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The scarcity of water resources is one of the biggest problems restricting food production. Crop water productivity (CWP) is a practical and quantifiable indicator for characterizing agricultural water use efficiency. Remote sensing technology provides an accurate, cost-effective, and regional method for estimating CWP. However, remote sensing methods for CWP estimation and their application scenarios need to be summarized. In this paper, the CWP and related parameters are clearly defined. Different types of CWP estimation methods and their application at different scales are reviewed. CWP, as the crop yield ratio to actual crop evapotranspiration (ETa), is typically not directly estimated but calculated by estimating crop yield and ETa. Therefore, crop yield and ETa estimation methods are summarized, respectively. ETa can be remotely sensed using surface energy balance residual methods, semi-empirical formula methods, statistical regression methods, and ground instruments. Crop yield can be remotely estimated using data assimilation, statistical regression, and ground instruments. Moreover, the application of these methods at the point, field, and regional scales is further reviewed from previous literature. Finally, the in-situ measurements of CWP are introduced. This review can provide a detailed reference for follow-up studies related to CWP. [ABSTRACT FROM AUTHOR]

The pairwise separability index (SI) has been demonstrated as an effective indicator for capturing crucial phenological differences between two plant species. However, its application to crop types, which have more obvious phenological characteristics than natural vegetation, has received less attention, and extending the pairwise SI to multiple crops for feature selection still remains a challenge. This paper presented two SI extension approaches (\textSI\mathrm{ave} and \textSI\min) to select the optimal spectro-temporal features for multiple crops, and investigated their classification performance using Heilongjiang Province, China, as a study area. Feature interpretability and classification accuracy of different crops were evaluated for the two approaches. The results showed that the \textSI\mathrm{ave} approach generally has relatively high feature interpretability due to its better description of crucial phenological characteristics of different crops. Those crops with high separability are insensitive to the extension approach and have similar classification accuracy for the two approaches, whereas those crops with poor separability show good performance with the \textSI\min method. Due to the higher temporal autocorrelation, the optimal features for crop classification that are selected by the \textSI\mathrm{ave} approach exhibit greater information redundancy across the time domain than those that are selected by the \textSI\min approach, which largely explains the relatively low classification accuracy achieved using the \textSI\mathrm{ave} approach. These comparison results between \textSI\min and \textSI\mathrm{ave} approaches also indicate that time-series images with high temporal resolution do not necessarily produce high classification accuracy, regardless of their ability to describe the seasonal characteristics of crops. [ABSTRACT FROM PUBLISHER]

Highlights: [•] We use survey data to analyze agricultural land system change in Northeast China. [•] Land system changes analyzed include land tenure, crop choice and intensification. [•] Spatial patterns of these aspects are related to road accessibility. [•] Crops for domestic use are increasingly replaced by cash crops in the study region. [•] Family characteristics affect land transfer while external drivers affect crop choice. [ABSTRACT FROM AUTHOR]

Abstract: Food security is greatly affected by the consequences of global change, especially its impact on agriculture. Currently, global change and food system interaction is a hot issue across the scientific community. Scientists have tried to explain this interaction from different perspectives, and the issues related to this interaction can be classified as (1) crop yield and productivity in response to global change; (2) crop distribution and allocation in relation with global change; (3) general impacts on food security. However, most of the existing studies lack consistency and continuity. As food systems exist at the intersection of the coupled human and natural system, the interdisciplinary context of global change and food security requires an integrated and collaborative framework for better describing their importance and complexity. To do so, we decompose global change/food security studies into different levels in accordance with the previous mentioned issues, field, regional, and global, and categorize them into the life sciences, earth and environmental sciences, and social and sustainability sciences, respectively (yet not necessarily one to one correspondence). At the field level, long-term observations and controlled experiments in situ are important for exploring the mechanism of how global change will affect crop growth, and for considering possible adaptation methods that may maximize crop productivity. At the regional level, priority should be given to monitoring and simulating crop production (animal production and fishery are not included here) within large areas (a region or a continent). At the global level, food security studies should be based on scenario assessments to prioritize human adaptations under the changed environment, using integrated socioeconomic–biogeophysical measures. [Copyright &y& Elsevier]

[Display omitted] • Cropping cycles were depicted based on dynamic patterns of vegetation and brownness indices. • The CI mapping algorithm was efficient in reducing the omission errors of double rice. • The first 10-m CI map over conterminous China was generated using Sentinel-1/2 data. • Around 28% of cropped fields are cultivated by multiple cropping in China in 2020. Multiple cropping is a widespread agricultural intensification for increasing food production. National-scale Cropping Intensity (CI) mapping is important for achieving sustainable development goals. However, previous studies have largely relied on vegetation indices (VI) waves for detecting valid cropping cycles, which is challenged by the complexity of agricultural systems. i.e. winter crops with multiple VI waves and double rice with less distinctive waves. This study proposed a robust framework for Mapping cropping Intensity through better-characterizing crop Life cycles based on combined considerations of vegetative and productive Stages (MILS). The number of cropping cycles was estimated by the frequency of valid coupled patterns of vegetation and brownness indices from Sentinel-2 (S2) MultiSpectral Instrument (MSI) time series and further improved through fusing Sentinel-1 (S1) SAR and S2 data to reduce the omission errors of double rice. The MILS algorithm was implemented using the Google Earth Engine platform and applied to China, which is dominated by smallholder farms with diverse cropping patterns. This study produced the first 10 m updated CI map over conterminous China (CIChina10m) in 2020 by fusing S1 and S2 time series. The CIChina10m had an overall accuracy of 93.93% when validated with 14,468 widely spread reference sites. The cropping intensity was 1.2769 on the national scale, which illustrated higher values in the Middle and lower reaches of the Yangtze River plain (CI = 1.5879) and South China (CI = 1.5503). There were 1086,620, 412,620 and 1,441 km2 areas cultivated by single, double and triple cropping in China, which accounted for 72.4%, 27.5% and 0.1% of cropland, respectively. The proposed MILS algorithm showed good performances in detecting complex agricultural systems, which can be further applied to generate continental or global 10-m CI products with good quality. Codes of the MILS algorithm are publicly available at https://code.earthengine.google.com/a7f24f76291bf901ee25a130025a7ce6 , and the first 10m national CI data products in China with good accuracy (ChinaCI10m) are also publicly accessed at https://doi.org/10.6084/m9.figshare.23939505. [ABSTRACT FROM AUTHOR]

