Rice Growth Stages Mapping with Normalized Difference Vegetation Index (NDVI) Algorithm Using Sentinel-2 Time Series Satellite Imagery

Bangun Muljo Sukojo; Rizki Hari Kurniawan

doi:10.18517/ijaseit.11.4.12335

Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7100405 ◽

2018 ◽

Vol 7 (10) ◽

pp. 405 ◽

Cited By ~ 12

Author(s):

Urška Kanjir ◽

Nataša Đurić ◽

Tatjana Veljanovski

Keyword(s):

Land Use ◽

Time Series ◽

Agricultural Policy ◽

Common Agricultural Policy ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Land ◽

Agricultural Land Use ◽

Land Use Control ◽

Sentinel 2

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.

Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring

Remote Sensing ◽

10.3390/rs12142195 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2195 ◽

Cited By ~ 4

Author(s):

Blanka Vajsová ◽

Dominique Fasbender ◽

Csaba Wirnhardt ◽

Slavko Lemajic ◽

Wim Devos

Keyword(s):

Time Series ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Policy Framework ◽

Pixel Size ◽

Control Approach ◽

Continuous State ◽

Ndvi Time Series ◽

High Level ◽

Sentinel 2

The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a new method to quantify and specify restrictions of the Sentinel-2 imagery in the context of checks by monitoring, a newly introduced control approach within the European Common Agriculture Policy framework. The method consists of a comparison of normalized difference vegetation index (NDVI) time series constructed from data of different spatial resolution to estimate the performance and limits of the coarser one. Using similarity assessment of Sentinel-2 (10 m pixel size) and PlanetScope (3 m pixel size) NDVI time series, it was estimated that for 10% out of 867 fields less than 0.5 ha in size, Sentinel-2 data did not provide reliable evidence of the activity or state of the agriculture field over a given timeframe. Statistical analysis revealed that the number of clean or full pixels and the proportion of pixels lost after an application of a 5-m (1/2 pixel) negative buffer are the geospatial parameters of the field that have the highest influence on the ability of the Sentinel-2 data to qualify the field’s state in time. We specified the following limiting criteria: at least 8 full pixels inside a border and less than 60% of pixels lost. It was concluded that compliance with the criteria still assures a high level of extracted information reliability. Our research proved the promising potential, which was higher than anticipated, of Sentinel-2 data for the continuous state assessment of small fields. The method could be applied to other sensors and indicators.

Detecting Cover Crop End-Of-Season Using VENµS and Sentinel-2 Satellite Imagery

Remote Sensing ◽

10.3390/rs12213524 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3524

Author(s):

Feng Gao ◽

Martha C. Anderson ◽

W. Dean Hively

Keyword(s):

Remote Sensing ◽

Time Series ◽

Cover Crops ◽

Cover Crop ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Research ◽

Agricultural Landscapes ◽

Absolute Difference ◽

Sentinel 2

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

Sentinel-2 Satellite Imagery for Agronomic and Quality Variability Assessment of Pistachio (Pistacia vera L.)

Sustainability ◽

10.3390/su12208437 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8437

Author(s):

Enrique Barajas ◽

Sara Álvarez ◽

Elena Fernández ◽

Sergio Vélez ◽

José Antonio Rubio ◽

...

Keyword(s):

Satellite Imagery ◽

Nutritional Quality ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Quality Parameters ◽

Productive Capacity ◽

Pistacia Vera ◽

Phenological Stages ◽

Organoleptic Characteristics ◽

Sentinel 2

The objective of this work is to evaluate the agronomic, phenological, nutritional quality and organoleptic characteristics of pistachios (Pistacia vera L.) based on the NDVI (Normalized Difference Vegetation Index) calculated in the phenological stage of nut filling from Sentinel satellite imagery. Based on this index, three pistachio tree orchards were studied and classified into two levels of vigour: high and low. The results obtained have discriminated the production per tree, which is strongly related to yield. Regarding the nutritional quality parameters, significant differences were not observed between vigour levels, although the most vigorous trees have shown nuts with a higher percentage of fibre and protein. In terms of phenology, there have not been differences between trees of different vigour, only a slight advance of some phenological stages has been observed in several high-vigour trees. Triangular tests have been made successfully to discriminate the origin of the dry nut and the vigour of the trees. In conclusion, for a given nut quality within a given orchard, the NDVI is a good index to classify different areas according to productive capacity and can be useful to apply variable management, irrigation and fertilization according to vigour.

