A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments

Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R2 = 0.48; RMSE= 151.92 kg ha−1) compared with the unassimilated results (R2 = 0.23;RMSE= 373.6 kg ha−1) and the TM LAI results (R2 = 0.27; RMSE= 191.6 kg ha−1). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates

Contribution of Remote Sensing on Crop Models: A Review

Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products

RICE YIELD ESTIMATION USING REMOTE SENSING AND CROP SIMULATION MODEL IN NALGONDA DISTRICT, TELANGANA

A study on “Rice yield estimation using Remote Sensing and crop simulation model in Nalgonda district, Telangana” was carried out during kharif, 2021. Precise and real-time agricultural yield data at the national, international and regional levels is becoming increasingly crucial for global food security. Crop yield forecasting could be very useful in advanced crop planning, strategy creation, and management. Because of the importance of yield prediction in food security, the present study used the APSIM-ORYZA model and remote sensing to estimate rice yield. The core objective of this study was to develop a method to integrate remotely sensed data and APSIM model for rice yield estimation in Nalgonda district, Telangana. This study includes mapping of rice growing areas and execution of APSIM model, followed by integration of remote sensing and crop simulation model for rice yield prediction and verification using government statistics. Based on stratification, two villages, Telakantigudem from Kangal mandal and Mallaram village from Kattangoor mandal in Nalgonda district were selected and ten fields from each village were chosen for the study to collect the measured LAI values with the help of ceptometer in the fields and the crop management data from the respected farmers. Crop classification was performed on Sentinel-1 and Sentinel-2 time series data using a Random Forest (RF) classifier and ground reference points collected from field surveys in the Google Earth Engine platform. The results demonstrated an overall accuracy of 92% and a kappa coefficient of 0.85, and rice area was validated with the crop coverage report (kharif, 2021) provided by the Department of Agriculture (DOA), Telangana state showed a relative variation of -0.16%. Remote sensing products like VV, VH AND VH/VV from Sentinel-1 and NIR, Red and NDVI from Sentinel-2 were derived using GEE and were calibrated with the measured LAI data collected from farmers’ fields. The result showed that there was a significant relation (R2=0.78) between NDVI and field LAI and hence it was considered for integration with the crop model output. Maps were derived showing spatial variation in crop extent, and leaf area index (LAI), which are crucial in yield assessment. Execution of APSIM-ORYZA model was done using the weather parameters, soil parameters, genetic coefficients and crop management data. The evaluation of the model with simulated yield and observed yield in the farmers’ fields showed linear regression of R2 = 0.79, root mean square error (RMSE)=804 kg ha-1 and mean absolute error (MAE)=728 kg ha-1. The overall spatially averaged model yield for the district showed 4925 kg ha-1 which is deviated by 2% from the average yield in the government statistics with 5024 kg ha-1. The study showed that by assimilation of remotely sensed data with the crop models, crop yields before harvest could be successfully predicted

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

peer reviewedMathematical models have been widely employed for the simulation of growth dynamics of annual crops, thereby performing yield prediction, but not for fruit tree species such as jujube tree (Zizyphus jujuba). The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter. The model was established using data collected from dedicated field experiments performed in 2016–2018. Simulated growth dynamics of dry weights of leaves, stems, fruits, total biomass and leaf area index (LAI) agreed well with measured values, showing root mean square error (RMSE) values of 0.143, 0.333, 0.366, 0.624 t ha−1 and 0.19, and R2 values of 0.947, 0.976, 0.985, 0.986 and 0.95, respectively. Simulated phenological development stages for emergence, anthesis and maturity were 2, 3 and 3 days earlier than the observed values, respectively. In addition, in order to predict the yields of trees with different ages, the weight of new organs (initial buds and roots) in each growing season was introduced as the initial total dry weight (TDWI), which was calculated as averaged, fitted and optimized values of trees with the same age. The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI. The modelling performance was significantly improved when it considered TDWI integrated with tree age, showing good global (R2≥0.856, RMSE≤0.68 t ha−1) and local accuracies (mean R2≥0.43, RMSE≤0.70 t ha−1). Furthermore, the optimized TDWI exhibited the highest precision, with globally validated R2 of 0.891 and RMSE of 0.591 t ha−1, and local mean R2 of 0.57 and RMSE of 0.66 t ha−1, respectively. The proposed model was not only verified with the confidence to accurately predict yields of jujube, but it can also provide a fundamental strategy for simulating the growth of other fruit trees

