scholarly journals Using Sentinel-2 to Track Field-Level Tillage Practices at Regional Scales in Smallholder Systems

2021 ◽  
Vol 13 (24) ◽  
pp. 5108
Author(s):  
Weiqi Zhou ◽  
Preeti Rao ◽  
Mangi L. Jat ◽  
Balwinder Singh ◽  
Shishpal Poonia ◽  
...  
Keyword(s):  
Satellite Data ◽  
Management Practices ◽  
Growing Season ◽  
Google Earth ◽  
Large Field ◽  
Zero Tillage ◽  
Tillage Practices ◽  
Spatio Temporal ◽  
Sentinel 2 ◽  
Smallholder Systems

Zero tillage is an important pathway to sustainable intensification and low-emission agriculture. However, quantifying the extent of zero tillage adoption at the field scale has been challenging, especially in smallholder systems where field sizes are small and there is limited ground data on zero tillage adoption. Remote sensing offers the ability to map tillage practices at large spatio-temporal scales, yet to date no studies have used satellite data to map zero tillage adoption in smallholder agricultural systems. In this study, we use Sentinel-2 satellite data, random forest classifiers, and Google Earth Engine to map tillage practices across India’s main grain producing region, the Indo-Gangetic Plains. We find that tillage practices can be classified with moderate accuracy (an overall accuracy of 75%), particularly in regions with relatively large field sizes and homogenous crop management practices. We find that models that use satellite data from only the first half of the growing season perform as well as models that use data throughout the growing season, allowing for the creation of within-season tillage maps. Finally, we find that our model can generalize well through time in the western IGP, with reductions in accuracy of only 5–10%. Our results highlight the ability of Sentinel-2 satellite data to map tillage practices at scale, even in smallholder systems where field sizes are small and cropping practices are heterogeneous.

scholarly journals High-Resolution Sea Surface Temperature and Salinity in the Global Ocean from Raw Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2191 ◽  
Author(s):  
Encarni Medina-Lopez ◽  
Leonardo Ureña-Fuentes
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Data ◽  
Google Earth ◽  
Sea Surface ◽  
Global Ocean ◽  
The Neural Network ◽  
Wide Range ◽  
Sentinel 2

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

scholarly journals FATE OF AGRICULTURAL AREAS OF KAILALI DISTRICT OF NEPAL: A TEMPORAL LAND USE LAND COVER CHANGE (LUCC) ANALYSIS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1601-1606
Author(s):  
◽  
L. Thapa ◽  
D. P. Shukla
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Management Practices ◽  
Agricultural Land ◽  
Local Population ◽  
Google Earth ◽  
Series Data ◽  
Land Use Land Cover ◽  
Land Resources

Abstract. Changes of agricultural land into non-agricultural land is the main issue of increasing population and urbanization. The objective of this paper is to identify the various land resources and its changes into other Land Use Land Cover (LULC) type. LANDSAT satellite data for 1990, 2000, 2010 and 2018 years of Kailali district Nepal was acquired for supervised LULC mapping and change analysis using ENVI 5.4 software. Sentinel-2 and Google earth satellite data were used for the accuracy assessment of the LULC map. The time-series data analysis from 1990–2000–2010–2018 shows major changes in vegetation and agriculture. The changes in LULC show that settlement and bare land is continuously increasing throughout these years. The change in land use and land cover during the period of 1990–2018 shows that the settlement area is increased by 204%; and agriculture is decreased by 57%. The fluctuating behavior of vegetation, agriculture and water bodies in which the areas decrease and increase over the selected periods is due to natural calamities and migration of the local population. This shows that human influence on the land resources is accelerating and leading to a deterioration of agricultural land. Thus effective agricultural management practices and policies should be carried out at the government level for minimizing land resources degradation by the human-induced impact.

scholarly journals Sight for Sorghums: Comparisons of Satellite- and Ground-Based Sorghum Yield Estimates in Mali