The impact of climate change on the crop water footprint (WF) is highly uncertain, hindering effective agricultural water use in response to climate change. This study proposes a factor sensitivity analysis method, which can screen out statistically significant climatic factors and interactions on crop WF under various uncertainties and determine water resource management measures to mitigate drought for different crops. A special case study was conducted in Heilongjiang Province, China. The results showed that (1) the annual change in crop WF showed a downward trend from 1988 to 2018. The crop WF was dominated by green water footprint (WF green), and the average occupancy rate of WF green in crops was 58.7%–74.1 %; the spatial distribution of WF has latitude zonality. (2) Drought has different effects on WF of different crops, and WF of soybean is susceptible to drought. Wind speed, sunshine hours, and humidity have a greater impact on crop WF in most growth stages. (3) The effect of climatic factors on crop WF varies in different months. The rice WF is mainly affected by the climate in May, and there is an interaction between May humidity and May rain. The WF of maize and soybeans are affected primarily by the climate in July, especially sunshine hours. The proposed approach attempts to analyze that crop WF is affected by not only an individual climatic factor but also their interactions. Crop water management practices should be adjusted based on the results to mitigate the adverse impact of climatic conditions on crop WF during different growing months. [ABSTRACT FROM AUTHOR]

Understanding of cropland dynamics in a large geographical extent is mostly based on observations of area change, while the changes in landscape pattern are hardly assessed. The total amount of cropland in China has remained relatively stable in recent years, which might suggest there was little change. In this analysis, we combine the number of cropland patches (NP) with the total cropland area (TA) for a more comprehensive characterization of cropland change in China. We use GlobeLand30–a global land cover dataset with a 30 m resolution for the years 2000 and 2010–and characterize changes in TA and NP for each county as increase, stable, or decrease. This characterization shows that 703 out of 2420 counties experienced both cropland loss and increased fragmentation. The predominant cropland loss in these areas, especially in the North China Plain, is converted to artificial land. Another 212 are characterized by the opposite developments: an increase in cropland and decreased fragmentation. These counties, are mainly characterized by a conversion of forest areas and grassland areas. It suggests that the cropland conservation policy in China effectively protected the total cropland area in overall, but the consequences in terms of fragmentation might be underestimated. Counties with no obvious change in both indicators, measuring 279 counties, are mainly located in the Southeast. Our results are further compared with local level case studies: the fair consistency indicates alternatives of applying GlobeLand30 for analyzing landscape changes across scales and for cross-site comparisons. [ABSTRACT FROM AUTHOR]

Aim Agricultural practices have dramatically altered the land cover of the earth, but the spatial extent and intensity of these practices is often difficult to catalogue. Information on the distribution and performance of specific crops is often only available through national or subnational statistics. Recently, however, there have been multiple independent efforts to incorporate the detailed information available from statistical surveys with supplemental spatial information to produce a spatially explicit global dataset specific to individual crops. While these datasets provide decision makers with improved information on global cropping systems, the final global cropping maps differ substantially from one another. This study aims to explore and quantify systematic similarities and differences between four major global cropping systems products and the subsequent implications for analyses dependent on those models. Location This study was conducted at a global scale. Methods Each global cropping systems model was assessed by latitude as a measure of biophysical plausibility and each pair of models was compared using a Gaussian filter to remove trivial spatial discrepancies. Model disagreement was explored in relation to the interdependent input data of each model pair with a particular focus on cropland extent. The influence of the observed model differences on subsequent analyses was demonstrated using model-dependent estimates of the yield gap as an example. Results The results of our analysis indicate that the choice of cropping systems model is non-trivial: considerable differences exist between model-specific estimates of the yield gap across nearly all climate zones and the average model difference exceeds the average estimated yield gap in certain regions. The differences in crop-specific harvested area and yield products of each model are significant, and mostly result from differences in the input datasets and downscaling methodologies. In particular, the choice of dataset on cropland extent proved to be influential regardless of the downscaling process employed. Main conclusions Discrepancies in the final products of cropping systems models are currently poorly understood, but have implications for basic policy decisions relating to agricultural production and food security. The considerable disagreement between models serves as a reminder of the ongoing challenges to the creation of spatially explicit estimates of harvested area and yield based on crop statistics. Our analysis helps shed light on the importance of model choice by demonstrating the implications for further analyses that depend on cropping systems models, and works to overcome these challenges by characterizing model-dependent differences in harvested area and yield. [ABSTRACT FROM AUTHOR]

Highlights: [•] Describe an improved spatial crop allocation model. [•] Estimate crop production for 20 major crops across a global 5arc minute grid. [•] Summarize factors that give rise to systematic differences of the global datasets. [Copyright &y& Elsevier]