Accurate automated segmentation of remote sensing data could benefit applications from land cover mapping and agricultural monitoring to urban development surveyal and disaster damage assessment. While convolutional neural networks (CNNs) achieve state-of-the-art accuracy when segmenting natural images with huge labeled datasets, their successful translation to remote sensing tasks has been limited by low quantities of ground truth labels, especially fully segmented ones, in the remote sensing domain.

The recent shift in the United States from coal to natural gas as a primary feedstock for the production of electric power has reduced the intensity of sectoral carbon dioxide emissions, but—due to gaps in monitoring—its downstream pollution-related effects have been less well understood. Here, I analyse old units that have been taken offline and new units that have come online to empirically link technology switches to observed aerosol and ozone changes and subsequent impacts on human health, crop yields and regional climate.

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.

Many mountainous and high‐latitude regions have experienced more precipitation as rain rather than snow due to warmer winter temperatures. Further decreases in the annual snow fraction are projected under continued global warming, with potential impacts on flood risk. Here, we quantify the size of streamflow peaks in response to both seasonal and event‐specific rain‐fraction using stream gage observations from watersheds across the western United States.

Machine learning and satellite data of crops shows that farms that till the soil less can increase yields of corn and soybeans and improve the health of the soil. Farmers have resisted a switch to reduced tilling because it was believed to reduce yields. Instead, it may increase yields while lowering production costs and reducing soil erosion.

The economic costs of Indonesia’s 2015 forest fires are estimated to exceed US $16 billion, with more than 100,000 premature deaths. On several days the fires emitted more carbon dioxide than the entire United States economy. Here, we combine detailed geospatial data on fire and local climatic conditions with rich administrative data to assess the underlying causes of Indonesia’s forest fires at district and village scales. We find that El Niño events explain most of the year-on-year variation in fire.

As the global population and people’s incomes rise, the demand for ocean-derived food will continue to grow. At the same time, hunger and malnutrition continues to be a challenge in many countries, particularly in rural or developing areas. Looking to the ocean as a source of protein produced using low-carbon methodologies will be critical for food security, nutrition and economic stability, especially in coastal countries where hunger and malnutrition are a challenge.

Understanding the determinants of agricultural productivity requires accurate measurement of crop output and yield. In smallholder production systems across low- and middle-income countries, crop yields have traditionally been assessed based on farmer-reported production and land areas in household/farm surveys, occasionally by objective crop cuts for a sub-section of a farmer’s plot, and rarely using full-plot harvests. In parallel, satellite data continue to improve in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots.

Feeding a growing population while reducing negative environmental impacts is one of the greatest challenges of the coming decades. We show that microsatellite data can be used to detect the impact of sustainable intensification interventions at large scales and to target the fields that would benefit the most, thereby doubling yield gains.

Irrigation has been pivotal in wheat’s rise as a major crop in India and is likely to be increasingly important as an adaptation response to climate change. Here we use historical data across 40 years to quantify the contribution of irrigation to wheat yield increases and the extent to which irrigation reduces sensitivity to heat.

Changes in global food and fuel demand, the effects of climate change, and regional depletion of groundwater resources for irrigation create uncertainty for U.S. farmers.

Efficient responses to climate change require accurate estimates of both aggregate damages and where and to whom they occur. While specific case studies and simulations have suggested that climate change disproportionately affects the poor, large-scale direct evidence of the magnitude and origins of this disparity is lacking. Similarly, evidence on aggregate damages, which is a central input into the evaluation of mitigation policy, often relies on country-level data whose accuracy has been questioned.

Wheat is the most important staple crop grown in Australia, and Australia is one of the top wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is important for regional and global food security. Prior studies use either climate data, or satellite data, or a combination of these two to build empirical models to predict crop yield. However, though the performance of yield prediction using empirical methods is improved by combining the use of climate and satellite data, the contributions from different data sources are still not clear.

Key messages:

Accurate measurements of maize yields at field or subfield scales are useful for guiding agronomic practices and investments and policies for improving food security. Data on smallholder maize systems are currently sparse, but satellite remote sensing offers promise for accelerating learning about these systems. Here we document the use of Google Earth Engine (GEE) to build “wall-to-wall” 10 m resolution maps of (i) cropland presence, (ii) maize presence, and (iii) maize yields for the main 2017 maize season in Kenya and Tanzania.

The ratio of plant carbon gain to water use, known as water use efficiency (WUE), has long been recognized as a key constraint on crop production and an important target for crop improvement. WUE is a physiologically and genetically complex trait that can be defined at a range of scales. Many component traits directly influence WUE, including photosynthesis, stomatal and mesophyll conductances, and canopy structure. Interactions of carbon and water relations with diverse aspects of the environment and crop development also modulate WUE.

Understanding the causes of economic inequality is critical for achieving equitable economic development. To investigate whether global warming has affected the recent evolution of inequality, we combine counterfactual historical temperature trajectories from a suite of global climate models with extensively replicated empirical evidence of the relationship between historical temperature fluctuations and economic growth. Together, these allow us to generate probabilistic country-level estimates of the influence of anthropogenic climate forcing on historical economic output.

We assess scientific evidence that has emerged since the U.S. Environmental Protection Agency’s 2009 Endangerment Finding for six well-mixed greenhouse gases and find that this new evidence lends increased support to the conclusion that these gases pose a danger to public health and welfare.

Millions of people worldwide are absent from their country’s census. Accurate, current, and granular population metrics are critical to improving government allocation of resources, to measuring disease control, to responding to natural disasters, and to studying any aspect of human life in these communities. Satellite imagery can provide sufficient information to build a population map without the cost and time of a government census.

Low-intensity tillage has become more popular among farmers in the United States and many other regions.

Crop type mapping at the field level is necessary for a variety of applications in agricultural monitoring and food security.

Oil palm expansion resulted in 2 million hectares (Mha) of forest loss globally in 2000–2010. Despite accounting for 24% (4.5 Mha) of the world’s total oil palm cultivated area, expansion dynamics in sub-Saharan Africa have been overlooked. We show that in Southwest Cameroon, a top producing region of Africa, 67% of oil palm expansion from 2000–2015 occurred at the expense of forest. Contrary to the publicized narrative of industrial-scale expansion, most oil palm expansion and associated deforestation is occurring outside large agro-industrial concessions.

Indonesia’s oil palm expansion during the last two decades has resulted in widespread environmental and health damages through land clearing by fire and peat conversion, but it has also contributed to rural poverty alleviation. In this paper, we examine the role that decentralization has played in the process of Indonesia’s oil palm development, particularly among independent smallholder producers.

Our Report draws attention to a complex but understudied issue: How will climate warming alter losses of major food crops to insect pests? Because empirical evidence on plant-insect-climate interactions is scarce and geographically localized, we developed a physiologically based model that incorporates strong and well-established effects of temperature on metabolic rates and on population growth rates.