Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms

Remote sensing offers a way to map crop types across large spatio-temporal scales at low costs. However, mapping crop types is challenging in heterogeneous, smallholder farming systems, such as those in India, where field sizes are often smaller than the resolution of historically available imagery. In this study, we examined the potential of relatively new, high-resolution imagery (Sentinel-1, Sentinel-2, and PlanetScope) to identify four major crop types (maize, mustard, tobacco, and wheat) in eastern India using support vector machine (SVM). We found that a trained SVM model that included all three sensors led to the highest classification accuracy (85%), and the inclusion of Planet data was particularly helpful for classifying crop types for the smallest farms (<600 m2). This was likely because its higher spatial resolution (3 m) could better account for field-level variations in smallholder systems. We also examined the impact of image timing on the classification accuracy, and we found that early-season images did little to improve our models. Overall, we found that readily available Sentinel-1, Sentinel-2, and Planet imagery were able to map crop types at the field-scale with high accuracy in Indian smallholder systems. The findings from this study have important implications for the identification of the most effective ways to map crop types in smallholder systems.

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Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040277 ◽

2020 ◽

Vol 9 (4) ◽

pp. 277 ◽

Cited By ~ 2

Author(s):

Luka Rumora ◽

Mario Miler ◽

Damir Medak

Keyword(s):

Machine Learning ◽

Land Cover ◽

Classification Accuracy ◽

Atmospheric Correction ◽

Land Cover Classification ◽

Support Vector ◽

Atmospheric Effects ◽

Surface Reflectance ◽

The Impact ◽

Sentinel 2

Atmospheric correction is one of the key parts of remote sensing preprocessing because it can influence and change the final classification result. This research examines the impact of five different atmospheric correction processing on land cover classification accuracy using Sentinel-2 satellite imagery. Those are surface reflectance (SREF), standardized surface reflectance (STDSREF), Sentinel-2 atmospheric correction (S2AC), image correction for atmospheric effects (iCOR), dark object subtraction (DOS) and top of the atmosphere (TOA) reflectance without any atmospheric correction. Sentinel-2 images corrected with stated atmospheric corrections were classified using four different machine learning classification techniques namely extreme gradient boosting (XGB), random forests (RF), support vector machine (SVM) and catboost (CB). For classification, five different classes were used: bare land, low vegetation, high vegetation, water and built-up area. SVM classification provided the best overall result for twelve dates, for all atmospheric corrections. It was the best method for both cases: when using Sentinel-2 bands and radiometric indices and when using just spectral bands. The best atmospheric correction for classification with SVM using radiometric indices is S2AC with the median value of 96.54% and the best correction without radiometric indices is STDSREF with the median value of 96.83%.

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Classification of standing and sitting phases based on in-socket piezoelectric sensors in a transfemoral amputee

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2018-0249 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Tawfik Yahya ◽

Nur Azah Hamzaid ◽

Sadeeq Ali ◽

Farahiyah Jasni ◽

Hanie Nadia Shasmin

Keyword(s):

Time Domain ◽

Classification Accuracy ◽

Sensory System ◽

Metabolic Energy ◽

Support Vector ◽

Data Set ◽

Transfemoral Amputee ◽

Sit To Stand ◽

Optimal Classifier ◽

The Impact

AbstractA transfemoral prosthesis is required to assist amputees to perform the activity of daily living (ADL). The passive prosthesis has some drawbacks such as utilization of high metabolic energy. In contrast, the active prosthesis consumes less metabolic energy and offers better performance. However, the recent active prosthesis uses surface electromyography as its sensory system which has weak signals with microvolt-level intensity and requires a lot of computation to extract features. This paper focuses on recognizing different phases of sitting and standing of a transfemoral amputee using in-socket piezoelectric-based sensors. 15 piezoelectric film sensors were embedded in the inner socket wall adjacent to the most active regions of the agonist and antagonist knee extensor and flexor muscles, i. e. region with the highest level of muscle contractions of the quadriceps and hamstring. A male transfemoral amputee wore the instrumented socket and was instructed to perform several sitting and standing phases using an armless chair. Data was collected from the 15 embedded sensors and went through signal conditioning circuits. The overlapping analysis window technique was used to segment the data using different window lengths. Fifteen time-domain and frequency-domain features were extracted and new feature sets were obtained based on the feature performance. Eight of the common pattern recognition multiclass classifiers were evaluated and compared. Regression analysis was used to investigate the impact of the number of features and the window lengths on the classifiers’ accuracies, and Analysis of Variance (ANOVA) was used to test significant differences in the classifiers’ performances. The classification accuracy was calculated using k-fold cross-validation method, and 20% of the data set was held out for testing the optimal classifier. The results showed that the feature set (FS-5) consisting of the root mean square (RMS) and the number of peaks (NP) achieved the highest classification accuracy in five classifiers. Support vector machine (SVM) with cubic kernel proved to be the optimal classifier, and it achieved a classification accuracy of 98.33 % using the test data set. Obtaining high classification accuracy using only two time-domain features would significantly reduce the processing time of controlling a prosthesis and eliminate substantial delay. The proposed in-socket sensors used to detect sit-to-stand and stand-to-sit movements could be further integrated with an active knee joint actuation system to produce powered assistance during energy-demanding activities such as sit-to-stand and stair climbing. In future, the system could also be used to accurately predict the intended movement based on their residual limb’s muscle and mechanical behaviour as detected by the in-socket sensory system.

