scholarly journals National Crop Mapping Using Sentinel-1 Time Series: A Knowledge-Based Descriptive Algorithm

2021 ◽  
Vol 13 (5) ◽  
pp. 846
Author(s):  
Carole Planque ◽  
Richard Lucas ◽  
Suvarna Punalekar ◽  
Sebastien Chognard ◽  
Clive Hurford ◽  
...  
Keyword(s):  
Time Series ◽  
Crop Growth ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Growth Stages ◽  
Knowledge Based ◽  
Crop Type ◽  
Crop Mapping ◽  
Crop Growth Stages ◽  
Crop Types

National-level mapping of crop types is important to monitor food security, understand environmental conditions, inform optimal use of the landscape, and contribute to agricultural policy. Countries or economic regions currently and increasingly use satellite sensor data for classifying crops over large areas. However, most methods have been based on machine learning algorithms, with these often requiring large training datasets that are not always available and may be costly to produce or collect. Focusing on Wales (United Kingdom), the research demonstrates how the knowledge that the agricultural community has gathered together over past decades can be used to develop algorithms for mapping different crop types. Specifically, we aimed to develop an alternative method for consistent and accurate crop type mapping where cloud cover is quite persistent and without the need for extensive in situ/ground datasets. The classification approach is parcel-based and informed by concomitant analysis of knowledge-based crop growth stages and Sentinel-1 C-band SAR time series. For 2018, crop type classifications were generated nationally for Wales, with regional overall accuracies ranging between 85.8% and 90.6%. The method was particularly successful in distinguishing barley from wheat, which is a major source of error in other crop products available for Wales. This study demonstrates that crops can be accurately identified and mapped across a large area (i.e., Wales) using Sentinel-1 C-band data and by capitalizing on knowledge of crop growth stages. The developed algorithm is flexible and, compared to the other methods that allow crop mapping in Wales, the approach provided more consistent discrimination and lower variability in accuracies between classes and regions.

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

2016 ◽  
Vol XLI-B8 ◽  
pp. 1037-1042 ◽  
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.

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

2016 ◽  
Vol XLI-B8 ◽  
pp. 1037-1042 ◽  
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.

scholarly journals ADVANCEMENTS IN ESTIMATING CROP GROWTH STAGES USING RADARSAT-2 AND TERRASAR-X POLARIMETRIC DATA

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1227-1232 ◽  
Author(s):  
G. Lampropoulos ◽  
Y. Li ◽  
T. Liu
Keyword(s):  
Alpha Angle ◽  
Ground Truth ◽  
Crop Growth ◽  
Synthetic Aperture ◽  
Growth Stages ◽  
Polarimetric Synthetic Aperture Radar ◽  
Crop Growth Stages ◽  
Crop Types ◽  
Differential Reflectivity ◽  
Ground Truth Information

This paper uses RADARSAT-2 quad Polarimetric Synthetic Aperture Radar (PolSAR) and TerraSAR-X dual polarimetric SAR data to monitor agriculture crop growth stages. Two RADARSAT-2 Fine Quad Wide (FQW) beam modes FQ2W and FQ10W, each with 5 sets of data and 13 sets of Stripmap TerraSAR-X data were used in the study. Both RADARSAT-2 POLSAR data and TerraSARX data were acquired in summer 2012 outside Winnipeg, Manitoba, Canada. The study was carried out to two crop types: canola and wheat, each contains 5 regions of interest from ground truth crop classification map in the image scene. Polarimetric features such as differential reflectivity bands ratio, entropy, anisotropy, alpha angle, lambda, scattering diversity and polarization index were evaluated for two crop types. The results from both RADARSAT-2 and TerraSAR-X data were compared and they demonstrated clear relationships between crop growth stages and polarimetric parameters. It is observed that entropy, lambda and differential reflectivity from both data have similar responses to crop growth stages in their common coverage period. The results were also validated using ground truth information.

scholarly journals Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Feng Gao ◽  
Xiaoyang Zhang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Real Time ◽  
Land Surface ◽  
Vegetation Index ◽  
Growing Season ◽  
Crop Growth ◽  
Growth Stages ◽  
Crop Phenology ◽  
Crop Growth Stages