Crop modeling for assessing and mitigating the impacts of extreme climatic events on the US agriculture system

The US agriculture system is the world’s largest producer of maize and soybean, and typically supplies more than one-third of their global trading. Nearly 90% of the US maize and soybean production is rainfed, thus is susceptible to climate change stressors such as heat waves and droughts. Process-based crop and cropping system models are important tools for climate change impact assessments and risk management. As data- science is becoming a new frontier for agriculture growth, the incoming decade calls for operational platforms that use hyper-local growth monitoring, high-resolution real-time weather and satellite data assimilation and cropping system modeling to help stakeholders predict crop yields and make decisions at various spatial scales. The fundamental question addressed by this dissertation is: How crop and cropping system models can be “useful” to the agriculture production, given the recent advent of cloud computing and earth observatory power? This dissertation consists of four main chapters. It starts with a study that reviews the algorithms of simulating heat and drought stress on maize in 16 major crop models, and evaluates algorithm performances by incorporating these algorithms into the Agricultural Production Systems sIMulator (APSIM) and running an ensemble of simulations at typical farms from the US Midwest. Results show that current parameterizations in most models favor the use of daylight temperature even though the algorithm was designed for using daily mean temperature. Different drought algorithms considerably differed in their patterns of water shortage over the growing season, but nonetheless predicted similar decreases in annual yield. In the next chapter of climate change assessment study, I quantify the current and future yield responses of US rainfed maize and soybean to climate extremes with and without considering the effect of elevated atmospheric CO2concentrations, and for the first time characterizes spatial shifts in the relative importance of temperature, heat and drought stresses. Model simulations demonstrate that drought will continue to be the largest threat to rainfed maize and soybean production, yet shifts in the spatial pattern of dominant stressors are characterized by increases in the concurrent stress, indicating future adaptation strategies will have trade-offs between multiple objectives. Following this chapter, I presented a chapter that uses billion-scale simulations to identify the optimal combination of Genotype × Environment × Management for the purpose of minimizing the negative impact of climate extremes on the rainfed maize yield. Finally, I present a prototype of crop model and satellite imagery based within-field scale N sidedress prescription tool for the US rainfed maize system. As an early attempt to integrate advances in multiple areas for precision agriculture, this tool successfully captures the subfield variability of N dynamics and gives reasonable spatially explicit sidedress N recommendations. The prescription enhances zones with high yield potentials, while prevents over-fertilization at zones with low yield potentials

Agro-hydrological modelling of regional irrigation water demand

The irrigation sector accounts for over 70% of the total freshwater consumption in the world. Therefore, e cient management of irrigation water is essential to ensure water, food, energy and environmental securities in a sustainable manner; these securities are grand challenges of the 21st century. The main objective of this research is to evaluate the simulation of irrigation water demand at the catchment scale in order to develop improved tools for conducting quantitative planning and climate change studies. Irrigation water demand is mostly driven by soil moisture. It is a state variable which is used to trigger the irrigation in hydrological models. In this study, a hydrolgical model (Soil and Water Assessment Tool, SWAT) is evaluated for reliably simulating the spatial and temporal patterns of soil moisture at a catchment scale. The SWAT simulated soil moisture was compared with the indirect estimates of soil moisture from Landsat and Time-domain re ectometry (TDR). The results showed that the SWAT simulated soil moisture was comparable with the soil moisture estimated from Landsat and TDR. Secondly, the applicability of the SWAT model was tested for simulating stream ow, evapotranspiration (ET) and irrigation water demand for four di erent agro-climatic zones (Mediterranean, Subtropical monsoon, Humid, and Tropical). Two di erent irrigation scheduling techniques were used to simulate irrigation namely, soil water de cit and plant water demand. It was seen from the results that the SWAT simulated irrigation amounts under soil moisture irrigation scheduling technique were close to the irrigation statistics provided by the state. However, the irrigation amounts simulated under the plant water demand irrigation scheduling technique were underestimated. Additionally, the two reanalysis data were also used to check the data uncertainty in simulating irrigation water demand. SWAT model code was modi ed by incorporating modi ed root density distribution function and dynamic stress factor. The modi ed model was used to simulate irrigation and crop yield. It was tested against the irrigation and crop yield simulated by Soil Water Atmosphere Plant (SWAP) model and eld data (Hamerstorf, Lower Saxony, Germany). It was then validated for di erent catchments (Germany, India and Vietnam). The results showed that the SWAT simulated irrigation water demand in case of plant water demand is comparable with the amount simulated by the model under soil water de cit irrigation scheduling technique. This dissertation not only bridges the gap between the scales of soil moisture determination but also establishes a close connection with the actual observations and modelled soil moisture and irrigation amounts at the eld, regional and global studies in agricultural water management. Additionally, the studies about simulating irrigation water requirement in data-scarce areas must address data uncertainty when using reanalysis data. It was found that rainfall is not always the dominant variable in irrigation simulation. Therefore, it is worth checking and bias correct the other climate variables

