Deep learning based multi-temporal crop classification

For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

https://halshs.archives-ouvertes.fr/halshs-02438951/document

Remote Sensing and Cropping Practices: A Review

https://pure.uva.nl/ws/files/2011400/137673_A_quantitative_framework.pdf

A quantitative framework for assessing spatial flows of ecosystem services

Agriculture in Brazilian Amazonia is going through a period of intensification. Crop mapping is important in understanding the way this intensification is occurring and the impact it is having. Two successive classifications based on MODIS MODerate Resolution Imaging Spectroradiometer-TERRA/EVI Enhanced Vegetation Index time series are applied 1 to map agricultural areas and 2 to identify five crop classes. These classes represent agricultural practices involving three commercial crops soybean, maize and cotton planted in single or double cropping systems. Both classifications are based on five steps: 1 analysis of the MODIS/EVI time series, 2 application of a smoothing algorithm, 3 application of a feature selection/extraction process to reduce the data set dimensionality, 4 application of a classifier and 5 application of a post-classification treatment. The first classification detected 95% of the agricultural areas 5 617 250 ha during the 2006–2007 harvest and correlation coefficients with agricultural statistics exceeded 0.98 for the three crop classes at municipality level. The second classification overall accuracy = 74% and kappa index = 0.675 allowed us to obtain the spatial variability mapping of agricultural practices in the state of Mato Grosso. A total of 30% of the total planted area was cultivated through double cropping systems, especially along the BR163 highway and in the Parecis plateau region.

Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

As a new high-technology with an advantage of high temporal resolution,wide coverage and low cost,remote sensing is currently used in a wide arrange of earth observation activities and thus provides a useful tool to detect and monitor the spatial patterns of crop cultivation.Based on the systematic summary of the progress of studies in remote-sensing-based monitoring of spatial patterns of agricultural crops in the latest decade,including its theories,methods and applications,a series of problems that should be urgently resolved in the study are put forward,and some important study directions and priorities for future are viewed.Studies show that crop acreage can be monitored according to the differences in spectral characteristics of different crops,which are normally recorded by the satellite sensors.There are three major approaches used for crop acreage monitoring:spectral-based identification,phenology-based identification and multiple data-fusion-based identification methods.Mapping multiple cropping systems using remote sensing is mainly based on the crop growth curves,which can be derived from the smoothed time-series vegetation index(VI) data.Furthermore,cropping patterns can be also examined through discriminating the crop growth period from variations in time-series VI data and characteristics of different cropping patterns.How to construct the theoretical and technological systems,to develop and verify the image classification methods,to optimize the smoothing methods for time-series data and to improve the capability of automatic extraction of information could be the major development trends of this field in the future.

Recent Progresses in Monitoring Crop Spatial Patterns by Using Remote Sensing Technologies

This paper presents a scenario-based assessment of global future food security. To do that, the socio-economic and climate change scenarios were defined for the future and were linked to an integrated modeling framework. The crop yields simulated by the GIS-based Environmental Policy Integrated Climate (EPIC) model and crop areas simulated by the crop choice decision model were combined to calculate the total food production and per capita food availability, which was used to represent the status of food availability and stability. The per capita Gross Domestic Product (GDP) simulated by IFPSIM model was used to reflect the situation of food accessibility and affordability. Based on these two indicators, the future food security status was assessed at a global scale over a period of approximately 20 years, starting from the year 2000. The results show that certain regions such as South Asia and most African countries will likely remain hotspots of food insecurity in the future as both the per capita food availability and the capacity of being able to import food will decrease between 2000 and 2020. Low food production associated with poverty is the determining factor to starvation in these regions, and more efforts are needed to combat hunger in terms of future actions. Other regions such as China, most Eastern European countries and most South American countries where there is an increase in per capita food availability or an increase in the capacity to import food between 2000 and 2020 might be able to improve their food security situation.

Scenario-based assessment of future food security

【Objective】 The objective of this research is to analyze the phenophases(including the stages of seeding,heading and maturity) changes of rice,maize,spring wheat and soybean in three provinces of Northeast China【Method】Annual slope change rate(θ) was computed to evaluate the temporal trend and the relationship between agroclimate and phenophases【Result】Over the past 20 years,the timing of first effective temperature date(T≥10℃) was advanced,the first frost date was postponed,the growth period was continually extended and the accumulated temperature(T≥10℃) was increased in most area of the three provincesUnder this background,the temporal trend of phenophases of both rice and maize showed an advanced seeding stage(004θ055 d?a-1 and 004θ035 d?a-1),a postponed maturity stage(009θ035 d?a-1 and 023θ038 d?a-1) and an extended length of growth period(031θ126 d?a-1 and 011θ057 d?a-1)By contrast,temporal trend of soybean phenophases was different and characterized by an advanced seeding stage(001θ061 d?a-1),a maturity stage(018θ019 d?a-1),and a shortened growth period(006θ017 d?a-1)As for spring wheat,there was no obvious temporal change for the phenophases【Conclusion】Climate change resulted in the increase of temperature suitability for crops,which could benefit from an early seeding stage,a late maturity stage and a long growth period

Zhou Qingbo

Papers

Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

Recent Progresses in Monitoring Crop Spatial Patterns by Using Remote Sensing Technologies

Scenario-based assessment of future food security

Trend analysis of typical phenophases of major crops under climate change in the three provinces of Northeast China.

Sampling and scaling scheme for monitoring the change of winter wheat acreage in China.