Application of nanosatellites PlanetScope data to monitor crop growth

Kokhan, Svitlana; Vostokov, Anatoliy

doi:10.1051/e3sconf/202017102014

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…We used the Level-3B surface reflectance products that were atmospherically corrected by Planet Labs using the 6S radiative transfer model with ancillary data from Moderate Resolution Imaging Spectroradiometer (MODIS) [42,43]. Due to its high spatial resolution, the application of PlanetScope images includes change detection [44], crop monitoring [45], and vegetation detection [46]. Unlike the other data used in this study, PlanetScope data are not free, but through the Education and Research program of Planet Labs, special permission was given for free download.…”

Section: Data Acquisition 221 Satellite Datamentioning

confidence: 99%

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

et al. 2021

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Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C2 matrix and S-1 H/α polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C2) matrix + H/α polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making.

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Section: Data Acquisition 221 Satellite Datamentioning

confidence: 99%

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

et al. 2021

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“…Landsat 8 consists of two sensors -the Operational Land Imager (OLI) and Thermal Infrared Sensor (TRIS). These two sensors provide the images at spatial resolution via bands covering Visible, NIR, SWIR, Thermal and Panchromatic [8][9][10].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The image collection were selected in such a way that they possess least cloud cover during the corresponding dates i.e., 01-01-2020 to 01-02-2021. Furthermore, the classification and boundary detection was applied using the ground truth data obtained through Google Earth [9].…”

Section: Data Acquisitionmentioning

confidence: 99%

Crop Field Boundary Detection and Classification using Machine Learning

Mylapalli

2024

SCPE

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Crop classification and detection of crop field boundaries empower farmers which helps the agricultural businesses to estimate crop field dimensions and yields accurately. Our research focuses on estimating crucial agricultural inputs such as seeds, pesticides, insecticides, and fertilizers to enhance overall production. The conventional method of manually identifying field boundaries is both time-consuming with labour-intensive. In contrast, our study harnesses data from diverse satellites such as Sentinel, Landsat, and MODIS, encompassing valuable land usage information. By integrating this data with machine learning algorithms, we achieve real-time monitoring of crop fields through effective classification and boundary identification. For the classification of crop fields within our study area, we recommend employing the Classification and Regression Tree (CART) algorithm. Additionally, we leverage normalized difference indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), as features for classification. We compare these features with Support Vector Machine (SVM) and Random Forest (RF) algorithms. Subsequently, we utilize the Canny edge detection technique to identify boundaries within the classified crop areas. Notably, our approach utilizes the Google Earth Engine (GEE) as a primary platform for extracting features, conducting data training, and visualizing information. The proposed algorithm yields impressive results with a high level of accuracy. Notably, the CART algorithm achieves a remarkable accuracy rate of 96.1%. Furthermore, we incorporate NDWI-based Canny edge detection into our methodology. The outcomes convincingly underscore the practicality and applicability of our research in real-world scenarios.

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“…The high temporal resolution of PlanetScope data also allowed for monitoring of algal bloom events and small changes in water quality [55]. In addition, PlanetScope images have been used for several agricultural applications, including monitoring crop growth, management, health, and productivity [48], [50], [51], [56]- [62].…”

Section: B Planetscopementioning

confidence: 99%

New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification

Aneece

Foley

Thenkabail

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur Luft -und Raumfahrt-(DLR) Earth Sensing Imaging Spectrometer (DESIS) and high spatial resolution PlanetScope in classifying eight crop types in California's Central Valley during the 2020 growing season. The DESIS sensor onboard the International Space Station collects data at 235 hyperspectral narrowbands (HNB) each with 2.55 nm bandwidth from 400-1000 nm and 30 m spatial resolution. In contrast, PlanetScope Dove-R data have 4 multispectral broadbands (MBB) with 3-4 m spatial resolution. We obtained best classification accuracies using 14 DESIS HNB from the August 2020 image, with an overall accuracy of 85% and producer's and user's accuracies of 72-100% and 75-100% respectively for the eight crops. The best classification accuracies using PlanetScope data were obtained using an image mosaic pair from June and August 2020; this resulted in an overall accuracy of 79% and producer's and user's accuracies of 56-100% and 61-100% respectively. Combining the best 14 DESIS HNB from August 2020 with the 4 PlanetScope MBB from August 2020 yielded an overall accuracy of 82% and producer's and user's accuracies of 65-100% and 60-94% respectively. On one-to-one single date comparisons of DESIS versus PlanetScope data, the hyperspectral data always outperformed high spatial resolution data in crop type classification. Nevertheless, high spatial resolution data will remain invaluable in assessing withinfield variability and crop biophysical/biochemical modeling in precision agriculture.

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Application of nanosatellites PlanetScope data to monitor crop growth

Cited by 7 publications

References 14 publications

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

Crop Field Boundary Detection and Classification using Machine Learning

New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification

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