Summer Maize Mapping by Compositing Time Series Sentinel-1A Imagery Based on Crop Growth Cycles

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (<a href="http://earthexplorer.usgs.gov/"target="_blank">http://earthexplorer.usgs.gov/</a>). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

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Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

Geocarto International ◽

10.1080/10106049.2017.1316781 ◽

2017 ◽

Vol 33 (9) ◽

pp. 942-956 ◽

Cited By ~ 16

Author(s):

P. Kumar ◽

R. Prasad ◽

D. K. Gupta ◽

V. N. Mishra ◽

A. K. Vishwakarma ◽

...

Keyword(s):

Time Series ◽

Winter Wheat ◽

Growth Parameters ◽

Crop Growth ◽

Wheat Crop ◽

Winter Wheat Crop ◽

Sar Data

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Bayesian Sigmoid-Type Time Series Forecasting with Missing Data for Greenhouse Crops

Sensors ◽

10.3390/s20113246 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3246 ◽

Cited By ~ 1

Author(s):

Alexander Kocian ◽

Giulia Carmassi ◽

Fatjon Cela ◽

Luca Incrocci ◽

Paolo Milazzo ◽

...

Keyword(s):

Time Series ◽

Environmental Control ◽

Measurement Data ◽

Environmental Parameters ◽

Integrated Approach ◽

Crop Growth ◽

Parameter Estimates ◽

Sigmoid Function ◽

Area Index ◽

Hidden States

This paper follows an integrated approach of Internet of Things based sensing and machine learning for crop growth prediction in agriculture. A Dynamic Bayesian Network (DBN) relates crop growth associated measurement data to environmental control data via hidden states. The measurement data, having (non-linear) sigmoid-type dynamics, are instances of the two classes observed and missing, respectively. Considering that the time series of the logistic sigmoid function is the solution to a reciprocal linear dynamic model, the exact expectation-maximization algorithm can be applied to infer the hidden states and to learn the parameters of the model. At iterative convergence, the parameter estimates are then used to derive a predictor of the measurement data several days ahead. To evaluate the performance of the proposed DBN, we followed three cultivation cycles of micro-tomatoes (MicroTom) in a mini-greenhouse. The environmental parameters were temperature, converted into Growing Degree Days (GDD), and the solar irradiance, both at a daily granularity. The measurement data were Leaf Area Index (LAI) and Evapotranspiration (ET). Although measurement data were only available scarcely, it turned out that high quality measurement data predictions were possible up to three weeks ahead.

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Growth, cycles and convergence in US regional time series

International Journal of Forecasting ◽

10.1016/j.ijforecast.2005.04.017 ◽

2005 ◽

Vol 21 (4) ◽

pp. 667-686 ◽

Cited By ~ 27

Author(s):

Vasco M. Carvalho ◽

Andrew C. Harvey

Keyword(s):

Time Series ◽

Growth Cycles

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Crop Yield Estimation Using Multi-source Satellite Image Series and Deep Learning

10.5194/egusphere-egu2020-13957 ◽

2020 ◽

Author(s):

Gohar Ghazaryan ◽

Sergii Skakun ◽

Simon König ◽

Ehsan Eyshi Rezaei ◽

Stefan Siebert ◽

...

Keyword(s):