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Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images

Remote Sensing ◽

10.3390/rs13193956 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3956

Author(s):

Shan He ◽

Huaiyong Shao ◽

Wei Xian ◽

Shuhui Zhang ◽

Jialong Zhong ◽

...

Keyword(s):

Remote Sensing ◽

Single Type ◽

Support Vector ◽

Svm Classifier ◽

Cultivated Land ◽

Remote Sensing Images ◽

Data Set ◽

Abandoned Land ◽

Spatio Temporal ◽

Sentinel 2

Hilly areas are important parts of the world’s landscape. A marginal phenomenon can be observed in some hilly areas, leading to serious land abandonment. Extracting the spatio-temporal distribution of abandoned land in such hilly areas can protect food security, improve people’s livelihoods, and serve as a tool for a rational land plan. However, mapping the distribution of abandoned land using a single type of remote sensing image is still challenging and problematic due to the fragmentation of such hilly areas and severe cloud pollution. In this study, a new approach by integrating Linear stretch (Ls), Maximum Value Composite (MVC), and Flexible Spatiotemporal DAta Fusion (FSDAF) was proposed to analyze the time-series changes and extract the spatial distribution of abandoned land. MOD09GA, MOD13Q1, and Sentinel-2 were selected as the basis of remote sensing images to fuse a monthly 10 m spatio-temporal data set. Three pieces of vegetation indices (VIs: ndvi, savi, ndwi) were utilized as the measures to identify the abandoned land. A multiple spatio-temporal scales sample database was established, and the Support Vector Machine (SVM) was used to extract abandoned land from cultivated land and woodland. The best extraction result with an overall accuracy of 88.1% was achieved by integrating Ls, MVC, and FSDAF, with the assistance of an SVM classifier. The fused VIs image set transcended the single source method (Sentinel-2) with greater accuracy by a margin of 10.8–23.6% for abandoned land extraction. On the other hand, VIs appeared to contribute positively to extract abandoned land from cultivated land and woodland. This study not only provides technical guidance for the quick acquirement of abandoned land distribution in hilly areas, but it also provides strong data support for the connection of targeted poverty alleviation to rural revitalization.

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Satellite Constellation Reveals Crop Growth Patterns and Improves Mapping Accuracy of Cropping Practices for Subtropical Small-Scale Fields in Japan

Remote Sensing ◽

10.3390/rs12152419 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2419

Author(s):

Asahi Sakuma ◽

Hiroya Yamano

Keyword(s):

Classification Accuracy ◽

Vegetation Indices ◽

Temporal Variations ◽

Crop Growth ◽

Growth Patterns ◽

Small Scale ◽

Satellite Constellation ◽

High Classification Accuracy ◽

Crop Types ◽

Spatio Temporal

Mapping of agricultural crop types and practices is important for setting up agricultural production plans and environmental conservation measures. Sugarcane is a major tropical and subtropical crop; in general, it is grown in small fields with large spatio-temporal variations due to various crop management practices, and satellite observations of sugarcane cultivation areas are often obscured by clouds. Surface information with high spatio-temporal resolution obtained through the use of emerging satellite constellation technology can be used to track crop growth patterns with high resolution. In this study, we used Planet Dove imagery to reveal crop growth patterns and to map crop types and practices on subtropical Kumejima Island, Japan (lat. 26°21′01.1″ N, long. 126°46′16.0″ E). We eliminated misregistration between the red-green-blue (RGB) and near-infrared band imagery, and generated a time series of seven vegetation indices to track crop growth patterns. Using the Random Forest algorithm, we classified eight crop types and practices in the sugarcane. All the vegetation indices tested showed high classification accuracy, and the normalized difference vegetation index (NDVI) had an overall accuracy of 0.93 and Kappa of 0.92 range of accuracy for different crop types and practices in the study area. The results for the user’s and producer’s accuracy of each class were good. Analysis of the importance of variables indicated that five image sets are most important for achieving high classification accuracy: Two image sets of the spring and summer sugarcane plantings in each year of a two-year observation period, and one just before harvesting in the second year. We conclude that high-temporal-resolution time series images obtained by a satellite constellation are very effective in small-scale agricultural mapping with large spatio-temporal variations.