2019 ◽  
Vol 12 (1) ◽  
pp. 100 ◽  
Author(s):  
David B. Lobell ◽  
Stefania Di Tommaso ◽  
Calum You ◽  
Ismael Yacoubou Djima ◽  
Marshall Burke ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Growing Season ◽  
Survey Experiment ◽  
Yield Data ◽  
Satellite Systems ◽  
Model Training ◽  
Better Than ◽  
Sentinel 2 ◽  
Peak Value ◽  
Smallholder Systems

The advent of multiple satellite systems capable of resolving smallholder agricultural plots raises possibilities for significant advances in measuring and understanding agricultural productivity in smallholder systems. However, since only imperfect yield data are typically available for model training and validation, assessing the accuracy of satellite-based estimates remains a central challenge. Leveraging a survey experiment in Mali, this study uses plot-level sorghum yield estimates, based on farmer reporting and crop cutting, to construct and evaluate estimates from three satellite-based sensors. Consistent with prior work, the analysis indicates low correlation between the ground-based yield measures (r = 0.33). Satellite greenness, as measured by the growing season peak value of the green chlorophyll vegetation index from Sentinel-2, correlates much more strongly with crop cut (r = 0.48) than with self-reported (r = 0.22) yields. Given the inevitable limitations of ground-based measures, the paper reports the results from the regressions of self-reported, crop cut, and (crop cut-calibrated) satellite sorghum yields. The regression covariates explain more than twice as much variation in calibrated satellite yields (R2 = 0.25) compared to self-reported or crop cut yields, suggesting that a satellite-based approach anchored in crop cuts can be used to track sorghum yields as well or perhaps better than traditional measures. Finally, the paper gauges the sensitivity of yield predictions to the use of Sentinel-2 versus higher-resolution imagery from Planetscope and DigitalGlobe. All three sensors exhibit similar performance, suggesting little gains from finer resolutions in this system.

scholarly journals Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

2020 ◽  
Vol 12 (3) ◽  
pp. 522 ◽  
Author(s):  
Abdul Qadir ◽  
Pinki Mondal
Keyword(s):  
Satellite Data ◽  
Critical Role ◽  
Mixed Forest ◽  
Google Earth ◽  
Optical Data ◽  
Growth Stages ◽  
Global Agriculture ◽  
Optical Satellite Data ◽  
Sentinel 2 ◽  
Cropland Mapping

Monsoon crops play a critical role in Indian agriculture, hence, monitoring these crops is vital for supporting economic growth and food security for the country. However, monitoring these crops is challenging due to limited availability of optical satellite data due to cloud cover during crop growth stages, landscape heterogeneity, and small field sizes. In this paper, our objective is to develop a robust methodology for high-resolution (10 m) monsoon cropland mapping appropriate for different agro-ecological regions (AER) in India. We adapted a synergistic approach of combining Sentinel-1 Synthetic Aperture Radar (SAR) data with Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical data using the Google Earth Engine platform. We developed a new technique, Radar Optical cross Masking (ROM), for separating cropland from non-cropland by masking out forest, plantation, and other non-dynamic features. The methodology was tested for five different AERs in India, representing a wide diversity in agriculture, soil, and climatic variations. Our findings indicate that the overall accuracy obtained by using the SAR-only approach is 90%, whereas that of the combined approach is 93%. Our proposed methodology is particularly effective in regions with cropland mixed with tree plantation/mixed forest, typical of smallholder dominated tropical countries. The proposed agriculture mask, ROM, has high potential to support the global agriculture monitoring missions of Geo Global Agriculture Monitoring (GEOGLAM) and Sentinel-2 for Agriculture (S2Agri) project for constructing a dynamic monsoon cropland mask.

scholarly journals Sentinel-2 Analysis of Spruce Crown Transparency Levels and Their Environmental Drivers After Summer Drought in the Northern Eifel (Germany)

2021 ◽  
Vol 4 ◽  
Author(s):  
Carsten Montzka ◽  
Bagher Bayat ◽  
Andreas Tewes ◽  
David Mengen ◽  
Harry Vereecken
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Google Earth ◽  
Training Data ◽  
Cumulative Distribution ◽  
Environmental Drivers ◽  
Summer Drought ◽  
Data Set ◽  
Crown Transparency ◽  
Sentinel 2