Crop phenology is critical for agricultural management, crop yield estimation, and agroecosystem assessment. Traditionally, crop growth stages are observed from the ground, which is time-consuming and lacks spatial variability. Remote sensing Vegetation Index (VI) time series has been used to map land surface phenology (LSP) and relate to crop growth stages mostly after the growing season. In recent years, high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season. This paper summarizes two classes of near-real-time mapping methods, i.e., curve-based and trend-based approaches. The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages. The curve-based approaches are capable of a short-term prediction. The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds. The trend-based approaches only use current observations. Both curve-based and trend-based approaches are promising in mapping crop growth stages timely. Nevertheless, mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages. The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season. Recent satellite datasets such as the harmonized Landsat and Sentinel-2 (HLS) are promising for mapping crop phenology within the season over large areas. Operational applications in the near future are feasible.

scholarly journals Integrating Economics in the Critical Period for Weed Control Concept in Corn

Weed Science ◽  
2014 ◽  
Vol 62 (4) ◽  
pp. 608-618 ◽  
Author(s):  
Martina Keller ◽  
Geoffroy Gantoli ◽  
Jens Möhring ◽  
Christoph Gutjahr ◽  
Roland Gerhards ◽  
...  
Keyword(s):  
Weed Control ◽  
Critical Period ◽  
Logistic Models ◽  
Crop Growth ◽  
Growth Stages ◽  
Corn Yield ◽  
Leaf Stage ◽  
Weed Interference ◽  
Free Interval ◽  
Crop Growth Stages

The effect of weed interference on corn yield and the critical period for weed control (CPWC) were determined in Germany and Benin. Treatments with weed control starting at different crop growth stages and continuously kept weed-free until harvest represented the “weed-infested interval.” Treatments that were kept weed-free from sowing until different crop growth stages represented the “weed-free interval.” Michaelis–Menten, Gompertz, logistic and log–logistic models were employed to model the weed interference on yield. Cross-validation revealed that the log–logistic model fitted the weed-infested interval data equally well as the logistic and slightly better than the Gompertz model fitted the weed-free interval. For Benin, economic calculations considered yield revenue and cost increase due to mechanical weeding operations. Weeding once at the ten-leaf stage of corn resulted already profitable in three out of four cases. One additional weeding operation may optimize and assure profit. Economic calculations for Germany determined a CPWC starting earlier than the four-leaf stage, challenging the decade-long propagated CPWC for corn. Differences between Germany and Benin are probably due to the higher yields and high costs in Germany. This study provides a straightforward method to implement economic data in the determination of the CPWC for chemical and nonchemical weed control strategies.

scholarly journals Improved Crop Classification with Rotation Knowledge using Sentinel-1 and -2 Time Series

2020 ◽  
Vol 86 (7) ◽  
pp. 431-441 ◽  
Author(s):  
Sébastien Giordano ◽  
Simon Bailly ◽  
Loic Landrieu ◽  
Nesrine Chehata
Keyword(s):  
Time Series ◽  
Conditional Random Field ◽  
Temporal Structure ◽  
Linear Chain ◽  
Agricultural Practices ◽  
Annual Crops ◽  
Automatic Integration ◽  
Crop Type ◽  
Crop Types ◽  
Type Classification

Leveraging the recent availability of accurate, frequent, and multimodal (radar and optical) Sentinel-1 and -2 acquisitions, this paper investigates the automation of land parcel identi- fication system (LPIS ) crop type classification. Our approach allows for the automatic integration of temporal knowledge, i.e., crop rotations using existing parcel-based land cover databases and multi-modal Sentinel-1 and -2 time series. The temporal evolution of crop types was modeled with a linear- chain conditional random field, trained with time series of multi-modal (radar and optical) satellite acquisitions and associated LPIS. Our model was tested on two study areas in France (≥ 1250 km2) which show different crop types, various parcel sizes, and agricultural practices: . the Seine et Marne and the Alpes de Haute-Provence classified accordingly to a fine national 25-class nomenclature. We first trained a Random Forest classifier without temporal structure to achieve 89.0% overall accuracy in Seine et Marne (10 classes) and 73% in Alpes de Haute-Provence (14 classes). We then demonstrated experimentally that taking into account the temporal structure of crop rotation with our model resulted in an increase of 3% to +5% in accuracy. This increase was especially important (+12%) for classes which were poorly classified without using the temporal structure. A stark posi- tive impact was also demonstrated on permanent crops, while it was fairly limited or even detrimental for annual 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.

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