Detection of Crop Seeding and Harvest through Analysis of Time-Series Sentinel-1 Interferometric SAR Data

Shang, Jiali; Liu, Jiangui; Poncos, Valentin; Geng, Xiaoyuan; Qian, Budong; Chen, Qihao; Dong, Ting; MacDonald, Dan; Martin, Timothy A.; Kovacs, John M.; Walters, Daniel F.

doi:10.3390/rs12101551

Cited by 50 publications

(30 citation statements)

References 49 publications

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“…In combination, grass fodder species used for livestock grazing, e.g., Alysicarpus ovalifolius, Pennisetum pedicellatum, Senna tora and Zornia glochidiata, were also sown, which was established quickly in the plots within the 1st year of planting. The economic benefits and the incentive for maintenance of plots and plants include for example, revenues generation from fodder species during the first years of planting by communities [20,21].…”

Section: Discussionmentioning

confidence: 99%

Monitoring Large-Scale Restoration Interventions from Land Preparation to Biomass Growth in the Sahel

2021

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In this work we demonstrate that restoration interventions in arid to semi-arid landscapes can be independently assessed by remote sensing methods throughout all phases. For early verification, we use Sentinel-1 radar imagery that is sensitive to changes in soil roughness and thus able to rapidly detect disturbances due to mechanised ploughing, including identification of the time of occurrence and the surface area prepared for planting. Subsequently, time series of the normalized difference vegetation index (NDVI) derived from high-resolution imagery enabled tracking and verifying of the increase in biomass and the long-term impact of restoration interventions. We assessed 111 plots within the Great Green Wall area in Burkina Faso, Niger, Nigeria and Senegal. For 58 plots, the interventions were successfully verified, corresponding to an area of more than 7000 ha of degraded land. Comparatively, these computerised data were matched with field data and high-resolution imagery, for which the NDVI was used as an indicator of subsequent biomass growth in the plots. The trends were polynomial and presented clear vegetation gains for the monthly aggregates over the last 2 years (2018–2020). The qualitative data on planted species also showed an increase in biodiversity as direct sown seeds of a minimum of 10 native Sahel species (six woody mixed with four fodder herbaceous species) were planted per hectare. This innovative and standardised monitoring method provides an objective and timely assessment of restoration interventions and will likely appeal more actors to confidently invest in restoration as a part of zero-net climate mitigation.

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Section: Discussionmentioning

confidence: 99%

Monitoring Large-Scale Restoration Interventions from Land Preparation to Biomass Growth in the Sahel

2021

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“…On the other side, SAR backscatter coefficients time series [28] and interferometric coherence time series [49] can be used to retrieve rice phenology, but there are some caveats: SAR data are sensitive to soil moisture (dielectric constant), surface roughness as well as terrain, so non-vegetation factors (e.g., tillage, precipitation) can introduce substantial uncertainty in the time series. Moreover, imaging defects in mountainous regions, such as fore-shortening, shadowing, and overlaying phenomenon, limit its use.…”

Section: Transplanting Phase Determinationmentioning

confidence: 99%

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

Xiao

2021

Remote Sensing

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In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from Savitzky–Golay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH) plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions.

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“…For example, coherence products (e.g., mean and standard deviation values) can be used along with the amplitude data to produce different false color composite products, and spatially investigate their applications for wetland classification. Various maps can also be produced based on the multi-temporal coherence products to investigate the effectiveness of these time-series products for wetland change analysis [15,17,54].…”

Section: Future Workmentioning

confidence: 99%

InSAR Coherence Analysis for Wetlands in Alberta, Canada Using Time-Series Sentinel-1 Data

Amani¹,

Poncos²,

Brisco³

et al. 2021

Remote Sensing

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Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that have investigated the application of the Interferometric Synthetic Aperture Radar (InSAR) coherence products for wetland studies, especially over large areas. Therefore, in this study, coherence products over the entire province of Alberta, Canada (~661,000 km2) were generated using the Sentinel-1 data acquired from 2017 to 2020. Then, these products along with large amount of wetland reference samples were employed to assess the separability of different wetland types and their trends over time. Overall, our analyses showed that coherence can be considered as an added value feature for wetland classification and monitoring. The Treed Bog and Shallow Open Water classes showed the highest and lowest coherence values, respectively. The Treed Wetland and Open Wetland classes were easily distinguishable. When analyzing the wetland subclasses, it was observed that the Treed Bog and Shallow Open Water classes can be easily discriminated from other subclasses. However, there were overlaps between the signatures of the other wetland subclasses, although there were still some dates where these classes were also distinguishable. The analysis of multi-temporal coherence products also showed that the coherence products generated in spring/fall (e.g., May and October) and summer (e.g., July) seasons had the highest and lowest coherence values, respectively. It was also observed that wetland classes preserved coherence during the leaf-off season (15 August–15 October) while they had relatively lower coherence during the leaf-on season (i.e., 15 May–15 August). Finally, several suggestions for future studies were provided.

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Detection of Crop Seeding and Harvest through Analysis of Time-Series Sentinel-1 Interferometric SAR Data

Cited by 50 publications

References 49 publications

Monitoring Large-Scale Restoration Interventions from Land Preparation to Biomass Growth in the Sahel

Monitoring Large-Scale Restoration Interventions from Land Preparation to Biomass Growth in the Sahel

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

InSAR Coherence Analysis for Wetlands in Alberta, Canada Using Time-Series Sentinel-1 Data

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