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Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem

Remote Sensing ◽

10.3390/rs12010198 ◽

2020 ◽

Vol 12 (1) ◽

pp. 198 ◽

Cited By ~ 4

Author(s):

Frederick D.L. Hunter ◽

Edward T.A. Mitchard ◽

Peter Tyrrell ◽

Samantha Russell

Keyword(s):

Time Series ◽

Land Degradation ◽

Classification Accuracy ◽

Grass Species ◽

Support Vector ◽

Invasive Shrub ◽

Seasonal Time Series ◽

Large Populations ◽

Dry Seasons ◽

Sentinel 2

In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imagery—images acquired within and across seasons—on the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10–20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of images, including dry season imagery, results in improved classification accuracy regardless of the classifier (average increase in overall accuracy (OA) = 0.1632). SVM consistently outperformed RF, and the most accurate model and was SVM with a radial kernel using imagery from both wet and dry seasons (OA = 0.8217). Maps showed that seasonal grazing areas provide functionally different grazing opportunities and have different vegetation characteristics that are critical to a landscape’s ability to support large populations of both livestock and wildlife. This study highlights the potential of multi-spectral satellite imagery for species-level mapping of savannas.

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Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data

10.20944/preprints202010.0517.v1 ◽

2020 ◽

Author(s):

Grace Aduvukha ◽

Elfatih Abdel-Rahman ◽

Arthur Sichangi ◽

Godfrey Makokha ◽

Tobias Landmann ◽

...

Keyword(s):

Agricultural Production ◽

Vegetation Indices ◽

Cropping Pattern ◽

Smallholder Farming ◽

Radar Backscatter ◽

Cropping Patterns ◽

Heterogeneous Landscape ◽

High Resolution Imagery ◽

Test Of Significance ◽

Sentinel 2

The proportion of area under various crops at a given point in time, known as a cropping pattern, plays an essential role in determining the level of agricultural production. In this study, cropping patterns of three sub-counties in Murang’a County, a typical African smallholder farming area in Kenya, were mapped. Specifically, we compared the performance of eight classification scenarios for mapping cropping patterns; namely using (i) only Sentinel-2 reflectance bands (S2), (ii) S2 and S2 derived vegetation indices (VIs); (iii) S2 and S2 vegetation phenology (VP); (iv) S2 and Sentinel-1 radar backscatter data (S1); (v) S2, VIs, and S1; (vi) S2, VP, and S1; (vii) S2, VIs and VP, and (viii) S2, VIs, VP and S1. Reference data of the dominant cropping patterns and non-croplands were collected. The guided regularized random forest (GRRF) algorithm was used to select the optimum variables and to perform the respective classification for each scenario. The most accurate result of the overall accuracy of 93.16% was attained from the scenario (viii) S2, VIs, VP, and S1. The McNemar’s test of significance did not show significant differences (p≤0.05) among the tested scenarios. Our study demonstrated the strength of GRRF and the synergetic advantage of S2 and S1 derivatives to map cropping patterns in a heterogeneous landscape where high resolution imagery are inaccessible. Our cropping pattern mapping approach can be used in other sites of relatively similar agro-ecological conditions.

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THE USE OF SPECTRAL AND TEXTURAL FEATURES IN CROP TYPE MAPPING USING SENTINEL-2A IMAGES: A CASE STUDY, ÇUKUROVA REGION, TURKEY

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

10.5194/isprs-archives-xliii-b3-2021-117-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 117-122

Author(s):

A. Tuzcu Kokal ◽

A. F. Sunar ◽

A. Dervisoglu ◽

S. Berberoglu

Keyword(s):

Satellite Image ◽

Support Vector ◽

Textural Features ◽

Factors Affecting ◽

Object Based ◽

Crop Types ◽

Sentinel 2A ◽

Occurrence Matrix ◽

Agriculture Management ◽

Sentinel 2

Abstract. Turkey has favorable agricultural conditions (i.e. fertile soils, climate and rainfall) and can grow almost any type of crop in many regions, making it one of the leading sectors of the economy. For sustainable agriculture management, all factors affecting the agricultural products should be analyzed on a spatial-temporal basis. Therefore, nowadays space technologies such as remote sensing are important tools in providing an accurate mapping of the agricultural fields with timely monitoring and higher repetition frequency and accuracy. In this study, object based classification method was applied to 2017 Sentinel 2 Level 2A satellite image in order to map crop types in the Adana, Çukurova region in Turkey. Support Vector Machine (SVM) was used as a classifier. Texture information were incorporated to spectral wavebands of Sentinel-2 image, to increase the classification accuracy. In this context, all of the textural features of Gray-Level Co-occurrence Matrix (GLCM) were tested and Entropy, Standard deviation, and Mean textural features were found to be the most suitable among them. Multi-spectral and textural features were used as an input separately and/or in combination to evaluate the potential of texture in differentiating crop types and the accuracy of output thematic maps. As a result, with the addition of textural features, it was observed that the Overall Accuracy and Kappa coefficient increased by 7% and 8%, respectively.

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AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

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

10.5194/isprs-archives-xlii-1-451-2018 ◽

2018 ◽

Vol XLII-1 ◽

pp. 451-456 ◽

Cited By ~ 1

Author(s):

M. Ustuner ◽

F. B. Sanli ◽

S. Abdikan ◽

M. T. Esetlili ◽

G. Bilgin

Keyword(s):

Classification Accuracy ◽

Alpha Angle ◽

Test Site ◽

Classification Performance ◽

Support Vector ◽

Observation Data ◽

Data Set ◽

Crop Classification ◽

Sar Data ◽

Spatio Temporal

Abstract. Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (&alpha;) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) H&alpha;, (2) H&alpha;Span, (3) H&alpha;A, (4) H&alpha;ASpan and (5) coherency [T] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that H&alpha;ASpan (91.43% for SVM, 92.25% for RF and 90.55% for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25% by RF and H&alpha;ASpan while lowest classification accuracy was obtained as 66.99% by NB and H&alpha;. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification.

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Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel-2 MSI: the importance of model functional form

10.5194/egusphere-egu21-2039 ◽

2021 ◽

Author(s):

Ibrahim Alameddine ◽

Mohamad Abbas

Keyword(s):

Water Quality ◽

Harmful Algal Blooms ◽

Algal Blooms ◽

Multivariate Adaptive Regression Splines ◽

Support Vector ◽

Negative Consequences ◽

Significant Drop ◽

Chl A ◽

Spatio Temporal ◽

Sentinel 2

Anthropogenic eutrophication is a pressing global environmental problem that threatens the ecological functions of many inland freshwaters and diminishes their abilities to meet their designated uses. Water authorities worldwide are being pressed to manage the negative consequences of harmful algal blooms (HABs) based largely on data collected from conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management. This study assesses the potential of using Sentinel 2 MSI to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly-monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive HABs during the growing season. The performance and transferability of water quality models previously calibrated based on Landsat 7 and 8 surface reflectance to predict Chlorophyll-a (Chl-a), total suspended solids (TSS), Secchi Disk Depth (SDD), and Phycocyanin (PC) levels in the reservoir are first assessed. The results showed poor transferability between Landsat and Sentinel 2, with all models experiencing a significant drop in their predictive skill. Sentinel 2 specific models were then developed for the reservoir based on 153 water quality samples collected over two years. Different model functional forms were then tested, including multiple linear regressions (MR), multivariate adaptive regression splines (MARS), and support vector regressions (SVR). Our results showed that for Chl-a, the MARS model outperformed MR and SVR, with an R2 of 60%. Meanwhile, the SVR-based models outperformed their MR and MARS counterparts for TSS, SDD and PC (R2 = 59%, 94%, and 72% respectively).

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‘More vital to our future than we realize?’ Learning from Netting’s thesis on smallholder farming, 25 years on

Outlook on Agriculture ◽

10.1177/0030727017744931 ◽

2017 ◽

Vol 46 (4) ◽

pp. 258-264 ◽

Cited By ~ 2

Author(s):

Stephen Whitfield

Keyword(s):

Agricultural Research ◽

Commodity Prices ◽

Farming Systems ◽

Sub Saharan Africa ◽

Smallholder Agriculture ◽

Smallholder Farming ◽

Household Organization ◽

Sub Saharan ◽

Contemporary Context ◽

Smallholder Systems

Twenty-five years on from Netting’s paradigm challenging thesis about the dynamic efficiencies of household organization and the sophisticated nature of smallholder farming systems, the work continues to have relevance to contemporary debates about the future of smallholder agriculture in sub-Saharan Africa (SSA). This review is organized around four contemporary challenges for smallholder agriculture in SSA: (i) market centralization, liberalization and falling commodity prices; (ii) shifting agricultural research agendas and innovation funding; (iii) environmental degradation and climate change; and (iv) population pressures, large land acquisition and limited land availability. In each case, an argument inferred from Netting’s thesis is presented alongside recent evidence, predominantly from research in SSA that supports and challenges it. Based on the lessons of Netting, in this contemporary context, it is argued that smallholder systems continue to have value and relevance and that rather than implementing protectionist strategies based on generic assumptions about smallholder vulnerability, that effort should be made to learn from the diversity of smallholder systems, knowledges and experiences of adapting to change.

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