Time Series ◽

Agricultural Production ◽

Remotely Sensed ◽

Growth Conditions ◽

Crop Growth ◽

County Level ◽

Yield Estimation ◽

Field Level ◽

The Impact ◽

Level Analysis

Timely monitoring of agricultural production and early yield predictions are essential for food security. Crop growth conditions and yield are related to climate variability and extreme events. Remotely sensed time-series can be used to study the variability in crop growth and agricultural production. However, the choice of remotely sensed data and methods is still an issue, as different datasets have different spatiotemporal characteristics. Thus, our primary goal was to study the impact of applying different remotely sensed time series on yield estimation in U.S. at the county and field scale. Furthermore, the impact of crop growth conditions on yield variability was assessed. For county-level analysis, MODIS-based surface reflectance, Land Surface Temperature, and Evapotranspiration time series were used as input datasets. Whereas field-level analysis was carried out using NASA&#8217;s Harmonized Landsat Sentinel-2 (HLS) product. 3D convolutional neural network (CNN) and CNN followed by long-short term memory (LSTM) were used. For county-level analysis, the CNN-LSTM model had the highest accuracy, with a mean percentage error of 10.3% for maize and 9.6% for soybean. This model presented robust results for the year 2012, which is considered a drought year. In the case of field-level analysis, all models achieved accurate results with R2 exceeding 0.8 when data from mid growing season were used. The results highlight the potential of yield estimation at different management scales.

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Crop Yield and Water Productivity Responses in Management Zones for Variable-Rate Irrigation Based on Available Soil Water Holding Capacity

Transactions of the ASABE ◽

10.13031/trans.12340 ◽

2017 ◽

Vol 60 (5) ◽

pp. 1659-1667 ◽

Cited By ~ 3

Author(s):

Weixia Zhao ◽

Jiusheng Li ◽

Rumiao Yang ◽

Yanfeng Li

Keyword(s):

Winter Wheat ◽

Growth Parameters ◽

Water Productivity ◽

Crop Growth ◽

Variable Rate ◽

Summer Maize ◽

Area Index ◽

Management Zones ◽

Water Holding ◽

Variable Rate Irrigation

Abstract. Effective management of variable-rate irrigation (VRI) is a critical factor for maximizing the benefit of a VRI system. In this study, the influences of soil properties on winter wheat and summer maize were studied to verify whether differences in soil available water holding capacity (AWC) had an influence on crop growth parameters, yield, and water productivity (WP). A center-pivot VRI system was employed to deliver irrigation water across the field in an alluvial flood plain in China, and AWC was used to delineate VRI management zones. Three management zones with substantial differences in AWC were created, with AWC varying from 152 to 161 mm, from 161 to 171 mm, and from 171 to 185 mm for zones 1, 2, and 3, respectively. All zones were managed using the same allowed depletion. In the two-year study, the seasonal irrigation amount was basically equivalent among management zones for both winter wheat and summer maize. Differences in crop growth parameters were detected in plant height and leaf area index for winter wheat. The maximum plant height and leaf area index observed in zone 2 were 5 cm and 2.1 greater, respectively, than in the other zones. For both winter wheat and summer maize, the highest yield and WP were observed in zone 2, except for summer maize WP in the 2014 season. Compared with the average value for this field, the yields in zone 2 were 27% and 23% greater for winter wheat and 4% and 11% greater for summer maize in the 2014 and 2015 seasons, respectively. We demonstrate that AWC is an effective parameter for zone identification in VRI management, and differences in AWC and the layered-textural soils in a field may influence the crop growth parameters, yield, and WP of winter wheat and summer maize. Keywords: Center-pivot irrigation, Critical soil moisture deficit, Management zone, Summer maize, Variable-rate irrigation, Winter wheat.

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TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-1037-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1037-1042 ◽

Cited By ~ 1

Author(s):

S. A. Sawant ◽

M. Chakraborty ◽

S. Suradhaniwar ◽

J. Adinarayana ◽

S. S. Durbha

Keyword(s):

Remote Sensing ◽

Time Series ◽

Growth Stage ◽

Crop Growth ◽

Water Requirement ◽

Growth Stages ◽

Crop Water ◽

Horticultural Crops ◽

Crop Growth Stages ◽

Crop Growth Stage

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (<a href="http://earthexplorer.usgs.gov/"target="_blank">http://earthexplorer.usgs.gov/</a>). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

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Assessing Landsat Fractional Ground-Cover Time Series across Australia's Arid Rangelands: Separating Grazing Impacts from Climate Variability