Droughts in recent years weaken the forest stands in Central Europe, where especially the spruce suffers from an increase in defoliation and mortality. Forest surveys monitor this trend based on sample trees at the local scale, whereas earth observation is able to provide area-wide information. With freely available cloud computing infrastructures such as Google Earth Engine, access to satellite data and high-performance computing resources has become straightforward. In this study, a simple approach for supporting the spruce monitoring by Sentinel-2 satellite data is developed. Based on forest statistics and the spruce NDVI cumulative distribution function of a reference year, a training data set is obtained to classify the satellite data of a target year. This provides insights into the changes in tree crown transparency levels. For the Northern Eifel region, Germany, the evaluation shows an increase in damaged trees from 2018 to 2020, which is in line with the forest inventory of North Rhine-Westphalia. An analysis of tree damages according to precipitation, land surface temperature, elevation, aspect, and slope provides insights into vulnerable spruce habitats of the region and enables to identify locations where the forest management may focus on a transformation from spruce monocultures to mixed forests with higher biodiversity and resilience to further changes in the climate system.

scholarly journals A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3232
Author(s):  
Nicola Genzano ◽  
Nicola Pergola ◽  
Francesco Marchese
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Hot Spot ◽  
Global Scale ◽  
Google Earth ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Google Earth Engine ◽  
Sentinel 2

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.

scholarly journals Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine

2019 ◽  
Vol 11 (3) ◽  
pp. 288 ◽  
Author(s):  
Luis Carrasco ◽  
Aneurin O’Neil ◽  
R. Morton ◽  
Clare Rowland
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Cloud Cover ◽  
Google Earth ◽  
Land Cover Mapping ◽  
Temporal Aggregation ◽  
Landsat 8 ◽  
Composite Approach ◽  
Google Earth Engine ◽  
Sentinel 2

Land cover mapping of large areas is challenging due to the significant volume of satellite data to acquire and process, as well as the lack of spatial continuity due to cloud cover. Temporal aggregation—the use of metrics (i.e., mean or median) derived from satellite data over a period of time—is an approach that benefits from recent increases in the frequency of free satellite data acquisition and cloud-computing power. This enables the efficient use of multi-temporal data and the exploitation of cloud-gap filling techniques for land cover mapping. Here, we provide the first formal comparison of the accuracy between land cover maps created with temporal aggregation of Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8 (L8) data from one-year and test whether this method matches the accuracy of traditional approaches. Thirty-two datasets were created for Wales by applying automated cloud-masking and temporally aggregating data over different time intervals, using Google Earth Engine. Manually processed S2 data was used for comparison using a traditional two-date composite approach. Supervised classifications were created, and their accuracy was assessed using field-based data. Temporal aggregation only matched the accuracy of the traditional two-date composite approach (77.9%) when an optimal combination of optical and radar data was used (76.5%). Combined datasets (S1, S2 or S1, S2, and L8) outperformed single-sensor datasets, while datasets based on spectral indices obtained the lowest levels of accuracy. The analysis of cloud cover showed that to ensure at least one cloud-free pixel per time interval, a maximum of two intervals per year for temporal aggregation were possible with L8, while three or four intervals could be used for S2. This study demonstrates that temporal aggregation is a promising tool for integrating large amounts of data in an efficient way and that it can compensate for the lower quality of automatic image selection and cloud masking. It also shows that combining data from different sensors can improve classification accuracy. However, this study highlights the need for identifying optimal combinations of satellite data and aggregation parameters in order to match the accuracy of manually selected and processed image composites.

scholarly journals A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS

2021 ◽  
Vol 13 (9) ◽  
pp. 1608
Author(s):  
Miguel M. Pinto ◽  
Ricardo M. Trigo ◽  
Isabel F. Trigo ◽  
Carlos C. DaCamara
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Computation Time ◽  
Practical Method ◽  
Google Earth ◽  
Burned Area ◽  
High Resolution Data ◽  
Burned Areas ◽  
Resolution Data ◽  
Sentinel 2

Mapping burned areas using satellite imagery has become a subject of extensive research over the past decades. The availability of high-resolution satellite data allows burned area maps to be produced with great detail. However, their increasing spatial resolution is usually not matched by a similar increase in the temporal domain. Moreover, high-resolution data can be a computational challenge. Existing methods usually require downloading and processing massive volumes of data in order to produce the resulting maps. In this work we propose a method to make this procedure fast and yet accurate by leveraging the use of a coarse resolution burned area product, the computation capabilities of Google Earth Engine to pre-process and download Sentinel-2 10-m resolution data, and a deep learning model trained to map the multispectral satellite data into the burned area maps. For a 1500 ha fire our method can generate a 10-m resolution map in about 5 min, using a computer with an 8-core processor and 8 GB of RAM. An analysis of six important case studies located in Portugal, southern France and Greece shows the detailed computation time for each process and how the resulting maps compare to the input satellite data as well as to independent reference maps produced by Copernicus Emergency Management System. We also analyze the feature importance of each input band to the final burned area map, giving further insight about the differences among these events.

scholarly journals Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms

2021 ◽  
Vol 13 (7) ◽  
pp. 1349
Author(s):  
Laleh Ghayour ◽  
Aminreza Neshat ◽  
Sina Paryani ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Google Earth ◽  
Kernel Functions ◽  
Support Vector ◽  
Landsat 8 ◽  
Penalty Parameter ◽  
Tuning Parameters ◽  
Sentinel 2

With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers.

scholarly journals Effect of Tillage and Weed Control Techniques on Energy Dynamics and Profitability of Chickpea (Cicer arietinum L.) - Rice Cropping Sequence in Irrigated Ecosystem of C. G. Plains

2019 ◽  
pp. 1-8
Author(s):  
Anjum Ahmad ◽  
T. Chowdhury ◽  
Adyant Kumar
Keyword(s):  
Weed Management ◽  
Energy Use ◽  
Management Practices ◽  
Conventional Tillage ◽  
Maximum Energy ◽  
Zero Tillage ◽  
Energy Productivity ◽  
Net Return ◽  
Tillage Practices ◽  
Hand Weeding

A field study was conducted during rabi seasons of 2010-11 and 2011-12 at the Research cum Instructional Farm of Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh to evaluate the effect of various tillage and weed management techniques on energy dynamics and profitability of chickpea-rice cropping sequence in irrigated ecosystem of C.G. plains. The results indicate that plots were divided into main and sub plots (tillage and weed management practices). Three tillage practices viz., conventional tillage (T1),  minimum tillage (T2) and zero tillage (T3) in main plot and nine weed management practices as pendimethalin @ 1000 g ha-1 PE (W1), imazethapyr @ 80 g ha-1 PE (W2), imazethapyr @ 90 g ha-1 PE (W3), imazethapyr @ 100 g ha-1 PE (W4) at 2 DAS, imazethapyr @ 70 g ha-1 POE (W5), imazethapyr @ 80 g ha-1 POE (W6), imazethapyr @ 90 g ha-1 POE (W7) at 20 DAS, one hand weeding at 20 DAS (W8) and weedy check (W9), in sub plots. Among the various tillage practices, maximum energy use efficiency 3.74 q MJ-1 × 10-3 ha-1 and energy productivity 160.34 kg MJ-1 ha-1 were obtained with conventional tillage (T1) followed by minimum tillage (T2) and zero tillage (T3) and among the different weed control methods, maximum energy use efficiency 5.46 q MJ-1 × 10-3 ha-1 and energy productivity 233.37 kg MJ-1 ha-1 were found with one hand weeding at 20 DAS (W8) followed by post-emergence application of imazethapyr @ 90 g ha-1 (W7) followed by imazethapyr @ 80 g ha-1 PoE (W6). The economic production of experiment in terms of net return was maximum under (T1) conventional tillage Rs.19824.21 ha-1 with B:C ratio 1.19 and (W8) one hand weeding at 20 DAS Rs.19171.44 ha-1 with B:C ratio 0.95 and this was followed by @ 90 g ha-1 imazethapyr, where net return Rs.19086.74 ha-1 and B:C ratio 1.04. The minimum net return and B:C ratio was observed under zero tillage (T3) and weedy check (W9).

Sign in / Sign up

Export Citation Format

Share Document