Assessing the Use of Sentinel-2 Time Series Data for Monitoring Cork Oak Decline in Portugal

Remote Sensing ◽

10.3390/rs11212515 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2515 ◽

Cited By ~ 4

Author(s):

Ana Navarro ◽

Joao Catalao ◽

Joao Calvao

Keyword(s):

Time Series ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Series Data ◽

Temporal Dynamics ◽

Cork Oak ◽

Series Data ◽

Tree Level ◽

Oak Decline ◽

Sentinel 2

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing to the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsened. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables monitoring of gradual processes. These processes can be monitored using spectral vegetation indices (VI) as their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

MAPPING OF <i>SARGASSUM</i> DISTRIBUTION IN THE EASTERN COAST OF SOUTHERN LEYTE USING SENTINEL 2 SATELLITE IMAGERY

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

10.5194/isprs-archives-xlii-4-w19-289-2019 ◽

2019 ◽

Vol XLII-4/W19 ◽

pp. 289-295

Author(s):

J. J. Lasquites ◽

A. C. Blanco ◽

A. Tamondong

Keyword(s):

Satellite Imagery ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Accuracy Assessment ◽

Marine Ecosystem ◽

Brown Seaweed ◽

The Philippines ◽

Eastern Coast ◽

Department Of Agriculture ◽

Sentinel 2

Abstract. Sargassum is a brown seaweed distributed in the Philippines and recognized as an additional source of income for fishing communities. Due to uncontrolled harvesting of the seaweed, the Department of Agriculture regulated its collection and harvesting by imposing seasonal restrictions. Hence, the need to identify the locations and cover of healthy Sargassum is vital to address the demand in the market while maintaining ecological balance in the marine ecosystem. Two Sentinel-2 satellite imagery (10 m resolution) acquired on December 08, 2017 (peak growth) and May 27, 2018 (senescence stage) were used to map the presence of Sargassum in the eastern coast of Southern Leyte. Supervised classification using maximum likelihood algorithm and accuracy assessment were conducted before generating the map. Three classes were considered namely Sargassum, clouds and land. Furthermore, Anselin Local Moran’s I (cluster and outlier analysis) was conducted to determine which areas have significant clustering of “healthy” Sargassum using the normalized difference vegetation index (NDVI). For both image dates, high classification accuracies of Sargassum were obtained in the islands. However, there are misclassifications of Sargassum in Silago (UA = 78.72%) and Hinunangan (PA = 82.35%) using the May image. Furthermore, misclassification of Sargassum were obtained in Silago (PA = 93.6%) and Hinundayan (PA = 96.23%) using the December image. Clusters of high NDVI values are more evident in December. Healthy Sargassum are apparent in the coast of Silago and mostly found near shore and in rocky substrates.

Evaluation of Vegetation Index-Based Curve Fitting Models for Accurate Classification of Salt Marsh Vegetation Using Sentinel-2 Time-Series

Sensors ◽

10.3390/s20195551 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5551

Author(s):

Chao Sun ◽

Jialin Li ◽

Luodan Cao ◽

Yongchao Liu ◽

Song Jin ◽

...

Keyword(s):

Time Series ◽

Salt Marsh ◽

Curve Fitting ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Coefficient Of Determination ◽