10.20944/preprints201703.0236.v1 ◽

2017 ◽

Author(s):

Jason Barnetson ◽

Stuart Phinn ◽

Peter Scarth ◽

Robert Denham

Keyword(s):

Time Series ◽

Climate Variability ◽

Ground Cover ◽

Field Measurements ◽

Change Point Detection ◽

Grazing Impact ◽

Growth Cycles ◽

Grazing Impacts ◽

Arid Rangelands

Suitable measures of grazing impacts on ground cover, that enable separation of the effects of climatic variations, are needed to inform land managers and policy makers across the arid rangelands of the Northern Territory of Australia. This work developed and tested a time-series, change-point detection method for application to time series of vegetation fractional cover derived from Landsat data to identify irregular and episodic ground-cover growth cycles. These cycles were classified to distinguish grazing impacts from that of climate variability. A measure of grazing impact was developed using a multivariate technique to quantify the rate and degree of ground cover change. The method was successful in detecting both long term (> 3 years) and short term (< 3 years) growth cycles. Growth cycle detection was assessed against rainfall surplus measures indicating a relationship with high rainfall periods. Ground cover change associated with grazing impacts was also assessed against field measurements of ground cover indicating a relationship between both field and remotely sensed ground cover. Cause and effects between grazing practices and ground cover resilience can now be explored in isolation to climatic drivers. This is important to the long term balance between ground cover utilisation and overall landscape function and resilience.

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INTEGRATION OF HUMAN PARTICIPATORY SENSING AND ARCHIVES OF REMOTE SENSING OBSERVATIONS FOR FIELD LEVEL CROP PHENOLOGY ESTIMATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-547-2018 ◽

2018 ◽

Vol XLII-4 ◽

pp. 547-550

Author(s):

S. A. Sawant ◽

J. D. Mohite ◽

S. Pappula

Keyword(s):

Remote Sensing ◽

Time Series ◽

Vegetation Indices ◽

Crop Growth ◽

Growth Stages ◽

Growth Monitoring ◽

Specific Information ◽

Crop Phenology ◽

Field Level ◽

Sentinel 2

Abstract. The rise in global population has increased food and water demand thereby causing excessive pressure on existing resources. In developing countries with fragmented land holdings there exists constant pressure on available water and land resources. Obtaining field scale crop specific information is challenging task. Advent of open freely available multi-temporal remote sensing observations with improved radiometric resolution the possibilities for near real / real time applications has increased. In this study and an attempt has been made to establish operational model for field level crop growth monitoring using integrated approach of crowd sourcing and time series of remote sensing observations. The time series of Sentinel 2 (A and B) satellite has been used to estimate crop growth related components such as vegetation indices and crop growth stage and crop phenology. In initial stage high valued cereal crop Wheat has been selected. The field level information (i.e. 108 Wheat fields) collected using mobile based agro-advisory platform mKRISHI&reg; has been used to extract time series of Sentinel 2 observations (44 scenes for year 2016 and 2018). The moving average has been used for filling gaps in the time series of vegetation indices. The BFAST and GreenBrown package in R were used for detecting breaks in vegetation index time series and estimating crop growth stages. Analysis shows that the estimated crop phenology parameters were in better agreement with the field observations. In future more crops from different agro-climatic conditions will be considered for providing field level crop management advisory.

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Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain

Applied Sciences ◽

10.3390/app10082667 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2667 ◽

Cited By ~ 1

Author(s):

Xueting Wang ◽

Sha Zhang ◽

Lili Feng ◽

Jiahua Zhang ◽

Fan Deng

Keyword(s):

Time Series ◽

Large Scale ◽

Vegetation Index ◽

Vegetation Indices ◽

Regression Equations ◽

Summer Maize ◽

Temporal Shift ◽

Modis Evi ◽

Multi Temporal ◽

Moderate Resolution Imaging Spectroradiometer

Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R2 reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops.

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