Simulation of Irrigation Demand and Control in Catchments – A Review of Methods and Case Studies

The world's water resources are continuously facing challenges in fulfilling the needs of increasing agricultural water demand with finite or diminishing resources. Therefore, it is important to quantify the amount of irrigation water required to attain sustainable yield at a local, regional, and global level, especially in arid and semi-arid regions. This is mostly quantified by using agro-hydrological or agricultural models. The advances in simulation models and several options incorporated in them allow catchment/site-specific application of irrigation water to depict the field management practices undertaken by farmers. The objective of the present study is to provide a review of the simulation of irrigation water demand at catchment scale by agro-hydrological and agricultural models. This study discusses the different types of models, their dimensions, and the hydrological and agricultural process models incorporated into them. Additionally, this review provides an overview of how irrigation can be scheduled, how water is applied, and from which sources irrigation water can be extracted by the considered models, taking horizontal hydrological connectivity into consideration. Adding to the model review, seven different fields of innovative case studies are covered. Many agricultural models have been applied in a regional context without simulating horizontal hydrological fluxes, but only a few hydrological catchment models provide full support of both irrigation and plant growth simulation, which are important for the simulation of future crop yield under different climatic and agricultural management scenarios

ESA - RESGROW: Epansion of the Market for EO Based Information Services in Renewable Energy - Biomass Energy sector

Biomass energy is of growing importance as it is widely recognised, both scientifically and politically, that the increase of atmospheric CO2 has led to an enhanced efficiency of the greenhouse effect and, as such, warrants concern for climate change. It is accepted (IPCC 2011 and just recently in the draft version of the IPCC 2013 report) that climate change is partly induced by humans notably by using fossil fuels. For reducing the use of oil or coal, biomass energy is receiving more and more attention as an additional energy source available regionally in large parts of the world. Effective management of renewable energy resources is critical for the European and the global energy supply system. The future contribution of bioenergy to the energy supply strongly depends on its availability, in other words the biomass potential. Biomass potentials are currently mainly assessed on a national to regional or on a global level, with the bulk biomass potential allocated to the whole country. With certain biomass fractions being of low energy density, transport distances and thus their spatial distribution are crucial economic and ecological factors. For other biomass fractions a super-regional or global market is envisaged. Thus spatial information on biomass potentials is vital for the further expansion of bioenergy use. This study, which is an updated version of a study carried out in 2007 in frame of the ENVISOLAR project, analyses the potential use of Earth Observation data as input for biomass models in order to assessment and manage of the biomass energy resources especially biomass potentials of agricultural and forest areas with high spatial resolution (typical 1km x 1km). In addition to a sorrow review of recent developments in data availability and approaches in comparison to its 2007’ version, this study also includes a review on approaches to directly correlate remote sensing data with biomass estimations. An overview of existing biomass models is given covering models using remote sensing data as input as well as models using only meteorological and/or management data as input. It covers the full life cycle from the planning stage to plant management and operations (Figure 1). Several groups of stakeholders were identified