scholarly journals Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada

2019 ◽  
Vol 11 (20) ◽  
pp. 2419 ◽  
Author(s):  
Jiangui Liu ◽  
Jiali Shang ◽  
Budong Qian ◽  
Ted Huffman ◽  
Yinsuo Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Negative Correlation ◽  
Linear Correlation ◽  
Vegetation Index ◽  
Growth Stages ◽  
Yield Estimation ◽  
Grain Yields ◽  
Positive Linear Correlation ◽  
Reflectance Data

This study investigated the estimation of grain yields of three major annual crops in Ontario (corn, soybean, and winter wheat) using MODIS reflectance data extracted with a general cropland mask and crop-specific masks. Time-series two-band enhanced vegetation index (EVI2) was derived from the 8 day composite 250 m MODIS reflectance data from 2003 to 2016. Using a general cropland mask, the strongest positive linear correlation between crop yields and EVI2 was observed at the end of July to early August, whereas a negative correlation was observed in spring. Using crop-specific masks, the time of the strongest positive linear correlation for winter wheat was found between mid-May and early June, corresponding to peak growth stages of the crop. EVI2 derived at peak growth stages of a crop provided good predictive capability for grain yield estimation, with considerable inter-annual variation. A multiple linear regression model was established for county-level yield estimation using EVI2 at peak growth stages and the year as independent variables. The model accounted for the spatiotemporal variability of grain yields of about 30% and 47% for winter wheat, 63% and 65% for corn, and 59% and 64% for soybean using the general cropland mask and crop-specific masks, respectively. A negative correlation during the spring indicated that vegetation index extracted using a general cropland mask should be used with caution in regions with mixed crops, as factors other than the growth conditions of the targeted crops may also be captured by remote sensing data.

scholarly journals Sensor based calibration study for in-season nitrogen management of winter wheat in Turkey

2020 ◽  
Vol 6 (2) ◽  
pp. 204-211
Author(s):  
Erdinc Savasli ◽  
Oguz Onder ◽  
Cemal Cekic ◽  
Hasan Mufit Kalayci ◽  
Ramis Dayioglu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Agricultural Research ◽  
Nitrogen Nutrition ◽  
Stem Elongation ◽  
Block Design ◽  
Vegetation Indices ◽  
Nutrition Status ◽  
Growth Stages ◽  
Yield Estimation

The aims of this study were to compare the responses of four winter wheat cultivars to nitrogen fertilization with vegetation indices calculated using spectral reflection (GreenSeeker hand-held sensor) and to estimate in-season yield (INSEY) using the vegetation indices. The field experiment was conducted at Transitional Zone Agricultural Research Institute of Eskisehir province, Turkey in 2007-2008, 2008-2009 and 2009-2010 growing seasons. The experimental layout was a 2factor factorial in the randomized complete block design. Nitrogen rates were 0, 40, 80, 120, 160 and 200 kg N ha-1. Vegetation Index (NDVI) was obtained at growth stages of Zadoks 24 (tillering stage), Zadoks stage 30 (stem elongation), Zadoks stage 31 (the first node is visible) and Zadoks stage 32 (the second node is visible). The results revealed that Zadoks stage 30 was the most realistic reading time. NDVI had the advantage of providing information on biomass, in addition to nitrogen nutrition status of crops, enabling in-season yield estimation possible. Therefore, NDVI based calibration equations were preferred for testing in the fields of actual farmers for the last year of study. A comparison of the system with traditional farmer applications indicated that yield estimation obtained by the new system was quite similar yields with 13.2 kg ha-1 less N in the spring (ZD 3.0), showing its economically promising value. Asian J. Med. Biol. Res. June 2020, 6(2): 204-211

scholarly journals Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

2020 ◽  
Vol 12 (22) ◽  
pp. 3684
Author(s):  
Jie Jiang ◽  
Zeyu Zhang ◽  
Qiang Cao ◽  
Yan Liang ◽  
Brian Krienke ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Dry Matter ◽  
Vegetation Index ◽  
Nutrition Status ◽  
Growth Stages ◽  
Growth Indices ◽  
N Accumulation ◽  
N Nutrition ◽  
Red Edge ◽  
Aerial Vehicle

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops.

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals Physiological and developmental traits associated with the grain yield of winter wheat as affected by phosphorus fertilizer management

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xiu-Xiu Chen ◽  
Wei Zhang ◽  
Xiao-Yuan Liang ◽  
Yu-Min Liu ◽  
Shi-Jie Xu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Dry Matter ◽  
Photosynthetic Capacity ◽  
Growth Stages ◽  
Photosynthetic Parameters ◽  
Dry Matter Accumulation ◽  
Grain Yields ◽  
P Concentration ◽  
Spike Number

Abstract Although researchers have determined that attaining high grain yields of winter wheat depends on the spike number and the shoot biomass, a quantitative understanding of how phosphorus (P) nutrition affects spike formation, leaf expansion and photosynthesis is still lacking. A 3-year field experiment with wheat with six P application rates (0, 25, 50, 100, 200, and 400 kg P ha−1) was conducted to investigate this issue. Stem development and mortality, photosynthetic parameters, dry matter accumulation, and P concentration in whole shoots and in single tillers were studied at key growth stages for this purpose. The results indicated that spike number contributed the most to grain yield of all the yield components in a high-yielding (>8 t/ha) winter wheat system. The main stem (MS) contributed 79% to the spike number and tiller 1 (T1) contributed 21%. The 2.7 g kg−1 tiller P concentration associated with 15 mg kg−1 soil Olsen-P at anthesis stage led to the maximal rate of productive T1s (64%). The critical shoot P concentration that resulted in an adequate product of Pn and LAI was identified as 2.1 g kg−1. The thresholds of shoot P concentration that led to the maximum productive ability of T1 and optimal canopy photosynthetic capacity at anthesis were very similar. In conclusion, the thresholds of soil available P and shoot P concentration in whole plants and in single organs (individual tillers) were established for optimal spike formation, canopy photosynthetic capacity, and dry matter accumulation. These thresholds could be useful in achieving high grain yields while avoiding excessive P fertilization.

scholarly journals The Modification of Difference Vegetation Index (DVI) in middle and late growing period of winter wheat and its application in soil moisture inversion

2019 ◽  
Vol 131 ◽  
pp. 01098
Author(s):  
Zhang Hong-wei ◽  
Huai-liang Chen ◽  
Fei-na Zha
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Growth Stages ◽  
Wheat Growth ◽  
Inversion Result ◽  
Growing Period ◽  
The Difference ◽  
Different Growth Stages

In the middle and late growing period of winter wheat, soil moisture is easily affected by saturation when using MODIS data to retrieve soil moisture. In this paper, in order to reduce the effect of the saturation caused by increasing vegetation coverage in middle and late stage of winter wheat, the Difference Vegetation Index (DVI) model was modified with different coefficients in different growth stages of winter wheat based on MODIS spectral data and LAI characteristics of variation. LAI was divided into three stages, LAI ≤ 1 < LAI ≤, 3 < LAI, and the adjusting coefficient of α=1, α=3, α=5, were taken to modifying the Difference Vegetation Index(DVI). The results show that the Modified Difference Vegetation Index (MDVIα) can effectively reduce the interference of saturation, and the inversion result of soil moisture in the middle and late period of winter wheat growth is obviously superior to the uncorrected inversion model of DVI.

scholarly journals Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5431 ◽  
Author(s):  
Pengyu Hao ◽  
Huajun Tang ◽  
Zhongxin Chen ◽  
Zhengjia Liu
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Immune Network ◽  
Artificial Immune ◽  
Study Region ◽  
Early Season ◽  
Spring Maize ◽  
Artificial Immune Network ◽  
Sentinel 2

Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries–Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer’s accuracy (PA) and user’s accuracy (UA) values were higher than 95% when an April 1–May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1–August 15. As spring maize and cotton mature in late August and September–October, respectively, these two crops can be accurately mapped 4–6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management.

scholarly journals Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

2017 ◽  
Vol 38 (11) ◽  
pp. 3394-3414 ◽  
Author(s):  
Péter Bognár ◽  
Anikó Kern ◽  
Szilárd Pásztor ◽  
János Lichtenberger ◽  
Dávid Koronczay ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Yield Estimation ◽  
Modis Data

scholarly journals Comparison of Satellite and Ground-Based Phenology in China’s Temperate Monsoon Area

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Huanjiong Wang ◽  
Junhu Dai ◽  
Quansheng Ge
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Phenological Observation ◽  
Observation Network ◽  
Midpoint Method ◽  
Monsoon Area ◽  
Latitudinal Patterns ◽  
Very High ◽  
Reflectance Data

Continuous satellite datasets are widely used in tracking vegetation responses to climate variability. Start of season (SOS), for example, can be derived using a number of methods from the time series of satellite reflectance data; however, various methods often produce different SOS measures which limit the application of satellite data in phenological studies. Therefore, we employed five methods to estimate SOS from the Advanced Very High Resolution Radiometer (AVHRR)/normalized difference vegetation index (NDVI) dataset. Subsequently, we compared the SOS with the ground-based first leaf date (FLD) of 12 deciduous broadleaved plant species at 12 sites of the Chinese Phenological Observation Network (CPON). The results show that the latitudinal patterns of five satellite-derived SOS measures are similar to each other but different from the pattern of ground phenology. For individual methods, the variability of SOS time series is significantly different from ground phenology except for HANTS, Polyfit, and Midpoint methods. The SOS calculated using the Midpoint method showed significant correlations with ground phenophases most frequently (in 47.1% of cases). Using the SOS derived from the Midpoint method, significantly earlier trends in SOS were detected in 50.7% of the natural vegetation area from 1982 to 2006.

scholarly journals Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4660
Author(s):  
Fa Zhao ◽  
Guijun Yang ◽  
Hao Yang ◽  
Yaohui Zhu ◽  
Yang Meng ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Time Series Data ◽  
Short Term Memory ◽  
Combination Method ◽  
Series Data ◽  
Medium Term ◽  
Short Term ◽  
Deep Recurrent Neural Network

The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction.

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