGMS) (Pulatov, 2008). Many studies have been performed using the AVHRR satellite in order to monitor and forecast malting barley yield in Germany (Pulatov, 2008). Many research works have been made in the last years to investigate the contribution of remote sensing data for crop monitoring and yield prediction. In terms of forecasting the cotton yield, MODIS (MOD13Q1) NDVI products have been used for Tashkent Province in this research. The spatial resolution of MODIS is 250m per pixel. The research is aimed on identifying the best indicator in order to predict cotton yield at Province (oblast) level and at administrative district level. Forecasting of the yield can lead to better crop management, labor forces and time management and to know whether this satellite is suitable and sufficient enough for the scale of the field in the province.

1.3. MODIS data According to the National Aeronautics and Space Administration (NASA), there is MODIS (or Moderate Resolution Imaging Spectroradiometer) instrument, which is a key instrument on aboard of TERRA and AQUA satellites (Lindsey & Herring, 2011). TERRA satellite passes from north to south across the equator in the morning, while AQUA passes from south to north and crosses the equator in the afternoon (Lindsey & Herring, 2011). TERRA and AQUA MODIS acquire 36 spectral bands (Lindsey & Herring, 2011). MODIS is playing an essential role in gathering global data and improve our understanding of global dynamics of land surface and processes in the oceans and lower atmosphere. MODIS is a key instrument for developing Earth System models able to monitor and predict the changes on the surface (Lindsey & Herring, 2011). In order to select a good model for cotton crop yield forecasting, a variety of studies on this issue which were done in diverse countries were investigated and explored. Generally, use of MODIS for crop yield estimation is often applied. The majority of studies have been conducted in order to relate NDVI derived from MODIS with crop yield by monitoring the vegetation conditions, drought, estimation and forecasting. The pros of MODIS are that it has good spatial resolution (250m) and for example better radiometric calibration than AVHRR (Mkhabela et al., 2011). In addition, studies on the relation of MODIS with crop yield have been conducted in Uzbekistan but in another province, conditions and different environment (Ruecker, 2007c). Another study, performed in China was done in order to test the suitability of the methodology to estimate crop yield with MODIS NDVI on a regional level (Ren et al., 2008). The study on the crop yield forecasting on Canadian prairies was taken as an example. The objective of abovementioned study was to evaluate the possibility of using MODIS-NDVI to forecast crop yield on the Canadian prairies and also to identify the best time for making a reliable crop yield forecasting (Mkhabelaa, 2011). Additionally, another research on the near real time prediction of corn yield in the U.S. was done using MODIS derived Wide Dynamic Range Vegetation Index (WRDVI) (Sakamoto et al., 2014). 3

Based on the studies described, in terms of crop yield prediction by using MODIS satellite, this research explained below is going to be done.

1.4. Problem Definition The cotton harvest is time consuming and needs many labor forces. Due to outdated methods of empirical forecasting, the amount of labor forces and needed time is unknown. The redundancy of labor forces during the cotton harvest and weak time planning used for that, takes a lot of financial and human resources. In order to improve the time management and amount of labor forces, which are needed for the production of cotton, remote sensing based yield forecasting will be done in this research as an example of Tashkent province in Uzbekistan. Nowadays, two most used approaches to forecast crop yield are the empirical regression model and the biophysical crop model (Kogan et al., 2013). Currently, insufficient input data are available in Uzbekistan for applying a dedicated cotton growth model. So, an empirical regression model will be used in this research. It requires some selected predictors such as a vegetation index derived from satellite images, meteorological data and historical data of cotton yield in last 10 years (Kogan et al., 2013). Satellite data can provide continuous, timely, human-independently information for large territories (Kussul et al., 2009). The prediction of cotton yield may help farmers to improve the harvesting time, reduce the risks, which they may meet with the production, and it also may help the government to determine harvest plans strategically.

1.5. Research Questions

1. How can remote sensing prediction improve logistics of harvest in Tashkent province? 2. How accurate is the remote sensing prediction in comparison to ground truth data? 3. Which of the variables in NDVI metrics is valuable to identify the growing season?

1.6. Research objectives

1. Build statistical model for prediction of cotton yield 2. Validate the forecasting model 3. Determine the correlation between statistical data and remote sensing data 4. Identify the best variable for NDVI metrics 5. Study harvesting logistics in Tashkent province

2. Methods and Materials

2.1. Study Area The study area of current research is Tashkent province (oblast) of Uzbekistan (Figure 1). Figure 2 illustrates the MODIS tile and the Tashkent province as an image. There are 14 administrative districts: Bekobod, Bostanliq, Buka, Chinaz, Qibray, Ohangaron, Oqqurgan, Parkent, Pskent, Quyichirchiq, Urtachirchiq, Yangiyul, Yuqorichirchiq and Zangiota districts. Tashkent is the capital of the country and capital of the province, which covers an area of 15300 sq. km. The major cities of the province are Angren, Olmaliq, Ohangaron, Bekobod, Chirchiq, Tashkent, Yangiobod, and Yangiyul. The study was done for the agricultural zone of the province. Agricultural zone contains not all the districts. As a result, the analysis was done for Bekobod, Buka, Chinaz, Qibray, Oqqurgan, Pskent, Quyichirchiq, Urtachirchiq, Yangiyul, Yuqorichirchiq and Zangiota districts (shown below in Figure 3). The analysis and forecasting was done for all districts individually and at the province level as a whole. The Tashkent province is bordered at the northeastern of Uzbekistan by ranges of mountains named “Tian Shyan”. The main crops are cotton, grapes and grain cultivation, as well as silkworm breeding, fruits and vegetables, cereals and citrus fruits are increasing. Substantial parts of the province in the south and southwest are foothill flatlands. Tashkent features a Mediterranean climate with strong continental climate influences. The weather in Tashkent is characterized by cold and often snowy winters, but with long dry and hot summers. In total, the winter consists of about 32 fully covered snowy days. Humidity level of air is around 56% on an annual basis.

Figure 1 Digital Elevation Model representing Tashkent province location Figure 2 Illustration of extent of Tashkent province on MODIS tile such as MOD13Q1 (250m)

Figure 3 Agricultural zones and other area of Tashkent province.

Agricultural zone was taken as study area and the area covered by cotton was taken by applying a pixel-based NDVI threshold classification on the agricultural zone. Threshold is explained later in the classification section of the report. In Figure 3, the white color areas are different administrative districts, which are located in the agricultural zone as explained before, area with orange lines is an area not taken and not calculated for the prediction.

2.2. Cotton Local varieties of cotton were planted in the study area. The sowing dates starting from the beginning of April until the middle of May (Muminov, 1973). First harvesting was in the middle of September. According to Centre of Hydrometeorological Service at Cabinet of Ministers of the Republic of Uzbekistan (UzHydromet), cotton is distinguished for 5 phases:

1. Planting 2. First square 3. Flowering 4. Accumulation of cotton bulb 5. Harvest

Yields of cotton crop depend on the number of mature cotton bulb (balls) on the plant. For the time of planting temperature plays a big role. The most suitable temperature to sow is 10 degrees °C (Muminov, 1973). Freezing also plays an essential role in the development of the crop. Sometimes freezing can take place during the starting date of cotton growth, subsequently requiring re-planting of big areas. According to Muminov (1973) first leaves appear 10 days after sowing, if the temperature is larger than 10 degrees and next leaves start to appear in 4-5 days range after first leaves. The rate of growth increases and becomes more intense and leaves appear with intervals of 2-3 days. The agricultural management (fertilizer, cultivation, crop rotation, soil organic matter, etc.) plays essential role in cotton growth and productivity as well.

2.3. Materials description In this study, following datasets were used:

- MOD13Q1 product of MODIS at 250m resolution for 2000-2013 years - Yearly meteorological observations from 15 stations in Tashkent province for 2002-2013 - Historical data on cotton yields for Tashkent province for 2002-2013 - Historical data on cotton yields for all administrative districts in Tashkent province for 2002-2013

2.3.1. Methodology A regression model was used as an approach to predict single dependent variable (cotton yield) by a set of independent variables such as meteorological data and remote sensing data. The ground truth data (historical) were obtained from the State Statistics Committee of Uzbekistan. This data, on cotton yield for the last 10 years, was used to predict the crop yield and make a validation of the empirical prediction. Research was done at oblast (Province) level and administrative district level. Oblast level is a sub-national administrative unit. Satellite data such as MODIS were used, in order to test the suitability of spatial resolution for Tashkent province and administrative districts of Tashkent province. Vegetation index NDVI was derived from Terra MODIS satellite sensors product. MODIS product MOD13Q1 was downloaded for whole period of planting-flowering-harvest. The months for cotton calendar are approximated and determined as March-September. The MODIS product MOD13Q1 will be described and introduced later in this report. Meteorological data was obtained from the archive Uzhydromet. It includes information from 15 meteorological stations of Tashkent province. All stations provide monthly meteorological information such as precipitation, rainfall and average minimum temperature and average maximum temperature. All models were validated, calibrated and developed using official data on cotton crop yield at province/oblast level for the period 2002-2013. I applied repeated procedure, which means models were first calibrated and developed for the period 2002-2012 and forecasts made for 2013 and 2014. Linear regression model for cotton yield forecasting with use of satellite data derived NDVI were applied. Statistical cotton yield data have been derived from the State Statistics

Committee of Uzbekistan for Tashkent province (2002-2013) and administrative district (2003- 2013). The regression model shown below was used for yield representation:

Yi = Ti +dYi (1)

Where Yi is predicted yield, Ti is agricultural changes and in this study agricultural changes were taken as a trend component of the yield during 11 years. Time series show yearly changes, which can be compared with yearly NDVI and, moreover, it shows the deviation of the yield. DYi is representing the difference of actual yield from the trend line (Kogan et al., 2013).

Deterministic component of trend Ti can be acquired from the equation below:

Ti = a+b (year) (2) Where a is intercept of the trend and b is slope of the trend line of certain year. Yearly variations have been estimated using the next regression model, which connects the cotton yield dYi and NDVI.

dYi = Yi - Ti =f(NDVI)=b0+b1*NDVIij (3) b0 and b1 was applied to include year effects (Street et al., 1988). NDVIij means NDVI indicator such as maximum or integrated time series (Sum of NDVIij=iNDVI=NDVI integral) for some year (i) and j represents the day of the year (DOY). The deviations of NDVI during the growing season (14 times) for each year were obtained in order to investigate the strength of the relationship with NDVI and to determine whether the critical point has a strong correlation, when the crop productivity is highly sensitive to weather conditions. Leave-one-out cross-validation procedure was implemented for the prediction model. It means that the forecasting model were developed for the years 2002-2012 at province level and the years 2003-2012 at administrative district level (test data) in order to calibrate data for all years except one. Therefore, for each province, each administrative district and each DOY for which NDVI values are available, the n predicted values for cotton yield for testing data in this case n=11 for province level and n=10 for administrative district level. Subsequently, after calibration, model was used for the one left year (for instance 2013) to make a prediction for, in this case, 2013. Validation was done by using the statistical data for 2013 obtained from State Statistics Committee. In order to see the correlation between predicted value and statistical yield this procedure was repeated for each year, meaning each run one year was left out of the calibration and only used for validation. Afterwards, statistical data and predicted values were compared. Root mean square error (RMSE) was calculated, in order to see an error from the forecasting model. To estimate the RMSE the official historical data and predicted data values were used. RMSE was calculated by equation below (Kogan et al., 2013):

((푃푖)−(푂푖))2 푅푀푆퐸 = √∑ , (4) 푛

Where Pi and Oi are estimated yield data (Ti) and historical data of cotton crop, respectively. Ti also can be determined as slow agricultural changes indicator.

2.3.2. MOD13Q1 Production Global MODIS vegetation indices are designed to provide spatial and temporal data of vegetation conditions. B, R, NIR reflectance were used to identify vegetation indices. MODIS NDVI products provide us with time series for historical applications (USGS, 2014). MOD13Q1 has not only NDVI products but Enhanced Vegetation Index (EVI) products as well. EVI maintains sensitivity at a dense canopy and minimizes variations of canopy background. MOD13Q1 is computed from bi-directional surface reflectance and has been masked for clouds, water, shadows etc. (USGS, 2014). In addition, the product data provides 250 meter pixel size every 16 days. Due to its ease of application, simplicity and familiarity, VIs have wide range of users (USGS, 2014), (Solano et al., 2010). For more precise explanation of MOD13Q1 images downloaded by Julian days, Table 1 was created. It illustrates Julian days, which belongs to acquisition dates and the range of the day acquired by MODIS satellite. Table 1 Illustration of downloaded MOD13Q1 images with NDVI for growing season available for 2002-2013 years

Day range Acquisition Date Julian Day 61-91 03.06 065 61-91 03.22 081 92-121 04.07 097 92-121 04.23 113 122-152 05.09 129 122-152 05.25 145 153-182 06.10 161 153-182 06.26 177 183-213 07.12 193 183-213 07.28 209 214-244 08.13 225 214-244 08.29 241 245-274 09.14 257 245-274 09.30 273

2.3.3. NDVI Metrics description NDVI metrics are used to characterize the growing season under study. There are two approaches to identify the growing period. Phenological metrics can be derived from satellite data as a threshold-based approach and time-series of NDVI data using a curve derivative method (USGS, 2011). The threshold-based approach use some values, which can be relative or pre-defined values at which vegetation activity is assumed to begin. The timing and progress of the plants may help researchers to make a conclusion about condition of the plants and their environment (Reed et al., 1994). This study will derive the measures (metrics) from 10 years of NDVI time series data within the period March-September. These NDVI time series data will be derived from satellite data product MOD13Q1 with resolution of 250 m. Measurements with NDVI metrics will provide information, which gives the ability to track the seasonal characteristics of cotton more closely. NDVI indices will be obtained 14 times for 16-day periods

from March to September. Moreover, 14 data values are available for each of the 10 years, which in total are 140 data values. The past efforts concluded assigning a threshold NDVI value for the activity events of the plant (Reed et al., 1994). The NDVI values were investigated to identify the photosynthetic activity and deceleration of photosynthesis. The metrics that may be used as an indicator are shown in Figure 4:

 Start of growing season (OnP)  End of growing season (EndP)  Duration of growing season (DurP)  Maximum NDVI during growing season (MaxV)  Time integrated NDVI during growing season (TINDVI)

Figure 4 Phenological metrics from temporal NDVI profile (Reed et al., 1994)

Different indicators were described and studied in terms of precipitation and vegetation dynamics. Two studies were done in two study areas in different climatic zones, but generally with high amount of precipitation during the growing season of crops (Pulatov, 2008). Finally, there was undertaken the investigation of the main driving indicators for prediction. Integrated NDVI, which is equal to the sum of NDVI (iNDVI=sum of NDVI), is assumed as good indicator for seasonal variation of different land types. In the current study, two indicators were taken in order to see the relationship of NDVI to the yield and to predict the cotton yield in Tashkent province and administrative district level. One of the best indicators is NDVI integral, which is sum of the whole growing period (Kastens et al., 2005). This indicator can help to obtain the acreage of the area by summation of the most greenness period, and it was found out that the majority of yields is highly correlated with integrated NDVI summarized around the time of maximum greenness (Tucker et al., 1980). The maximum NDVI and sum NDVI, which is the integrated NDVI values over the year, derived from MODIS (MOD13Q1) provide with information on yield prediction (Pulatov, 2008).

2.4. NDVI Analysis NDVI values were taken for the Agro district of the Tashkent oblast for Julian days shown in table 1. All 12 year images were obtained from archive of U.S. Department of Interior

and Geological survey. NDVI analysis was done by choosing the proper and suitable indicators (max, integrated time series shown in Figure 4). In addition, in order to obtain the cotton pixels the thresholds were applied (explained in next section 2.5). The growing season was analyzed between beginning of March and the end of September. Respectively, all NDVI indicators were obtained for all years by repeating the whole growing season marked by Julian days. NDVI indicators were analyzed by applying the threshold for certain Julian days in order to repeat and suit the classification methodology of Platonov (2008) and Zhang (2011). Spectral signatures of cotton for MODIS were studied and determined by analysis of Figure 6 (see next section). Subsequently, certain dates were chosen and cotton was identified. During identification of cotton pixels, the values of two different indicators were obtained, which helped to analyze, understand and make calculation shown in results chapter.

2.5. Classification Classification gives an opportunity to model an earth’s surface. Studies on this research has a range of topics such as mapping of weed (Glenn, 2005); (Gokhale, 2006), modeling wildfire (Ercanoglu, 2006), forecasting urban growth, and other applications. According to the results of different scientists (Richards, 2005) classification accuracy depends on pixel’s location similarities with training sites. Error assessment is a critical point in analysis and interpretation process (Stehman, 1998).

Figure 5 Spectral signatures of different crops growing in Syrdarya province of Uzbekistan for Landsat 7 ETM+ (Platonov et al., 2008)

Cotton classification was made by applying NDVI threshold values and true-false decision rules in ArcGIS software. For example, NDVI<0.2 was assigned as bare soil. According to Platonov et al. (2008), there are several crops in this area, whose spectral signature is very similar to cotton at certain days of the year. However they can be separated at other days. As an example, cotton, rice, wheat crop and other crops are shown in Figure 5. This graph was

shown in order to know the similarities of the spectral signatures for different crops in Syrdarya area. Spectral signature used by Platonov was determined for Landsat ETM+ satellite. In order to apply NDVI, threshold values and decision rules to MODIS suitable values were explored and acquired. Figure 5 indicates that rice, cotton and wheat have very big similarity during the growing season and at a certain DOY. For example, rice has similarities with cotton spectral signatures on 110-114, 131, 160-162 Julian days and at the end of the growing season has almost the same signature on 274 DOY, while wheat has close pattern with cotton on 210, 226 and 274 DOY. So, to delineate cotton from other crops suitable values in comparison with Landsat 7 ETM+ were applied (Figure 6). In order to differentiate cotton from other crops the thresholds were examined from research done by Zhang (2011). That research expresses the cognitive information about the values, which were applied as a threshold for MODIS NDVI product, to obtain the cotton field pixels from the images. The NDVI thresholds such as NDVI< 0.2 for 065-161 DOY have been applied. That value indicates bare soil. Second threshold, which determines winter wheat-summer maize reflects the value such as NDVI> 0.35 and NDVI< 0.4 has been applied, moreover threshold for third critical point (225-273 DOY) of the flowering and harvesting period of growing season is the period when maximum cotton biomass reflectance have been caught. This period were indicated as NDVI > 0.63 and NDVI< 0.73. Last critical point helps us to get the spatially distributed cotton fields in that area (Zhang et al., 2011). The most difficult part of acquisition was the end of growing season (225-273 DOY) when several crop signatures showed crossing or had similarities as shown in the Platonov (2008) study. Then, some uncertainties had been discovered, such as absence of required mean value in certain layer for certain DOY. A pixel based threshold was applied to the images. Only three critical points had been taken due to three critical periods as advised by Platonov (2008) (Figure 5) and Zhang (2011) (Figure 6). Figure 5 was taken as an example of the differences of spectral signatures for the different crops during the growing season in that area for Landsat ETM+. Meanwhile, Zhang (2011) advised to use higher values for MODIS NDVI, which were applied for this research in order to delineate cotton with the MODIS (MOD13Q1) NDVI product. The images cover the growing season and have a frequency of one image close to the critical points of cotton spectral

Figure 6 Illustration of cotton, winter wheat-summer maize, and spring maize value MODIS NDVI temporal patters for China (Zhang et al., 2011)

signature such as April (113th day), June (161st day) and August (225th day). The limitations have been met on this research for images of some days in certain year due to applied thresholds. The issue of thresholds was not essential and were skipped at the moment of the research. The problems were in insufficient values of threshold for a time when assumingly cotton biomass, for instance, had maximum values at DOY 225, and in reality it has been less than the minimum threshold which was applied for the certain days of certain year. More often it has been met during the middle of the growing season. The classification gave NDVI values with lower average pixel values spread out around the whole region. The threshold was done for three images with NDVI by certain day of the year (NDVI DOY). The first moment was the beginning of the growing season as 113th day of the year, when the temperature degrees are equal or higher than the effective temperature (10 degrees Celsius). The minimum effective temperature for cotton growth has to be equal to 10 degrees Celsius, which means cotton is not growing in case of the less temperature. In order to record the period of wheat harvesting and cotton sowing (bare soil), 113th day was applied. The second day has been taken as 161st day of the year, which represents the middle of the growing season for the agricultural region. Cotton biomass has not yet grown enough for the high VI values representation, but assumingly it is possible to see the summer maize growth in order to divide these two crops from each other. The last spot of the obtained NDVI data have been 225th day, which lies on August 13th and can be counted as period with maximum NDVI value for cotton in Tashkent province level. Data from day 65-273 for growing season was gathered for 10 years. 65-273 DOY illustrates us the cotton growing calendar, which is expressed in Figure 6. To check the accuracy of classification, geo-referenced points in Urtachirchik district were taken as an example. All roads and cities were delineated from study area. The same classification was done for each administrative district. Validation for all regions was done by observations performed in Urtachirchik district. GPS points taken by me in the summer of 2012 during my internship and additional GPS points (coordinates), which were obtained by a first year master student of Tashkent institute of Irrigation and Melioration, obtained during his internship in 2014 were used. According to Platonov (2008), crops distributed over the area were recognized and validated with thresholds applied to maps. Due to visible and recognizable fields of cotton in Google maps, it was possible to check some area. The Urtachirchik administrative district was checked and validated by control points obtained by GPS Leica Geosystems. Control points were done near cotton and maize fields (Figure 9). As an additional validation, the planted area from statistics was compared with the area obtained from the classified cotton pixels using the NDVI thresholds. This comparison was done by using data on planted area under cotton in ha. These data were obtained from Tashkent statistical agency. Validation was done for all pixels for the whole agricultural area of Tashkent province.

2.6. Summary of methodology (Flow chart)

Figure 7 Schematic overview of methodology at oblast level and district level

Generally, the methodology of this research is followed by the flow chart indicated by the conceptual model in Figure 7. There are two steps: first step is for oblast (province) level and second step is for administrative district level. Each model has input and output. For instance, software such as R and Microsoft Excel will be used to acquire NDVI metrics as a time series with input from MOD13Q1 images of Tashkent province. Moreover, historical data obtained from Uzbekistan will be analyzed in statistical graphs. Regression analysis will be done to compare the output results collected from remote sensing and actual yield data. Results will be represented by the yield forecasting after yield analysis. To see the amendments of NDVI values, meteorological data were used and analyzed with the NDVI indicators.

3. Results

3.1. Tashkent Province (Oblast level)

3.1.1. Classification Thresholds used in the combination of DOY 113, 161 and 225 to identify cotton fields in the various years are given in Table 2. Since the precise cotton distribution map is not available, there are several different methods to check and validate the classification done in this research (see section 3.1.2). As an example, raster layers acquired after applying the thresholds for the signatures is shown for 2013 in Figure 8. Hereby, Figure 8 indicates three different days in three different colors and blue considered as bare soil (113 DOY) while green color was applied for the time of wheat harvesting. The time of wheat harvesting, which have been used for threshold (161 DOY) is the same period as cotton sowing time. So, it means that green color illustrates the cotton sowing period and wheat harvesting time. Finally, the red color was applied for the period of 225 DOY, when the cotton biomass was reflecting the maximum NDVI values.

Table 2. Thresholds applied to identify cotton fields for specific DOYs

DOY NDVI 113 < 0.2 161 > 0.35 AND < 0.4 225 > 0.63 AND < 0.73

Figure 8 Cotton map distribution of cotton pixels in the study area for 2013 obtained after classification

3.1.2. Validation of the cotton raster layers The major crops distributed over the agricultural zone in Tashkent province and NDVI of crops consistent with the growing calendar of crops shown in Figure 5. The maximum NDVI for wheat and other crops such as maize or orchard pixels is situated mainly on 161 DOY within the given thresholds. In general, cotton pixels did not recognized on rainfed areas in Figure 8. Control points obtained during my internship were used for validation of NDVI pixels in the study area and showed that cotton fields at August 13th had highest NDVI value of 0.675 during the 12 years of study (2002-2013). As a comparison, according to Zhang (2011) 0.73 is the maximum NDVI value, which was picked for cotton on their research. Red pixels illustrated in Figure 9 means that there are cotton fields on that area. The differences of the red color show differences in amount of biomass of cotton due to weather and agricultural management conditions (light tones represent less biomass). The points illustrated in Figure 9 expresses the territory of study during the internship and points were obtained mainly near the cotton areas. So, control points illustrate the fields covered nearby cotton fields and, for example, it is shown that the left top point is located near the cotton field, which had not good green biomass at the time image was taken. In general, all control points illustrate correct (meaning cotton) pixel layers on the fields. In the middle of the shape of control points, there were potato, water melon and other non-cotton, small fields, which correctly are not covered by any red pixels. On the right hand of the shape, control points were taken in maize fields, which are not covered by pixels as well. Six pixels illustrated on the bottom left are cotton as was discovered by the first year student. On the top of the image it is unknown whether the cotton fields are correct.

Figure 9 Ground control points obtained by Leica GNSS and GPS systems in 2012 nearby cotton, maize, orchards and other crops The pattern was validated in order to compare the special critical days, meanwhile the comparison showed good and almost similar pattern of cotton. As we can see on DOY 65, which is 6th of March, the pattern is less than 0.2 and the minimum value on DOY 113, which is 16

23rd April, is around 0.2 but increasing up to the maximum value (Figure 10), while maximum NDVI value appeared at the same time as in the pattern mentioned by Zhang (2011), on 13th of August (DOY 225). Those values show the similarities of the cotton pattern with Figure 6 and it may be concluded that the classification was done appropriately.

Comparison with pattern of Zhong (2011) to validate the classification 0.80 0.60 0.40

NDVI 0.20 0.00

Julian days

Figure 10 NDVI pattern for 2011 during four critical points indicates significance with the Zhang (2011) pattern of cotton. The validation mentioned in the end of section 2.5 refers to the area covered by the cotton pixels classified using the thresholds of table 2 for all the years as compared with the official area statistics for cotton. Figure 11 shows that points are close to the 1:1 line for most years, indicating that the threshold approach at least provides a good estimate of the total cotton area. Deviations can be explained because some cotton fields can have a very low biomass, resulting in a low NDVI value. The NDVI of cotton pixels was obtained after thresholds were applied. Meanwhile, the obtained NDVI of pixels is the reflectance of greenness and non-greenness of vegetation, which depends on solar radiation, photosynthetic activity (Ruecker, 2007), amount of precipitation, water balance (Muminov et al., 1973), soil organic matter, soil bioactivity and

Yearly planted area under cotton vs summarized NDVI pixel area 140 120 100 80 60 40 20

20 40 60 80 100 120 140 Plantedarea (thousandha) NDVI pixel area (thousand ha)

Figure 11. 1:1 Relationship illustration comparing the planted area with NDVI area in thoiusand ha

other reasons, which can affect the presence of amount of chlorophyll of vegetation (Ruecker, 2007) or absence of biomass.

3.2. Yield analysis

Figure 12 Yield (c\ha) against time series for 12 years, Tashkent Province The statistical data for the whole agricultural zone of Tashkent province expresses the information, which is valuable for the objectives. Time series of the gross yield has a positive trend line (Figure 12). Trend line was acquired by equation Y=0.4633x-905.69, where two variables of trend is slope and intercept. Therefore, the slope is represented by value of 0.4633 and the interception is -905.69. In addition, Figure 12 interprets that in 2002, 2003 and 2008 yield was less than in other points of the time series. Moreover, maximum value was obtained from 2006, continued by the decrease of the trend until 2008. Then it showed that in two last years crop growth was decreasing. Afterwards, statistical yield data was compared with temperature (Figure 13) to presume the influence of weather conditions to crop yield and see the reason of low yield in some years. After the comparison of monthly average of temperature with the yield, it interprets that there is no relationship (R2=0.01) with cotton yield (See below).

Relationship among Temperature and cotton growth 17.0 15.0

13.0 y = -0.0308x + 14.808 11.0 R² = 0.0103

9.0 Temperature 7.0 5.0 20.0 22.0 24.0 26.0 28.0 30.0 Cotton Yield c\ha

Figure 13 Relationship between cotton gross yield (c\ha) and temperature average over 12 years

There is an assumption that, due to unavailable daily or weekly observations of cotton, the relationship between variables is absent. Precipitation in the study area mostly is in autumn-winter-beginning of the spring, while cotton growth calendar starts in the end of March – beginning of April. Cotton is planted in irrigated zones in Tashkent province and data such as monthly (growing season) or yearly average of precipitation is not much playing a role in discovering relationship. The optimal amount of precipitation for the cotton biomass is 5000-8000 m3\ha (Anonymous, 2014). But generally, low amount of rainfall has to be fallen during the cotton growth, because at the same time the amount of precipitation is valuable as well. If water content is not enough, cotton biomass leave out the lowest layer of the boxes, in order to give the strength for the growth and productivity to the rest (Anonymous, 2014). Freezing has bad influence on cotton growth too (Muminov, 1973). Additionally, soil in Central Asian region is inadequate (Muminov, 1973) and soil has to be moist during the seeding period. This could be a reason of the low gross yield during 2007-2008 years. Assumptions are that freezing was longer than usually, and farmers started cotton seeding later than usually. No relationship trend line can be caused by applying the monthly average data obtained from Uzhydromet rather than daily observations in the fields.

3.3. NDVI Metrics

Figure 14 NDVI max changes over 12 years for cotton The only two indicators studied in this research were the NDVImax and the NDVI integral. The reason of using these two certain indicators are that different studies investigated two indicators as the best indicators for such kind of studies and they are most essential for forecasting.

Figure 14 illustrates the time series of maximum NDVI values for cotton biomass over 12 years (2002-2013). Thus, changes illustrated in Figure 14 says that NDVI maximum indicator varied not much and expresses very small range of changes during 12 year time series for Tashkent province level. These small changes indicating negative decrease of an NDVI value for cotton biomass, which leads to assumption that the temperature is increasing, which in its turn can be observed in the statistical data and the biomass slightly became drier, and uniformly lost chlorophyll content.

NDVI integrated time series 5.100 5.050 5.000 4.950 4.900

4.850 iNDVI 4.800 4.750 4.700 4.650 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year

Figure 15 Changes of NDVI integrated time series over 12 years

Figure 15 expresses NDVI integral (iNDVI) changes over 12 years. iNDVI shows wide range in changes over 12 years and that graph has cognitive information for research. Figure 15 also illustrates the presence of agricultural changes as cotton growth increased and decreased over 12 years in comparison with Figure 14. Figure 15 expresses that cotton NDVI integral is decreasing over 10 years but for the last years the pattern shows development and increase, which can be the influence of agricultural improvements. NDVI integral values is decreasing from 2003 (4.890) then the maximum value was caught in 2008 (5.043) and shows more temporal variability than the weather variability during 12 years. The changes over 12 years of NDVI values shows that the cotton NDVI values are decreasing, which means that biomass is not growing in some areas anymore or another assumption is that farmers started to pay more attention to production of cotton boxes than to growth of biomass. The causes of this kind of assumptions are that NDVI is the reflection of greenness and non-greenness of vegetation, absence and presence of chlorophyll amount in biomass, which is dependent on different variables (solar radiation, dry matter content, soil organic matter, herbicides, fertilizers, water content), but not cotton bulbs (section 3.2.). The increase of production in 2013 makes to think that predicted yield can be higher (Figure 15).

Figure 16 and 17 illustrate the relationship between the deviation of cotton yield from the trend line and NDVI indicators. Both indicators at province (oblast) level showed weak correlations (R2=0.0067 for maximum NDVI and R2=0.0838 for NDVI integral). There is an assumption that there is an outlier in both graphs, whereas the indicator relationships with the outlier included are shown. An outlier is interpreted as the maximum deviated point. iNDVI show us an outlier with a big deviation on point, where deviation is equal to 4.31 and NDVI indicator is equal to 4.9, while all other deviation values varying near trend line. The negative trend line can be understood as the biomass reflectance was high and rich with chlorophyll content, while yield (cotton boxes) production was low. It means that cotton plant was wet and did not produce the cotton or production was later than usually, as biomass produces cotton boxes when the plant becomes dry (Anonymous, 2014). In order to produce cotton, farmers increase dry matter content with herbicides. So, assumptions are that herbicides were added to the biomass late August or beginning of September. Generally, there is state standard for cotton growth management, but each farmer can adjust these schemes according to his own circumstances and conditions, i.e. skip one irrigation or change the date and time of agricultural input activity due to weather condition or farmer circumstances (Anonymous, 2014).

Illustration of yield deviation and NDVI max 5.00 4.00 3.00 2.00 y = -70.003x + 46.719 R² = 0.0067

1.00 DYi 0.00 -1.000.664 0.665 0.666 0.667 0.668 0.669 0.670 0.671 0.672 -2.00 -3.00 NDVI max

Figure 16 Regression model for Tashkent province. Estimated parameters for Eq.(3) were b0=46.719 and b1=-70.003

For all levels the linear trend was chosen. The assumption consequently obtained after studying Figure 16 and Figure 17 can state that NDVI indicators and MODIS satellites spatial resolution of 250m is not the most suitable and good enough for the oblast level. The best indicator for province level is stated as NDVI maximum, it became known after RMSE value was calculated for all tested indicators. Obtained RMSE for NDVI maximum is small and announcing a low value such as 0.14, which means that there is a weak error, and accurate prediction can be done by using this indicator.

Illustration of yield deviation and NDVI integrated 5.00 4.00 3.00 2.00 y = -6.6608x + 32.624 1.00 Dyi R² = 0.0838 0.00 -1.004.750 4.800 4.850 4.900 4.950 5.000 5.050 5.100 -2.00 -3.00 iNDVI

Figure 17 Regression model for Tashkent province. Estimated parameters for Eq.(3) were b0= 32.624 and b1=-6.6608 The forecasting of the yield for 2013 is giving a positive result and it interprets that yield will decrease in comparison with the previous year but cotton production increase. Figure 18 represents the predicted yield (red line) after using the LOOCV procedure. In order to see the correlation between original yield and predicted yield the scatter plot Figure 19 is created. After the LOOCV procedure was applied the prediction was fitted and done for 2013. Correlation shows good relationship on Figure 19, which means that prediction is accurate.

Representation of predicted yield and observed data time

series ha \ 30.0 25.0 20.0 15.0 10.0 Observed data 5.0 Predicted yield

0.0

Predicted and observed yield c yield observed and Predicted

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Year

Figure 18 Illustration of predicted yield and original yield obtained from State Statistics Committee

Corelation between Statistical data and predicted yield 29.0

27.0

25.0

23.0

Observed Observed data 21.0

19.0 19.0 21.0 23.0 25.0 27.0 29.0 Predicted yield

Figure 19 Relationship between original yield data obtained from State Statistical Committee

3.4. Results for administrative districts (District level) For each individual district various results and relationships between predicted yield and observed (original) yield were found, but the strength of the relationships depended upon the amount and quality of the imagery used. Figure 20 illustrates that cotton yield in Bekobod district increases year by year. It also shows unexpected fluctuations in 2004-2005 and 2007-2008 years. Moreover, it can be stated that NDVI integrated performed well in the relationship with yield deviation over 11 years for almost all districts. All districts show weak correlation, while the strongest is for Bekobod district. It shows positive correlation and RMSE is 0.28 (Figure 24). The linear trend indicates the performed correlation. The NDVI integrated indicator shows the best results for Bekobod, Buka, Oqqurgan and Urtachirchik districts, while NDVI maximum shows better results in other districts (table3). Several districts also had obstacles in this

Hystorical data time series for Bekobod district 30.0 25.0

20.0 ha

\ y = 0.6409x - 1264.2 15.0 R² = 0.3375

Yield Yield c 10.0 5.0 0.0 2002 2004 2006 2008 2010 2012 2014 Year

Figure 20 Historical data for Bekobod district. Estimated a0=0.6409 and a1=-1264.2 for that district as slope and intersect for Eq. (2)

research such as not complete set of historical data.

Illustration of yield deviation and NDVI integrated 6.00 4.00 R² = 0.1807 2.00

0.00 DYi 4.6500 4.7000 4.7500 4.8000 4.8500 4.9000 4.9500 -2.00 -4.00 -6.00 NDVI integrated

Figure 21 Illustration of yield deviation and NDVI integrated

As an example, Bekobod district is taken and shown. The best indicator for that district is iNDVI. The relationship shown in Figure 21 illustrates the deviation of the yield and iNDVI, where it was explored very low with negative linear trend. Figure 21 expresses the cognitive information such as illustration of the period when the production of the yield is high, NDVI expresses low values, or when the yield is low but biomass reflectance is high. The assumptions are that the agricultural management of cotton requires different inputs such as herbicides, before harvesting to drive the biomass for drought. If biomass reflects well, the assumption can appear such as there was rainfall in that area and cotton had increased water content, which subsequently leads to less or late production while NDVI reflectance is high.

Illustration of temperature relationship with NDVI integrated in Bekobod district 24.0 5.0000 23.0 4.9000 22.0 4.8000 21.0 4.7000 20.0 iNDVI 4.6000

Temperature 19.0 18.0 4.5000

Year

Temperature mean iNDVI

Figure 22 Temperature influence on NDVI integrated in Bekobod district

Illustration of temperature relationship with NDVI integrated in Bekobod district 4.9500 4.9000 4.8500 R² = 0.3416

4.8000 iNDVI 4.7500 4.7000 4.6500 20.0 20.5 21.0 21.5 22.0 22.5 23.0 23.5 Temperature mean

Figure 23 Correlation of the temperature and NDVI integrated in Bekobod district Comparison of the influence between weather conditions and NDVI (reflectance of biomass) shows similarities from 2007. Meanwhile, temperature shows low relationship with NDVI integrated. The reason could be that data used in this research, is an average of the temperature for the growing season of the cotton (Figure 22), which means there are no critical dates with high or low temperature inside. Positive correlation (Figure 23) with value of R2=0.34 indicates that there is correlation between temperature and cotton growth. According to personal communication and interview (2014) with an anonymous interviewer, cotton growth depends on an optimal temperature, and positive trend proves this statement. Statement says that there is optimal temperature for good and normal tense of cotton growth. In case of decrease and increase of the temperature, cotton plant decreases or stop growing.

Correlation between predicted yield and observed historical yield 30.0

25.0

20.0 Observed Observed yield

15.0 15.0 20.0 25.0 30.0 Predicted yield

Figure 24 Correlation between predicted yield (Yi) and observed yield

This district shows good relationship and RMSE is 0.28 between predicted yield and observed yield (Figure 24). Figure 20 illustrates the historical data time series, which was correlated with predicted date in Figure 24. Table 3 illustrates the results of RMSE values estimation for all administrative districts of Tashkent province. The most suitable for all regions, as was mentioned before, is NDVI integrated. The correlation (R2) between predicted yield and observed yield is low for almost all districts except the Tashkent province, which shows better results. Moreover, Bekobod and Buka indicate the best results among all administrative districts.

Table 3 Results of Root Mean Square Error (RMSE) for all administrative districts in Tashkent province

MODIS (MOD13Q1) indicators

NDVI Name NDVI max R2 integrated Tashkent province 0.14 0.16 0.45 Bekobod 0.31 0.28 0.47 Buka 0.02 0.005 0.51 No Chinaz 0.08 0.12 correlation Kybray NO NO No trend No Kuyichirchik 0.59 0.61 correlation Oqqurgan 0.0078 0.0072 0.28 Pskent 0.054 0.12 0.28 Urtachirchik 0.007 0.002 0.25 Yangiyul NO NO No trend Yukorichirchik 0.07 0.11 0.37 Zangiota NO NO No trend

The Best

Table 4 Illustrates overall results for all districts and all indicators. It expresses the predicted yield in comparison with observed yield for 2013. The best results are shown for the NDVI indicators and as correlation between predicted yield and observed yield are Buka, Oqqurgan, Pskent, Urtachirchik and Yukorichirchik districts.

Table 4 Illustration of all indicators for all distrits in 2013 predicted yield.

3.5. Validation of forecasting model The validation at the province level and administrative district level was done for an individual year. Root mean square error for Tashkent province and district level was calculated for the calibration and validation with the results. The results indicate that predicted yield and observed yield are relatively efficient. The correlation between all of the regions is shown in Table 3 as R2. By the table and the results of RMSE done according to Eq.(4), it can be concluded that cotton prediction can be done by using and validating this model. The accuracy has been calculated as RMSE for the administrative districts as well.

4. Discussion A cotton plant is dependent on many external variables like other crops in the study area. In addition, cotton plant growth is very sensitive to various inputs and outputs. The results, which were obtained by models in previous sections, proves it. The personal communication helped to understand different aspects of this influence. There is a schematic plan, which is called “technological map of cotton growth” (Ibragimov et al., 2007). According to interviews (2014), cotton productivity depends on agricultural management. Different farmers try to keep and follow the rules of the technological map, which indicates proper and accurate cotton growth methods. In order to explore the influence of weather conditions on cotton yield and the best NDVI indicator available from MOD13Q1 (MODIS NDVI product) for cotton yield prediction in the study area, regression models and NDVI prediction models were applied. Due to different crops growing in Tashkent province, the research needs to delineate the cotton fields from other crops. Classification methods were applied and some obstacles during this step have been met. According to Platonov, for cotton classification some thresholds can be applied. The reason of choosing that methodology was that the research of Platonov was done in a neighboring province, but for the scale of a farm. The plot made by Platonov gave information about different crops in the region growing at the same time. Method of Platonov says that in order to classify cotton some decision rules (section 2.4.) can be applied. That method expresses that NDVI pixel value for DOY 113 should be <0.2, which can be considered as bare soil or water. Then, NDVI pixel value was limited to <0.2 in order to see rice spectral differences with cotton, as rice is growing at the same time and with almost the same spectral signatures by DOY. Differences between cotton and rice can be seen in section 2.4, where spectral signature of rice rises much higher during the period of the maximum reflectance value of cotton (DOY 225). Previously mentioned DOY 225 has been chosen in order to classify cotton from other crops as this day has to be the period when the reflected MODIS NDVI value reflects maximum value, as NDVI at DOY 225 is <0.3 and NDVI at DOY 225 is >0.5. After applying these thresholds and limits have been met, the question was whether MODIS NDVI product needs the same NDVI values for the classification and the same reflections for cotton as Landsat ETM+. Meanwhile, Zhang et al. (2011) stated that MODIS pixels need higher thresholds for decision rule classification, as NDVI(@DOY113)>0.2, 0.35>NDVI(@DOY161)>0.4 and the latest critical point was NDVI(@DOY225)<0.63 and NDVI(@DOY225)>0.73. Two methodologies were compared and the results in this research showed similarities with Zhang’s methodology (Figure 6), where the thresholds for DOY were taken from. The reason of deviating outcomes was that cotton at that time had weak biomass, which just had been grown up and not reflected a strong greenness value. In order to know it, the thresholds were lower than at the end of cotton calendar and higher than at the beginning of the growing calendar. So, results illustrated that due to different temporal and spatial resolutions of both satellites thresholds to distinguish cotton biomass from rice, wheat and maize, has to be different. MODIS NDVI values are higher than for the Landsat ETM+ satellite. In order to make an assumption the MODIS spatial resolution and temporal range of images against the Landsat temporal and spatial resolution were studied and taken into account. The delineation of the cotton biomass from the rest drove us to more difficulties, as wheat, maize, rice and cotton have the same growing calendar and the most similar spectral signature has been studied between rice and cotton. The differences between them were 28

discovered only in one critical point at the end of the growing season for cotton plant and harvesting time. This period is 225 DOY and acquisition date is equal to 13th of August. Rice reflects much higher greenness than the cotton because rice is growing on the water, and water increases the water content of the biomass. Meanwhile cotton is stopping to grow as farmers apply different agricultural management, such as herbicide inputs in order to increase dry matter content of the cotton (Anonymous, 2014). The reason of using the herbicides in the end of cotton is that cotton flowers do not become boxes and seeds are not appearing as well as cotton wool, when biomass is green and full of water content (Anonymous, 2014). The reason of choosing the 225th DOY as a critical point is, that farmers chase the cotton plants on July when 10-12th leaves have appeared in order to stop the growing strength and drive the plant to give power to the flowers and bulbs. Subsequently, farmers irrigate the fields in July as a last time (Ibragimov et al., 2007), in order to give water content and then start to dry it (Anonymous, 2014). After chasing and increasing the water content, herbicides have to be applied to increase dry matter content as was mentioned before. So, the period between irrigation was done and herbicides are applied is DOY 225. In case of chasing time was delayed the herbicides input will delay as well, which means that cotton will give good reflectance but productivity will decrease (linear trend line illustrated in Figure 16 and Figure 17). Chasing time and herbicides input time is dependent on money, labor forces, appropriate time management and time of the last freezing in the beginning of the cotton calendar (Anonymous, 2014). According to personal communication with the cotton classifier (2014), the results of NDVI indicators for Tashkent province and for administrative district level leads to make an assumption that current agricultural management is not leading to better outcomes or MODIS NDVI indicators are not the most suitable for these regions. The reason of MODIS’ unsuitableness for this area is that the spatial resolution (250m) of the MOD13Q1 NDVI product is too large to apply it on Tashkent province and district level. Because the fields in Tashkent province are less than 60 ha (Djanibekov et al., 2010). Different literature indicates that better classification can be made by using the cotton distribution map or ground truth data in order to know the province and district. For this research several points had been used in order to validate cotton fields and it illustrates (Figure 10) that mostly cotton pixels lay on cotton fields and include other fields as well because of the resolution of the satellite. In theory, the ideal approach for classification for the purpose of crop yield forecasting the cotton distribution map is needed. The optimal temperature for good cotton plant growth during the season is 25-30 degrees. Data used in order to find a correlation between weather conditions and cotton yield were monthly and yearly averages for 12 years. Using monthly average data is not efficient for such discovery. For finding better relationships, data has to be daily, because during the month, there are critical temperatures and there might be critical days with precipitations, which are not influence to an average number, and data represents small values. The weak negative relationship between cotton yield and weather conditions helps to find explanations about limitations and bottlenecks of cotton growth. Assumptions carry information not only in non- sufficient and no quality data, but in addition it leads to an understanding of the impact of weather conditions on the growth of cotton. In case the amount of precipitation increases the adequate and optimal limit of good cotton growth or precipitation decreases below limit, the cotton biomass stops growing and throw out the lower leaves, in order to give the strength to the

leaves on the top of the plant. It means the production will be low but plant will reflect well to the sensors. This research was done on a limited area, with restricted time range, with use of specific methodology and model. Thus, this research might not indicate precise information for the whole Republic.

5. Conclusions and Recommendations The prediction of the annual cotton yield can lead to knowledge of acreage under cotton and of gross yield, which gives the opportunity to use proper logistics such as time planning the amount of labor and others. Better schema of agricultural management will help to increase and improve the production of the cotton, since cotton is very essential for Uzbekistan and is playing one of the important roles of economy. The methods used in this research can help the government of The Republic of Uzbekistan to create their own new-modern forecasting methodology and improve time management on spending and planning the cotton harvest (picking). The information and methodology helps to obtain cognitive information, which subsequently might express the amount of labor forces needed in order to harvest and pick the cotton without using excessive time and labor. Moreover, the results of this study could be helpful in time management for use of another sensor in order to predict cotton yield by accurately delineation of the cotton on that area. Therefore, this research expresses the selection of other alternative indicators or satellites in order to forecast crop yield in Tashkent province. This forecasting used in this research for the cotton crop can effectively and accurately lead to information about the amount of the yield before the harvesting time and gives to farmers and others, the hints and the opportunity to make better, accurate and more efficient cotton management and market decision with good time planning, and govern the harvesting. The results show negative results for the prediction, which means that cotton product will decrease in 2013, which is confirmed by historical data. This research leads to the assumption that remote sensing prediction can transfer better and larger information than the ground truth prediction because remote sensing gives information not only about crop yield, but it provides more information such as vision of larger areas and subsequently helps to create the spatial maps of the area. However, the most crucial things for precise prediction is to select and use the good, suitable and sufficient indicators for the Tashkent region and moreover the better sensors with lower resolution. The best indicators for the province and its administrative districts are NDVI integrated and NDVI maximum, which had been identified during the research. Other indicators should be checked as different regions and different scales might require different variables. The districts, which did not show good results, have to be studied better and more precisely in order to find the most suitable sensor, model and indicator. So, methods used in this research can conclude that farmers do not have enough knowledge about production rather than to increase biomass, which in this case is cotton wools. In addition, for better cotton prediction, there is need for a cotton distribution map or ground truth data, which will help for more accurate cotton delineation from other crops. However, classification was studied and learnt by literature review, which recommends cotton 30

NDVI values for MODIS sensors to apply decision rules. Classification was applied successfully and it was concluded that other satellites have to be checked in that region as well because cotton is growing on small scale fields. Therefore for better production, the transfer of knowledge of the scientist has to be done and farmers have to study all schemes and agricultural management to improve production. Farmers have to solve all obstacles and problems to earn or take a loan from a bank to cover financial expenses such as for buying fertilizers and herbicides, fuels and other costs. However, cotton growing procedure has to be done according to state technological map (scheme for proper plant growth). This will lead to better agricultural management, time planning and logistics. More assumptions are that daily observations have to be done for temperature and precipitation against cotton growth. Secondly, biweekly cotton observation has to be done in order to see the influences of the daily changes of the temperature or precipitation instead of comparing monthly measurements of temperature and precipitations against cotton gross yield, which can show us the influence of weather conditions on daily growing and the time of harvesting. This research can help to understand cotton growth. Moreover, research can derive valuable information such as to know the amount of data and the valuable data for prediction in that area. Therefore, this study explains which indicators are the best ones for prediction in Tashkent province level and at administrative level. Before making any conclusions regarding cotton yield prediction in Tashkent province and its administrative districts more research should be done, like collection of ground truth data, time series analysis and cotton distribution mapping.

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