Salt Marsh Vegetation ◽

Marsh Vegetation ◽

Sentinel 2

The successful launch of the Sentinel-2 constellation satellite, along with advanced cloud detection algorithms, has enabled the generation of continuous time series at high spatial and temporal resolutions, which is in turn expected to enable the classification of salt marsh vegetation over larger spatiotemporal scales. This study presents a critical comparison of vegetation index (VI) and curve fitting methods—two key factors for time series construction that potentially influence vegetation classification performance. To accomplish this objective, the stability of five different VI time series, namely Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), and Water-Adjusted Vegetation Index (WAVI), was compared empirically; the suitability between three curve fitting methods, namely Asymmetric Gaussian (AG), Double Logistic (DL), and Two-term Fourier (TF), and VI time series was measured using the coefficient of determination, and the salt marsh vegetation separability among different combinations of VI time series and curve fitting methods (i.e., VI time series-based curve fitting model) was quantified using overall the Jeffries–Matusita distance. Six common types of salt marsh vegetation from three typical coastal sites in China were used to validate these findings, which demonstrate: (1) the SAVI performed best in terms of time series stability, while the EVI exhibited relatively poor time series stability with conspicuous outliers induced by the sensitivity to omitted clouds and shadows; (2) the DL method commonly resulted in the most accurate classification of different salt marsh vegetation types, especially when combined with the EVI time series, followed by the TF method; and (3) the SAVI/NDVI-based DL/TF model demonstrated comparable efficiency for classifying salt marsh vegetation. Notably, the SAVI/NDVI-based DL model performed most strongly for high latitude regions with a continental climate, whilst the SAVI/NDVI-based TF model appears to be better suited to mid- to low latitude regions dominated by a monsoonal climate.

Assessing the Use of Sentinel-2 Time Series for Monitoring Cork Oak Decline in Portugal

10.20944/preprints201909.0316.v1 ◽

2019 ◽

Author(s):

Ana Navarro ◽

João Catalão ◽

João Calvão

Keyword(s):

Time Series ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Vegetation Indices ◽

Quercus Suber ◽

Cork Oak ◽

Tree Level ◽

Oak Decline ◽

Sentinel 2

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing for the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsen. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables gradual processes monitoring. These processes can be monitored using spectral vegetation indices (VI) once their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in-situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions

Remote Sensing ◽

10.3390/rs11050542 ◽

2019 ◽

Vol 11 (5) ◽

pp. 542 ◽

Cited By ~ 22

Author(s):

Laura Stendardi ◽

Stein Karlsen ◽

Georg Niedrist ◽

Renato Gerdol ◽

Marc Zebisch ◽

...

Keyword(s):

Time Series ◽

Optical Sensors ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Optical Data ◽

Radar Signal ◽

Acquisition Time ◽

Soil Conditions ◽

Mountain Regions ◽

Sentinel 2

A synergic integration of Synthetic Aperture Radar (SAR) and optical time series offers an unprecedented opportunity in vegetation phenology monitoring for mountain agriculture management. In this paper, we performed a correlation analysis of radar signal to vegetation and soil conditions by using a time series of Sentinel-1 C-band dual-polarized (VV and VH) SAR images acquired in the South Tyrol region (Italy) from October 2014 to September 2016. Together with Sentinel-1 images, we exploited corresponding Sentinel-2 images and ground measurements. Results show that Sentinel-1 cross-polarized VH backscattering coefficients have a strong vegetation contribution and are well correlated with the Normalized Difference Vegetation Index (NDVI) values retrieved from optical sensors, thus allowing the extraction of meadow phenological phases. Particularly for the Start Of Season (SOS) at low altitudes, the mean difference in days between Sentinel-1 and ground sensors is compatible with the acquisition time of the SAR sensor. However, the results show a decrease in accuracy with increasing altitude. The same trend is observed for senescence. The main outcomes of our investigations in terms of inter-satellite comparison show that Sentinel-1 is less effective than Sentinel-2 in detecting the SOS. At the same time, Sentinel-1 is as robust as Sentinel-2 in defining mowing events. Our study shows that SAR-Optical data integration is a promising approach for phenology detection in mountain regions.

Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

Remote Sensing ◽

10.3390/rs13040579 ◽

2021 ◽

Vol 13 (4) ◽

pp. 579

Author(s):

Xueqin Jiang ◽

Shenghui Fang ◽

Xia Huang ◽

Yanghua Liu ◽

Linlin Guo

Keyword(s):

Time Series ◽

Precision Agriculture ◽

Large Scale ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Southern China ◽

Growth Period ◽

Growth Monitoring ◽

Rice Growth ◽

Red Edge

Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture.